ORIGINAL_ARTICLE
Evaluate the Stability of Surface Water Quality Variation in Gorganrood River Basin
Information on water quality and pollution sources is important for the implementation of sustainable water resource management strategies. In this research, to evaluate spatial variation and the interpretation of large complex water quality data taken at seven different sites along the Gorganrood River were subjected to multivariate statistical analysis. The factor analysis generated two significant factors, which explained 97.80% of the variance in data sets. Factor 1 and factor 2 explained 86.27 and 15.76% of the total variance in water quality data sets. Principle components analysis results revealed that surface water quality was mainly controlled by Ca and pH parameters. In next step, information entropy theory applied to interpret the stability of surface water quality variation in each factors and each parameters. Overall results revealed instability in data recorded in Tamar and Galikesh sites and in factor 2, EC and TDS, Lazoreh has highest instability. With a well knowing the factor score and overlaying entropy, infactor1, Mg and Cl had highest stability. The ranks of information entropy values for Mg parameter at Galikesh, Tagi Abad, Basir Abad, Ramian and Tangrah sites vary noticeably due to geological formation. In general, sampling uncertainties are highly site specific.
https://ije.ut.ac.ir/article_56143_7730e480cc896e4be704113afd547ace.pdf
2015-06-22
129
140
10.22059/ije.2015.56143
Hamed
Rouhani
rouhani.hamed@gonbad.ac.ir
1
Range and Management department, Gonbad-e-Kavouse University, Gonbad, Iran
LEAD_AUTHOR
Elnaz
Zaki
ipak_777@yahoo.com
2
Rangeland Management Department, Tarbiat Modares University, Noor, Iran
AUTHOR
Mojtaba
Kashani
kashani.mojtaba@yahoo.com
3
Statistical Department, Gonbad-e-Kavouse University, Gonbad, Iran
AUTHOR
Abolhasan
Fathabadi
fathbabadi@ut.ac.ir
4
Range and Management department, Gonbad-e-Kavouse University, Gonbad, Iran
AUTHOR
خزایی موغانی، سولماز؛ نجفینژاد، علی؛ محسنی، عظیم؛ شیخ، واحدبردی،1392، ﺗﻐﯿﯿﺮات ﻣﮑﺎﻧﯽ و ﻓﺼﻠﯽ رﺳﻮب ﻣﻌﻠﻖ دراﯾﺴﺘﮕﺎهﻫﺎی واﻗﻊدرﻃﻮل رودﺧﺎﻧﻪ ﮔﺮﮔﺎﻧﺮود، اﺳﺘﺎنﮔﻠﺴﺘﺎن، پژوهشنامۀ مدیریت حوضۀ آبخیز، سال چهارم، شمارۀ 7: 15-1.
1
زارع گاریزی، آرش؛ سعدالدین، امیر، شیخ، واحد بردی؛ سلمان ماهینی، عبدلرسول،1391، بررسی روند تغییرات بلندمدت متغیرهای کیفیت آب رودخانۀ چهلچای (استان گلستان)، مجلۀ پژوهش آب ایران، سال ششم، شمارۀ 10: 165-155.
2
سید خادمی، مرتضی، 1379، پایش نیترات و بررسی نسبت کلرید به نیترات در آبهای زیرزمینی شهر گرگان، مجموعه مقالات چهارمین کارگاه آموزشی، تخصصی پایش کیفی منابع آب، شیراز.
3
شیرازی، محمد رضا، 1379. پایش کیفی منابع آب تهران، مجموعه مقالات چهارمین کارگاه آموزشی، تخصصی پایش کیفی منابع آب، شیراز ، ایران.
4
کبودوندپور، شهرام، 1373، اثرات فاضلابهای شهری و صنعتی بر کیفیت رودخانۀ قشلاق سنندج، کارشناسی ارشد، دانشگاه تربیت مدرس نور.
5
کلانتری، نصرا..؛ رحیمی، محمد حسین؛ اکبری، اکبر، 1388، بررسی هیدروشیمیایی دشت میان آب با استفاده از روشهای آماری، نمودارهای هیدروشیمیایی و منطق فازی. فصلنامۀ زمینشناسی ایران، سال سوم، شمارۀ نهم: 15-25.
6
معاونت برنامهریزی و نظارت راهبردی رییس جمهور ،1390، گزارش اقتصادی اجتماعی استان گلستان: 396.
7
معاونت برنامهریزی و نظارت راهبردی رییس جمهور، 1388، راهنمای پایش کیفیت آب سطحی، نشریۀ شمارۀ 522: 203.
8
Chatfield, C. and A. J. Collin, 1980, Introduction to Multivariate Analysis. Published in the USA by Chapman and Hall, New York NY.
9
Fetter, C. W., 1990, Applied hydrogeology, 3rd Ed., MacMillan Pub, 592 p.
10
GÜler, C., G.D. Thyne, J. E. Mc Cary, and K. A. Turner, 2002, Evaluation of graphical and multivariate statistical methods for classification of water chemistry data, Hydrogeology Journal, vol. 10, pp.455-474.
11
Helena, B., R. Pardo, M. Vega, E. Barrado, J. M. Fernandez, and L. Fernandez, 2000, Temporal evolution of groundwater composition in an alluvial aquifer (Pisuerga River, Spain) byprincipal component analysis, Water Research, vol 34 (3), pp. 807–816.
12
Jollife, I. T., 1986, Principal component analysis, Second Ed., Springer, 271. Pages.
13
Karpuzcu, M., S. Senes, and A. Akkoyunlu, 1987, Design of monitoring systems for water quality by principal component analysis and a case study. Proceedings of the International Symposium on Environmental Management (Environment 87), vol1, 673-690, Istanbul.
14
Liu, C. W., C. S. Jang, C. P. Chen, C. N. Lin, and K. L. Lou, 2008, Characterization of groundwater quality in Kinmen Island using multivariate analysis and geochemical modeling, Hydrological Processes, vol 22 (3), pp. 376-383.
15
Love, D., D. Hallbauer, A. Amos, and R. Hranova, 2004, Factor analysis as a tool in groundwater quality management: Two southern African case studies, Physics and Chemistry of the Earth, vol 29(15-18), pp. 1135-1143.
16
Mogheir, Y., V. P. Singh, and J. L. M. P. Lim, 2006, Spatial assessment and redesign of groundwater quality monitoring network entropy theory, Gaza Strip, Palestine, Hydrogeology Journal, vol 14, pp. 700-712.
17
Paliwal, R., P. Sharma, and A. Kansal, 2007, Water quality modeling of the river Yamuna (India) using QUAL2E-UNCAS, Journal of Environmental Management, vol 83, pp.131–144
18
Rango, G., M. De Luca, and G. Loele, 2007, An Application of Cluster Analysis and Multivariate Classification Methods to Spring Water Monitoring Data, Micro Chemical Journal, vol 87, pp. 19-127.
19
Ryberg, K. R., 2006, Cluster analysis of water-quality data for Lake Sakakawea, Audubon Lake, and Mc Clusky Canal, Central North Dakota, 1990-2003: U.S. Geological Survey Scientific Investigations Report 2006-5202, 38 P.
20
Satyanarayana M. and P. Periakali, 2003, Geochemistry of ground water in ultra basic peninsular gneissic rocks, Salem district, Tamil Nadu, Journal of the Geological Society of India, vol 62, pp. 63–73.
21
Shannon, C. E., 1948, A mathematical theory of communication, Bell Syst. Tech. J., vol 27, pp. 379-423.
22
Singh, K. P., A. Malik, D. Mohan, and S. Sinha, 2004, Multivariate statistical techniques for the evaluation of spatial and temporal variations in water quality of Gomti River, India, Water Research, vol 38, pp. 3980–3992.
23
Singh, V. P., 1997, The use of entropy in hydrology and water resources, Hydrological Processes, vol 11, pp. 587-626
24
Wang, Y. and Z. Luo Tma, 2001, Geostatistical and geochemical analysis of surface water leakage into ground water on a regional scale: A case study in the Liulin karst system, northwestern China, Journal of Hydrology, vol 246, pp. 223–234.
25
Wunderlin, D.A., M. Diaz, M. M. V., Ame, S. F. Pesce, A. C. Hued, and M. Bistoni, 2001, Pattern recognition techniques for the evaluation of spatial and temporal variations in water quality. A case study: Suquia river basin (Cordoba-Artgentina), Water Research, vol 35 (12), pp. 2881–2894.
26
Yidand, S., M. D. Ophori, and B. Banoeng-Yakubo, 2008, A Multivariate Statistical Analysis of Surface Water Chemistry Data- the Ankobra Basin, Ghana, Journal of Environmental Management, vol. 86, pp.80-87.
27
Yuan, Y., and J. K. Mitchell, 1999, A Method to Evaluate Pollutant Loads from Tile Drains. Transactions of the American Society of Agricultural Engineers, vol 42(5), pp. 1313-1319.
28
Zhang J, W. W. Huang, R. Letolle, and C. Jusserand, 1995, Major element chemistry of the Huanghe (Yellow River), China – Weathering processes and chemical fluxes, Journal of hydrology, vol 168, pp. 173–203.
29
ORIGINAL_ARTICLE
Efficiency Evaluation of Diskin Method in Derivation of Instantaneous Unit Hydrograph in Jafar-Abad watershed, Golestan Province
Instantaneous Unit Hydrograph (IUH) is the response of a watershed subjected to a storm with an excess rainfall of 1 unit depth occurring on the watershed instantaneously, used as the response function in rainfall- runoff modeling. Omission of excess rainfall duration from unit hydrograph theory has made it better and easier to investigate on rainfall- runoff relationship. This study focuses on derivation of IUH from direct runoff hydrograph using Diskin method in Jafar Abad watershed (109 km2) located in Golestan province. Using hourly hydro-meteoerological data the index unit hydrograph was derived from 27 storms by S curve method and then index IUH was calculated from S curve. Then the Diskin method was used for derivation of IUH from 4 different direct runoff hydrographs. The efficiency of Diskin method was evaluated by statistical comparison with the observed IUH. The results indicated that the method derived IUHs with good accuracy, 85% according to the Nash-Sutcliffe criterion. Also mean relative error of instantaneous peak discharge and relative error of instantaneous time to peak, are 2.24% and 6.25% respectively while the model bias for water balance is -0.92%. Nonetheless more researches in other watersheds are suggested for derivation of instantaneous and flashy floods for planning into reduction of flood damages.
https://ije.ut.ac.ir/article_56144_0bad9c6010add0b043f35682156d6851.pdf
2015-06-22
141
150
10.22059/ije.2015.56144
Direct runoff Hydrograph
Dsikin method
Instantaneous Unit Hydrograph
S curve
Jafar-Abad watershed
Raoof
Mostafazadeh
raoofmostafazadeh@uma.ac.ir
1
Assistant Professor, Dept. of Rangeland and Watershed Managemnet
LEAD_AUTHOR
Abdolreza
Bahremand
abdolreza.bahremand@yahoo.com
2
Associate Professor, Dept. of Watershed Management, Gorgan University of Agriculture Sciences and Natural Resources, Gorgan, Iran
AUTHOR
Mohsen
Zabihi
mohsen.zabihi@modares.ac.ir
3
PhD Student in Watershed Management Sciences and Engineering, of Science Graduated, Dept. of Watershed ‎Management Engineering, Faculty of Natural Resources, Tarbiat Modares University
AUTHOR
بهرهمند، عبدالرضا؛ مصطفیزاده، رئوف، 1389، مقایسۀ کارایی روشهای تخمین پارامترهای مدل هیدروگراف واحد لحظهای نش در شبیهسازی هیدروگراف جریان، پژوهشهای آبخیزداری، شمارۀ 86: 51 – 42.
1
صادقی، سیدحمیدرضا؛ افضلی، علی؛ وفاخواه، مهدی؛ تلوری، عبدالرسول، 1391، کارایی روشهای مختلف آنالیز آماری در تخمین مؤلفههای آبنمود واحد مصنوعی آبخیزهای شمال کشور، پژوهشنامة مدیریت حوزة آبخیز، شمارۀ 3: 15- 1.
2
صفوی، حمیدرضا، هیدرولوژی مهندسی، انتشارات اردکان، 1385: 620.
3
علیزاده، امین، اصول هیدرولوژی کاربردی، انتشارات دانشگاه امام رضا مشهد، 1377: 634.
4
قدسیان، مسعود، مهار سیلاب و مهندسی زهکشی، مرکز نشر آثار علمی دانشگاه تربیت مدرس، 1377: 391.
5
کارآموز، محمد؛ عراقینژاد، شهاب، هیدرولوژی پیشرفته، انتشارت دانشگاه صنعتی امیرکبیر، 1384: 465.
6
مصطفیزاده، رئوف؛ بهرهمند، عبدالرضا؛ سعدالدین، امیر، 1388، شبیهسازی هیدروگراف رواناب سطحی با مدل هیدروگراف لحظهای کلارک (مطالعۀ موردی: آبخیز جعفرآباد استان گلستان)، پژوهشهای حفاظت آب و خاک، شمارۀ 3: 105-122.
7
مصطفیزاده، رئوف؛ سعدالدین، امیر؛ بهرهمند، عبدالرضا؛ شیخ، واحدبردی؛ نظرنژاد، حبیب. 1389، ارزیابی اثرات هیدرولوژیک طرح آبخیزداری جعفرآباد استان گلستان با استفاده از مدل HEC-HMS، مهندسی و مدیریت آبخیز، شمارۀ 2: 93 - 83.
8
مهدوی، محمد، هیدرولوژی کاربردی، انتشارات دانشگاه تهران، 1378: 401.
9
مهندسین مشاور نهرسازان رستاق، 1380، گزارش مطالعات پایه و تلفیق آبخیز جعفرآباد استان گلستان: 215.
10
نجمایی، محمد، هیدرولوژی مهندسی، انتشارات سارا، 1368: 608.
11
12. Agirre Unai, Goni Mikel, Lopez, Jose Javier, Gimena, Faustino, 2005, Application of a unit hydrograph based on sub-watershed division and comparison with Nash’s instantaneous unit hydrograph, Catena, vol. 64, pp 321–332.
12
13. ASCE, 1993, Criteria for evaluation of watershed models, Journal of Irrigation and Drainage, vol 119(3), pp 429–442.
13
14. Assouline, Shmuel, and Mualem, Yechezkel, 2006, Runoff from heterogeneous small bare catchments during soil surface sealing, Water Resources Research, vol 42, W12405, DOI: 10.1029/WR004592.
14
15. Bahremand, Abdolreza, Mostafazadeh, Raoof, 2009. Mathematical computation of Nash model parameters for hydrograph prediction. International Conference on Approximation Methods and Numerical Modelling in Environment and Natural Resources, 10 June, Pau, France.
15
16. Ghosh, SN. Flood Control and Drainage Engineering, CRC Press, 1997, pp 314.
16
17. Hunt, B. 1985, The meaning of oscillations in unit hydrograph S-curves. Hydrological Sciences, vol 30, pp 331-342.
17
18. Knight, Donald, Shamseldin, Asaad, 2005, River basin modelling for flood risk mitigation. CRC Press, pp 670.
18
19. Kokkonen, Teemu, 2003, Rainfall-Runoff modeling-comparison of modeling strategies with a focus on ungauged predictions and model integration, PhD thesis, Helsinki University of Technology.
19
20. Kumar , Anil, 2015, Geomorphologic Instantaneous Unit Hydrograph Based Hydrologic Response Models for Ungauged Hilly Watersheds in India, Water Resources Management, vol 29, pp 863-883.
20
21. Kumar, Rakesh, Chatterjee, Chandranath, Lohani, Anil Kumar, Sanjay, Sing, Raj Deva, 2002, Sensitivity Analysis of the GIUH based Clark Model for a Catchment, Water Resources Management, vol 16, pp 263–278.
21
22. Moriasi, Daniel N, Arnold, Jeffrey G, Van Liew, Michael W, Bingner, Ronald L, Harmel, R. Daren, Veith, Tamie L, 2007, Model evaluation guidelines for systematic quantification of accuracy in watershed simulations, American Society of Agricultural and Biological Engineers, Vol 50 (3): pp 885−900.
22
23. Nash, JEa, Sutcliffe, Jonh V, 1970, River flow forecasting through conceptual models, Part 1, A discussion of principles, Journal of Hydrology, vol 10, pp 282–290.
23
24. Oguz, Beyhan, 2001, Mean Instantaneous Unit Hydrographs of Random channel Networks. Turkish Journal of Engineering and Environmental Sciences, vol 25, pp 117-126.
24
25. Renard, Kenneth, 1977, Past, Present and Future Water Resources Research in Arid and Semiarid Areas of the Southwestern United States, Hydrology Symposium, 28-30. Jun, Brisbane, Australia.
25
26. Sadeghi, Seyed Hamidreza, Mostafazadeh, Raoof, Sadoddin, Amir, 2015, Changeability of simulated hydrograph from a steep watershed resulted from applying Clark’s IUH and different Time Area Hystograms. Journal of Environmental Earth Sciences, DOI: 10.1007/s12665-015-4426-3.
26
27. Sadeghi, Seyed Hamidreza, Singh, Jai Karan, 2005, Development of Synthetic Sediment Graph using Hydrological Data, Journal of Agricultural Sciences and Technology (JAST), vol 7, pp 69-77.
27
28. Salas, Jose D, Notes on Unit Hydrographs, Colorado State University, 2006, pp 25.
28
29. Sarangi, Arjamadutta, Madramootoo, Chandra, Enright, Peter, Prasher, Shiv O, 2007, Evaluation of three unit hydrograph models to predict the surface runoff from a Canadian watershed, Water Resources Management, vol 21, pp 1127–1143.
29
30. Singh, Sushil K, 2015, Simple Parametric Instantaneous Unit Hydrograph, Journal of Irrigation and Drainage Engineering, 141(5), 04014066.
30
31. Singh, Vijay P, Hydrologic Systems, Rainfall-runoff modeling, Prentice Hall, 1989, pp 480.
31
ORIGINAL_ARTICLE
Sub-watershed flooding prioritization using morphometric and correlation analysis (Case study: Golestan Watershed)
Planning of watersheds is indispensable in terms of sustainable development and landscape management. Therefore, watershed prioritization and morphometric characterization are important to identify hydrological behavior of the basin for conducting management strategies. In this study, geospatial-statistical approach was used for identifying critical and priority sub-watersheds in the Golestan watershed. In first step, eight morphometric parameters (bifurcation ratio, drainage density, constant of channel maintenance, stream frequency, form factor, drainage texture rate, relief ratio, ruggedness number) which effect on hydrological, soil erosion and sediment transport were selected. The map of morphometric parameters using digital elevation model (DEM) were produced in ArcGIS10.2 software. In order to determine prioritization of sub-atersheds a new method based on morphometric and statistical analysis was applied. The Kendall’s tau and weighted sum analysis (WSA) methods were used for analyzing the relationship between morphometric parameters and determining their effect weights. Finally, sub-watershed prioritization index (SWPI) based on weighted linear composite (WLC) method was calculated for each sub-watersheds. For validation of the mentioned results, used of previous destructive floods location in the Golestan Watershed. The results showed that, meanwhile innovation method of prioritization, isn’t proper for all of the sub-watersheds in the study area, but the mentioned method was identified the sub-watersheds 3, 16, and 9 as the best regions for watershed management plans.
https://ije.ut.ac.ir/article_56241_47c9ad6b15e9915fc6d2e04cf823a88c.pdf
2015-06-22
151
161
10.22059/ije.2015.56241
Prioritization of sub-watershed
morphometric analysis
Correlation analysis
Golestan watershed
Omid
Rahmati
omid_rahmati@yahoo.com
1
PhD student in watershed management engineering and science, Lorestan University, Khoram-Abad
AUTHOR
Naser
Tahmasebipour
ntahmasebipour@yahoo.com; tahmasebi_n@ul.ac.ir
2
Department of watershed management engineering, Lorestan University, Khoram-abad
AUTHOR
Hamid Reza
Pourghasemi
hamidreza.pourghasemi@yahoo.com
3
Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz
LEAD_AUTHOR
[1]. ایزانلو، حسن؛ مرادی، حمیدرضا؛ صادقی، سید حمیدرضا، 1388، مقایسۀ اولویتبندی زمانی سیلخیزی در دورههای هیدرولوژیکی مختلف (مطالعۀ موردی: زیرحوضههای آبخیز کوشکآباد خراسان رضوی)، فصلنامۀ پژوهشهای آبخیزداری، شمارۀ 82: 30-21.
1
[2]. بهرامی، سید علیرضا؛ اونق، مجید؛ فرازجو، حسن، 1390، نقش روندیابی رودخانه در شناسایی و اولویتبندی واحدهای هیدرولوژیک حوضۀ سد بوستان از نظر سیلخیزی و ارائۀ راهکارهای مدیریتی، مجلۀ حفاظت منابع آب و خاک، شمارۀ 1: 27-11.
2
[3]. ثقفیان، بهرام؛ فرازجو، حسن، 1386، تعیین مناطق مولد سیل و اولویتبندی سیلخیزی واحدهای هیدرولوژیک حوضۀ سد گلستان، علوم و مهندسی آبخیزداری، شمارۀ 1: 11-1.
3
[4]. زهتابیان، غلامرضا؛ قدوسی، جمال؛ احمدی، حسن؛ خلیلیزاده، مجتبی، 1388، بررسی اولویت پتانسیل سیلخیزی زیرحوضههای آبخیز و تعیین مناطق مولد سیل در آن (مطالعۀ موردی: حوضۀ آبخیز مارمه-استان فارس)، فصلنامۀ جغرافیای طبیعی، شمارۀ 6: 38 – 27.
4
[5]. محمدی، علیاصغر؛ احمدی، حسن، 1390، اولویتبندی زیرحوضهها جهت ارائۀ برنامههای احیایی آبخیزداری (مطالعۀ موردی: حوضۀ آبخیز معروف)، فصلنامۀ جغرافیایی سرزمین، شمارۀ 29: 77-69.
5
[6]. Adinarayana, J., Krishna, R.N., and Rao, K., 1995, An integrated approach for prioritization of watersheds. Journal of Environmental Management, vol 44, p. 375-384.
6
[7]. Aher, P., Adinarayana, J., and Gorantiwar, S.D., 2014, Quantification of morphometric characterization and prioritization for management planning in semi-arid tropics of India: A remote sensing and GIS approach. Journal of Hydrology, vol 511, pp. 850-860.
7
[8]. Badar, B., Romshoo, S.A., and Khan, M.A., 2013, Integrating biophysical and socioeconomic information for prioritizing watersheds in a Kashmir Himalayan lake: a remote sensing and GIS approach. Environmental Monitoring and Assessment, vol 185, pp. 6419-6445.
8
[9]. Chowdary, V.M., Chakraborthy, D., Jeyaram, A., Krishna Murthy, Y.V.N., Sharma, J.R., Dadhwal, V.K., 2013, Multi-Criteria Decision Making Approach for Watershed Prioritization Using Analytic Hierarchy Process Technique and GIS. Water Resource Management, vol 27, pp. 3555-3571.
9
[10]. Grohmann, C.H., 2004, Morphometric analysis in geographic information systems: applications of free software GRASS and R star. Computer and Geoscience, vol 30 (10), pp. 1055-1067.
10
[11]. Horton, R.E., 1932, Drainage basin characteristics. Trans. Am. Geophys. Union vol 13, pp. 350–361.
11
[12]. Horton, R.E., 1945, Erosional development of streams and their drainage basins; hydrological approach to quantitative morphology. Geol. Soc. Am. Bull. vol 56, pp. 275–370.
12
[13]. Jang, T., Vellidis, G., Hyman, J.B., Brook, E., and Kurkalova, L.A., 2011, Impact of socioeconomic factors on synoptic assessment for prioritizing BMP implementation to reduce sediment load. In: ASABE Annual International Meeting Louisville, Kentucky, August, pp. 7-10.
13
[14]. Javed, A., Khanday, M.Y., and Ahmed, R., 2009, Prioritization of sub-watersheds based on morphometric and land use analysis using remote sensing and GIS techniques. Journal of the Indian Society of Remote Sensing, vol 37, pp. 261-274.
14
[15]. Melton, M.A., 1958, Correlations structure of morphometric properties of drainage systems and their controlling agents. Journal of Geology, vol 66, pp. 442-460.
15
[16]. Pandey, A., Chowdary, V.M., Mal, B.C., and Billib, M., 2009. Application of the WEPP model for prioritization and evaluation of best management practices in an Indian watershed. Hydrologic processes, vol 23, pp. 2997-3005.
16
[17]. Rahmati, O., Pourghasem, H.R., and Zeinivand, H., 2015, Flood susceptibility mapping using frequency ratio and weights-ofevidence models in the Golastan Province, Iran. Geocarto International. doi: 10.1080/10106049.2015.1041559.
17
[18]. Ratnam, N.K., Srivastava, Y.K., Rao, V.V., Amminedu, E., and Murthy, K.S.R., 2005, Check dam positioning by prioritization micro-watersheds using SYI model and morphometric analysis – remote sensing and GIS perspective. Journal of the Indian Society of Remote Sensing, vol 33 (1), pp. 25-38.
18
[19]. Saghafian, B., Farazjoo, H., Bozorgy, B., Yazdandoost, F., 2008, Flood intensification due to changes in land use. Water Resources Management, 22, pp. 1051-1067.
19
[20]. Sharifi F., Samadi S.Z., and Wilson C., 2012, Causes and consequences of recent floods in the Golestan catchments and Caspian Sea regions of Iran. Natural Hazards, vol 61, pp. 533-550.
20
[21]. Shieh, G.S., 1998, A weighted Kendall's tau statistic. Statistics & Probability Letters, vol 39(1), pp. 17-24.
21
[22]. Vittala, S.S., Govindaiah, S., and Gowda, H.H., 2008, Prioritization of sub-watersheds for sustainable development and management of natural resources: an integrated approach using remote sensing, GIS and socio-economic data. Current Science, vol 95(3), pp. 345-354.
22
ORIGINAL_ARTICLE
Investigate the effect of Karst Development on Karstic Springs Hydrogeology Treatment of Kermanshah Province
Abstract One of the importance groundwater resources in Kermanshah province is karstic aquifer. This study aimed to investigate the role of development of karstic aquifer in karst springs hydrological properties have been conducted. The studies areas are two karst aquifers include the Bistoon-Parau and Patagh Mountain in Kermanshah province. In the Bistoon-Parau region we used of Bistoon, Berkeh and Gaznahleh Springs data and in the Patagh region of Ghareh bolagh Spring data used. In this study, we used monthly precipitation and springs discharge during the 20 years. To determinate the development or undevelopment of karstic aquifer, recession coefficient, Storage dynamics volume and catchment area were calculated for each springs. The results of calculation of dynamic storage volume showed that the Ghare Bolagh Springs in Patagh region has dynamic storage volume equal 29/3 Million cubic meters that more than Bistoon, Berkeh and Gaznahleh springs in Bistoon-Parau region with dynamic storage volume equal 0/55, 2/2 and 1/3 Million cubic meters respectively. So these results showed the Bistoon-Parau more development of than Patagh region. Based on the results responses of the hydrological model the Bistoon-Parav is developed karst aquifer more quickly and severe than is Patagh area.
https://ije.ut.ac.ir/article_56146_a3f5ed16b77e809a22a43b9009751e5a.pdf
2015-06-22
163
173
10.22059/ije.2015.56146
dynamic Storage volume
Recession coefficient
lags time
Karst
Bistoon-Parav mass
Mohammad
Rahmati
mohammadrahmati14@yahoo.com
1
Faculty of Natural Resources, Tarbiat Modares University, Nour
AUTHOR
Hamid Reza
Moradi
hrmoradi@modares.ac.ir
2
Faculty of Agricultural, Ilam University, Ilam
LEAD_AUTHOR
Haji
Karimi
hajikarimi@yahoo.com
3
Faculty of Agricultural, Ilam University, Ilam
AUTHOR
Khalil
Jalili
khaliljalili@yahoo.com
4
PhD Student of Watershed Management Engineering, Faculty of Natural Resources, Tarbiat Modares University, Nour
AUTHOR
حمیدیزاده، فروغ.، کلانتری، نصراله.، کشاورزی، محمدرضا.، چرچی، عباس.، 1391. بررسی هیدروژئولوژیکی و زمینساختاری چشمة درهاناری در منطقة کارستی شیرین بهار استان خوزستان، تحقیقات منابع آب، 8(1): 42-30.
1
زیودار، مظفر.، ابراهیمی، بهروز.، 1387. بررسی حجم دینامیکی سرابهای کارستی در دوره خشکسالی، در اولین همایش بهرهبرداری بهینه از منابع آب استان لرستان، خرم آباد، 6 شهریور1390: 42-33.
2
کریمی، احمدرضا، 1380. مطالعه هیدرولوژیکی آبخوانهای کارستی تاقدیس پابده-لالی. پایاننامة کارشناسی ارشد آبهای زریزمینی، گروه زمینشناسی دانشگاه شهید چمران اهواز.
3
مقصودی، مهران، کریمی، حاجی، صفری، فرشاد، چهارراهی، ذبیحالله، 1388، بررسی توسعة کارست در تودة پرآو - بیستون با استفاده از ضرایب فرود، زمان مرگ چشمهها و تحلیل نتایج ایزوتوپی و شیمیایی، پژوهشهای جغرافیای طبیعی(69): 65-51.
4
میلانوویچ، پترویچ.، 1981. هیدرولوژی کارست، ترجمة آغاسی ع.، افراسیابیان ا.، انتشارات استانداردهای مهندسی آب کشور: 26.
5
ناصری، حمیدرضا.، علیجانی، فرشاد.، نخعی، محمد.، 1391. مقایسة اثرات خشکسالی بر هیدروژئولوژی کارست سازندهای آسماری و ایلام- سروک در جنوب غرب ایذه، مجلة پژوهش آب ایران، 6(11): 45-35.
6
Bonacci O., 1993. Karst Springs Hydrographs as Indicators of Karst Aquifers Journal of Hydrological Sciences38(1):24-37
7
Brian B. H., Brian A. S., Nico H., 2012. Real and Apparent Daily Springflow FluctuationsDuring Drought Conditions in a Karst Aquifer,Barton Springs Segment of the Edwards Aquifer, Central Texas, in: Gulf Coast Association of Geological Societies Transactions, 62(1): 189–204.
8
Chen Z., Stephen E., Grasby S. E., Osadetz K. G., 2004. Relation Between Climate Variability andGroundwater Levels in the Upper Carbonate Aquifer, Southern Manitoba, Canada,Journal of Hydrogeology, 290(2): 43–62.
9
Christophe J. G. D., 2008. Krast Aquifer Hydrogeology and Exploitation, Overexploitation and Contamination of Shared Groundwater Resource. NATO Science for Peace and Security Series.
10
Fiorillo F., 2009. Spring Hydrographs as Indicators of Droughts in a Karst Environment, Journal of Hydrology,373(3-4) :290-301
11
Fiorillo F., 2012. Tank-Reservoir Drainage as a Simulation of theRecession Limb of Karst Spring Hydrographs, Hydrogeology Journal20(7):1009–1019
12
Fiorillo F., Guadagno F. M., 2010. Karst Spring Discharges Analysis in Relation to Drought Periods, Using the SPI, Journal of Water Resources Management, 24(9):1867-1884
13
Ford D., Williams P., 2007. Karst Hydrogeology and Geomorphology, John Wiley and Sons Ltd1-562.
14
Iglesias A., Garrote L., Cancelliere A., Cubillo F., Wilhite D.A., 2009. Coping with Drought Risk in Agriculture and Water Supply Systems. 320 p.
15
Karimi H., Raeisi E., Bakalowicz M., 2005. Characterising the Main Karst Aquifers of the Alvand Basin, Northwest of Zagros, Iran, by a Hydrogeochemical Approach, Journal of Hydrogeology, 13(1): 787–799.
16
Orehova T., 2004. Comparative Estimate of Resistance to Drought for SelectedKarstic Aquifers in BULGARIA. in Journal of Speleology, 33(1/4):73-79.
17
ORIGINAL_ARTICLE
Study on Effects of Climate Changes on Surface Runoff Changes
Case Study: Urmia Lake Basin
Climate change is one of the most important problems in the present century. So assessing and prediction of future changes is important on water resources and so important for economics and socio-economic consequences. The purpose of this research is assessing the effects of climate change on surface runoff volumes under the scenarios: A1B, A2 and B1 using HadCM3 general circulation model and IHACRES rainfall-runoff model for three time periods (2011-2030, 2046-2065 and 2080-2099). LARS-WG downscaling model were used to bridge global data to sits, datasets. The results of the evaluation of observed and simulation data using statistical and of measurement error indices show that not difference between the simulated and the observed values on the critical error 0.5% .The results of climate model show that the average temperature of Basin will be increased between 0.55 to 3.15 ° C, and rainfall reduces amount 11.94 percent in the basin. Performance analysis of IHACRES rainfall-runoff model also showed good accuracy of the model to simulate the runoff changes in basin. The results the study of surface runoff changes showed that the long-term average of annual runoff is reduced in 2020s, 2050s and 2080s than the base period, respectively 5.4, 22.35 and 64.5 percent.
https://ije.ut.ac.ir/article_56152_1d7bf5d83d688e6c4c2193482b6a7958.pdf
2015-06-22
175
189
10.22059/ije.2015.56152
Massoud
Goudarzi
massoudgoodarzi@yahoo.com
1
Assistant Professor, Soil Conservation and Watershed Management Research Institute SCWMRI, Tehran
LEAD_AUTHOR
Boroumand
Salahi
bromand416@yahoo.com
2
Associate Professor, Faculty of Human Science, University of Mohaghegh Ardabili, Ardabil
AUTHOR
Asaad
Hoseini
hosseini.asad8@gmail.com
3
PhD Student, Faculty of Human Science, University of Mohaghegh Ardabili, Ardabil
AUTHOR
ORIGINAL_ARTICLE
Climatic and hydrological variable influence's on ground water level in Ajabshir plain
AbstractThe understanding of water resources for the purpose of ground and surface water management as two inseparable systems is great importance. In this study by examining the time series of climatic variables (precipitation), hydrological (flow and consume) and the level of water table, the parameters associated with each other and with fluctuating water table can be realized. There was a Direct relation between precipitation and flow with the ground water level, on the other hand the consume and ground water level were inversely related with each other. Then, by examining the cross correlation diagram of rainfall vs. water table level, reaction delays were identified. The results indicate water table level were related to rainfall with one month lag, whereas for flow, related on time; but for consume indicated two month lag in the plain Ajabshir. Finally, regression analysis of variables with the level of the water table was obtained.Keywords: precipitation, flow, water table level, cross correlation.
https://ije.ut.ac.ir/article_56157_d9f0c1417cbadcf7751a5242f27dcffc.pdf
2015-06-22
191
200
10.22059/ije.2015.56157
Asra
Asry
asrasry@gmail.com
1
MSc Student of Water Resources Engineering, University of Tabriz
AUTHOR
Ahmad
Fakherifard
fakheri@tabrizu.ac.ir
2
Department of Water Engineering, University of Tabriz, Tabriz
LEAD_AUTHOR
Ali
Zainali
ali_zeinali631@yahoo.com
3
Hydrogeologist, Shiraz University, Shiraz
AUTHOR
Esmail
Asadi
esasadi@gmail.com
4
Department of Water Engineering, University of Tabriz, Tabriz
AUTHOR
ORIGINAL_ARTICLE
Phytoremediation potential of Puccinellia distans (Jacq.) Parl in Cd contaminated soil and method of leaching reduction into ground waters
The aim of present study was to investigate phytoremediation potential of Puccinellia distans (Jacq) Parl using EDTA and DTPA and method of decreasing Cd- chelating leacing risk. The soil samples spiked with CdCl2. The treatments comprised the following dosages 2.5DTPA, 5DTPA, 2.5EDTA, 5EDTA, 2.5EDTA+2.5DTPA, 2.5EDTA+5DTPA, 5EDTA+2.5DTPA, 5EDTA+5DTPA and control pots were not treated with EDTA and DTPA. Bioconcentration factor (BCF), translocation factor (TF) and tolerance index (TI) were calculated to determine the Cd phytoremediation efficiency. Results indicated that EDTA and DTPA application significantly increased Cd content in the plant tissues and, root concentration of Cd was greater than the concentration in the shoot. The maximum bioconcentration factors (BCF) was observed in 5DTPA and 5EDTA respectively, and the maximum translocation factor (TF) was obtained for 5EDTA+5DTPA treatment. The results indicated that EDTA and DTPA had potential to promote the uptake of Cd by P.distans. In the next step to reduce leaching of Cd-chelate, 5mgkg-1 EDTA and DTPA in three ways of single, triple and six successive dosage were added to the soil. The results indicated that under single application, Cd content reached at its minimum concentration in the soil and, in the plant organs, the Cd concentration was the maximum. Metal concentration in the plant organs did not vary significant when triple and six successive dosage were added (p<5%). Overall, optimum phytoextraction of P.distans and Cd leaching reduction into ground waters was achieved when 5mg kg−1 EDTA and DTPAwas added in single dosage.
https://ije.ut.ac.ir/article_56159_4b7b66369406930210eff44083c527b6.pdf
2015-06-22
201
210
10.22059/ije.2015.56159
Mahdieh
Ebrahimi
maebrahimi2007@yahoo.com
1
Assistant Professor, Department of Range and Watershed Management, University of Zabol
LEAD_AUTHOR
Fattane
Ghasemi
fattaneghasemi@yahoo.com
2
M. Sc student, Department of Range and Watershed Management, University of Zabol
AUTHOR
Morteza
Pozesh Shirazi
shirazi754@yahoo.com
3
Instructor,, Agriculture and Natural Resources Center, Boshehr Province
AUTHOR
ORIGINAL_ARTICLE
Effect of Land Use Changes on Runoff Depth in Chalousrud Watershed
Due to the importance of the role of land use changes on runoff changes, several studies have taken place to develop models with simulating land use changes. In this study, the L-THIA model was used for estimating the runoff made by land use changes in Chalousrud watershed. Results of runoff modeling using L-THIA model showed that runoff depth increased from 422.98mm to 809.168mm during 1984 to 2000 that state an increase in runoff depth of 368.18 mm during 16 years and during 2000 to 2006 from 809.168mm to 825.496mm that state an increase 16.32 mm during 6 years. The results show that surface runoff depth increased due to decreasing of forest land with 3527.13ha from 58907.31ha to 55380.08ha and increasing of urban land with 7757.58ha from 7757.58ha to 362.50ha and increasing of abandoned land with 23176.01ha from 720.29ha to 23996.3ha. As a result of this study the L-THIA model has acceptable ability in explaining the way of land use changes effect on volume and depth of runoff. This model could provide the possibility of identifying accident prone areas, flood zones and flood management by spatial distribution map of runoff.
https://ije.ut.ac.ir/article_56160_30a6e8f44f142b5b4d74a3afca706911.pdf
2015-06-22
211
220
10.22059/ije.2015.56160
Mehdi
Vafakhah
vafakhah@modares.ac.ir
1
Faculty of Natural Resources, Tarbiat Modares University
LEAD_AUTHOR
Mohamad Reza
Javadi
javadi.desert@gmail.com
2
Islamic Azad University, Nour Branch
AUTHOR
Javanshir
Najafi Majd
javanshir.najafi@yahoo.com
3
Islamic Azad University, Nour Branch
AUTHOR
ORIGINAL_ARTICLE
Groundwater Potential Mapping using Shannon's Entropy and Random Forest Models in the Bojnourd Township
Nowadays, water supply for sustainable development is one of the most important concerns and challenges in most countries of the world. Due to determination of groundwater potential zones are one of the important tools in the conservation, management and utilization of water resources. Thus, the present study aimed to prioritize the effective factors on groundwater potential and its susceptibility zonation using Shannon’s entropy and Random Forest in Bojnourd Township. So, layers of slope angle, slope aspect, plan curvature, profile curvature, slope length, altitude, topographic wetness index, distance from fault, fault density, distance from river, drainage density, lithology and land use are known as affecting factors on Groundwater potential and were digitized in ArcGIS software environment. Subsequently, using Shannon’s entropy and Random Forest models, weight of affective factors was calculated in R statistical package and finally groundwater potential maps were prepared for the study area. The accuracy of groundwater potential zoning has been evaluated using relative operating curve (ROC), and according to the results, the accuracy of the Shannon’s entropy model was (85.55%), which is more acceptable than the accuracy of the Random Forest model (76.95 percent). Also, layers of land use, lithology, distance from river and altitude layers had the most effect on Groundwater potential in the study area based on the Shannon’s entropy model.
https://ije.ut.ac.ir/article_56242_0efabcba8b5a778b5a30e933a230010e.pdf
2015-06-22
221
232
10.22059/ije.2015.56242
Groundwater Potential
Random forest model
Shannon's Entropy Model
Spring
Bojnourd Plain
Mohsen
Zabihi
mohsen.zabihi@modares.ac.ir
1
PhD student in Range Sciences and Engineering, Islamic Azad University, Bojnourd, Iran
AUTHOR
Hamid Reza
Pourghasemi
hamidreza.pourghasemi@yahoo.com
2
Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, Iran
LEAD_AUTHOR
Morteza
Behzadfar
mbehzadfar@gmail.com
3
Planning and Management Organization of North-Khorasan Province
AUTHOR
[1]. ابراهیمخانی، سمیه؛ افضلی، مهدی؛ شکوهی، علی، 1390، پیشبینی و بررسی عوامل تصادفات جادهای با استفاده از الگوریتمهای دادهکاوی، فصلنامۀ دانش انتظامی زنجان، شمارۀ 1، سال اول: 127-111.
1
[2]. پورقاسمی، حمیدرضا؛ مرادی، حمیدرضا؛ فاطمی عقدا، سیدمحمود، 1392، تهیۀ نقشۀ حساسیت زمینلغزش با استفاده از سیستم استنتاج عصبی-فازی تطبیقی در شمال شهر تهران، مجلۀ پژوهشهای دانش زمین، شمارۀ 10: 78-63.
2
[3]. ذبیحی، محسن؛ شاهدی، کاکا؛ دارابی، حمید؛ صفری، عطا، 1392، مطالعۀ خشکسالی هواشناسی دشت بجنورد با استفاده از شاخصهای SPI، PNPI، NITZCHE، ZSI و DI، پنجمین کنفرانس مدیریت منابع آب ایران، بهمن ماه، تهران، ایران
3
[4]. فرشاد، محمد؛ ساده، جواد، 1392، مکانیابی خطای اتصال کوتاه در خطوط انتقال جریان مستقیم ولتاژ بالا با استفاده از شبکۀ عصبی، رگرسیون تعمیمیافته و الگوریتم جنگل تصادفی، سیستمهای هوشمند در مهندسی برق، سال چهارم، شمارۀ 2: 14-1.
4
[5]. فضلاولی، رامین؛ شریفی، فرود؛ بهنیا، عبدالکریم، 1385، بررسی تأثیر پخش سیلاب در تغذیۀ مصنوعی سفرۀ آب زیرزمینی دشت موسیان (استان ایلام)، مجلۀ منابع طبیعی ایران، جلد 59، شمارۀ 1: 74-54.
5
[6]. محمدی، حسینمراد؛ شمسیپور، علیاکبر، 1382، تأثیر خشکسالیهای اخیر در افت منابع آب زیرزمینی دشتهای شمال همدان، مجلۀ پژوهشهای جغرافیایی، شمارۀ 45: 130-115.
6
[7]. Bednarik, Martin. Magulova, Barbora, Matys. Mirko, and Marschalko, Marian, 2010, Landslide susceptibility assessment of the Kralovany–Liptovsky Mikulaš railway case study, Physics and Chemistry of the Earth, vol 35, pp162–171.
7
[8]. Chenini, Ismail. Ben Mammou, Abdallah, and May, Moufida El, 2010, Groundwater recharge zone mapping using GIS-based multi-criteria analysis: a case study in Central Tunisia (Maknassy Basin), Water Resources Management, vol 24 (5), pp 921–939.
8
[9]. Chowdhury, Alivia. Jha, Madan, Chowdary, Vuy, and Mal, Bimal C, 2009, Integrated remote sensing and GIS-based approach for assessing groundwater potential in West Medinipur district, West Bengal, India, International Journal of Remote Sensing, vol 30, pp 231–250.
9
[10]. Constantin, Mihaela. Bednarik, Martin, Jurchescu, Marta, and Vlaicu, Marius, 2011, Landslide susceptibility assessment using the bivariate statistical analysis and the index of entropy in the Sibiciu Basin (Romania), Environmental Earth Science, vol 63, pp 397–406.
10
[11]. Corsini, Alessandro . Cervi, Federico, and Ronchetti, Francesco, 2009, Weight of evidence and artificial neural networks for potential groundwater spring mapping: an application to the Mt. Modino area (Northern Apennines, Italy), Geomorphology, vol 111, pp 79–87.
11
[12]. Dar, Imran. Sankar, Kristian, and Dar, Mithas, 2010, Remote sensing technology and geographic information system modeling: an integrated approach towards the mapping of groundwater potential zones in Hardrock terrain, Mamundiyar basin, Journal of Hydrology, vol 394, pp 285–295.
12
[13]. DavoodiMoghaddam, Davood. Rezaei, Mojtaba, Pourghasemi, Hamid Reza, Pourtaghi, Zohre Sadat, and Pradhan, Biswajeet, 2013, Groundwater spring potential mapping using bivariate statistical model and GIS in the Taleghan watershed, Iran, Arabian Journal of Geoscience, vol 8, pp 913–929.
13
[14]. Fitts, Charles, Groundwater Science, Academic Press (Elsevier), 2002, pp 450.
14
[15]. Ganapuram, Sreedhar. Vijaya Kumar, Ganiga T, Murali, Krishna, Kahya, Ercan, and Demirel, Cuneyd, 2009, Mapping of groundwater potential zones in the Musi basin using remote sensing data and GIS, Advance Engineering Software, vol 40, pp 506–518.
15
[16]. Gaur, Shishir. Chahar, Bhagu R, and Graillot, Didier, 2011, Combined use of groundwater modeling and potential zone analysis for management of groundwater, International Journal of Applied Earth Observation and Geoinformation, vol 13, pp 127–139.
16
[17]. Ghayoumian, Jafar. Mohseni, Seyed Mohsen, Feiznia, Sadat, Nouri, Behzad, and Malekian, Arash, 2007, Application of GIS techniques to determine areas most suitable for artificial groundwater recharge in a coastal aquifer in southern Iran, Journal of Asian Earth Sciences, vol 30, pp 364–374.
17
[18]. Gupta, Manika, and Srivastava, Prashant, 2010, Integrating GIS and remote sensing for identification of groundwater potential zones in the hilly terrain of Pavagarh, Gujarat, India, Water International vol 35, pp 233–245.
18
[19]. Jain, Pradeep. 1998, Remote sensing techniques to locate ground water potential zones in upper Urmil River basin, district Chatarpur-central India, Journal of the Indian Society of Remote Sensing, vol 26, pp 135–147.
19
[20]. Jha, Madan. Chowdhury, Alivia, Chowdary, Vuy, and Peiffer, Stefan, 2007, Groundwater management and development by integrated remote sensing and geographic information systems: prospects and constraints, Water Resources Management, vol 21, pp 427–467.
20
[21]. Krishnamurthy, Jayasree. Srinivas, Rao G, 1995, Role of geological and geomorphological factors in groundwater exploration: a study using IRS LISS data, International Journal of Remote Sensing, vol 16(14), pp 2595–2618.
21
[22]. Manap, Mohamad Abd. Nampak, Haleh, Pradhan, Biswajeet, Lee, Saro, Soleiman, Wan Nor Azmin, and Ramli, Mohammad Firuz, 2012, Application of probabilistic-based frequency ratio model in groundwater potential mapping using remote sensing data and GIS, Arabian Journal of Geosciences, vol 7, pp 711-724.
22
[23]. Mukherjee, Soumyajit. 1996, Targeting saline aquifer by remote sensing and geophysical methods in a part of Hamirpur_Kanpur, India, Hydrogeology Journal, vol 19, pp 1867–1884.
23
[24]. Murthy, K Sri Rama. 2000, Groundwater potential in a semi-arid region of Andhra Pradesh- a geographical information system approach, International Journal of Remote Sensing, vol 21(9), pp 1867–1884.
24
[25]. Murthy, K Sri Rama. and Mamo, Abiy Gatachew, 2009, Multi-criteria decision evaluation in groundwater zones identification in Moyale-Teltelesubbasin, South Ethiopia, International Journal of Remote Sensing, vol 30, pp 2729–2740.
25
[26]. Naghibi, Seyed Amir. Pourghasemi, Hamid Reza, Pourtaghi Zohre Sadat, and Rezaei, Ashkan, 2014, Groundwater qanat potential mapping using frequency ratio and Shannon’s entropy models in the Moghan watershed, Iran, Journal of Earth Science, , vol 8 (1), pp 171-186.
26
[27]. Nefeslioglu, Hakan. Duman, Tamar, and Durmaz, Serap, 2008, Landslide susceptibility mapping for a part of tectonic Kelkit Valley (Easten Black Sea Region of Turkey), Geomorphology, vol 94, pp 401-418.
27
[28]. Nicodemus, Kristin K. 2011, Letter to the Editor: On the stability and ranking of predictors from random forest variable importance measures predictors from random forest variable importance measures, Brief Bio-inform, vol 12 (4), pp 369–373.
28
[29]. Oh, Hyun-Joo. Kim, Yong-Sung, Choi, Jong-Kuk, Park, Eungyu, and Lee, Saro, 2011, GIS mapping of regional probabilistic groundwater potential in the area of Pohang City, Korea, Journal of Hydrology, vol 399, pp 158–172.
29
[30]. Ozdemir, Adnan. 2011, GIS-based groundwater spring potential mapping in the Sultan Mountains (Konya, Turkey) using frequency ratio, weights of evidence and logistic regression methods and their comparison, Journal of Hydrology, vol 41, pp 290–308.
30
[31]. Pourghasemi, Hamid Reza. GoliJirandeh, Abbas, Pradhan, Biswajeet, Xu, Chong, and Gokceoglu, Candan, 2013, Landslide susceptibility mapping using support vector machine and GIS, Journal of Earth System Science, vol 122 (2), pp 349-369.
31
[32]. Pourghasemi, Hamid Reza. Mohammady, Majid, and Pradhan, Biswajeet, 2012, Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin, Iran, Catena, vol 97, pp 71–84.
32
[33]. Pourghasemi, Hamid Reza. Moradi, Hamid Reza, FatemiAghda, Seyed Mahmood, Gokceoglu, Candan, and Pradhan, Biswajeet, 2014, GIS-based landslide susceptibility mapping with probabilistic likelihood ratio and spatial multi criteria evaluation models (North of Tehran, Iran), Arabian Journal of Geoscience, vol 7, pp 1857-1878.
33
[34]. Pourtaghi, Zohre Sadat. and Pourghasemi, Hamid Reza, 2014, GIS-based groundwater spring potential assessment and mapping in the Birjand Township, southern Khorasan Province. Iran, Hydrogeology Journal, vol 22, pp 643-662.
34
[35]. Pradhan, Biswajeet. 2009, Groundwater potential zonation for basaltic watersheds using satellite remote sensing data and GIS techniques, Central European Journal of Geosciences, vol 1(1), pp 120–129.
35
[36]. Pradhan, Biswajeet. and Lee, Saro, 2010, Landslide susceptibility assessment and factor effect analysis: back propagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modeling, Environmental Modeling and Software, vol 25 (6), pp 747–759.
36
[37]. Rao, Subba, 2006. Groundwater potential index in a crystalline terrain using remote sensing data: Environmental Geology, vol 50 (7), pp 1067–1076.
37
[38]. Sener, Erhan. Davraz, Aysen, and Ozcelik, Mehmet, 2005, An integration of GIS and remote sensing in groundwater investigations: a case study in Burdur, Turkey, Hydrogeology Journal, vol 13, pp 826–834.
38
[39]. Shannon, Claude. 1948, A mathematical theory of communication, Bulletin System, Technology Journal, vol 27, pp 379–423.
39
ORIGINAL_ARTICLE
Simulation of Rainfall-Runoff Process using multilayer perceptron and Adaptive Neuro-Fuzzy Interface System and multiple regression
(Case Study: Khorramabd Watershed)
The discharge or runoff which ousts from a watershed is important. Because its deficiency leads to financial losses and its excesses cause damage in lives and property as flood. In this research using Artificial Neural Network Multi-layer Perceptron (MLP (and Adaptive Neuro-fuzzy interface system (ANFIS) and multiple regression method simulated rainfall- runoff process on daily basis in the Khorramabad watershed. For inputs, different combinations of precipitation inputs including current rainfall, pervious day rainfall and two previous days were used. Inputs membership function for ANFIS model in this research is: the trapezoid, triangular, Gaussian and Gaussian type 2. MLP model that used in this research, was evaluated with one hidden layer and the number of variables neurons. The results showed that Adaptive Neuro-fuzzy interface system (ANFIS) compared to multi-layer perceptron model (MLP) and multiple regression model, has better performance. Also by increasing in the number of inputs, involvement pervious day rainfall and two previous days, all three models performance will be better.
https://ije.ut.ac.ir/article_56243_f426d14b1f6600933ce6a191b919050b.pdf
2015-06-22
233
243
10.22059/ije.2015.56243
Adaptive Neuro-fuzzy interface system
Multi-Layer Perceptron
multiple regression
Runoff
Khorramabd watershed
Ali
Haghizadeh
alihaghi20@gmail.com
1
Department of Range and Watershed Management Engineering, Lorestan University, Khorramabad, Iran
LEAD_AUTHOR
Mohammad
Mohammadlou
muhammad.muhammadlou@gmail.com
2
MSc Student in Watershed Management Engineering, Department of Range and Watershed Management Engineering, Lorestan University, Khorramabad, Iran
AUTHOR
Fazel
Noori
fazelnoori65@yahoo.com
3
MSc Student in Watershed Management Engineering, Department of Range and Watershed Management Engineering, Lorestan University, Khorramabad, Iran
AUTHOR
[1]. فتحآبادی ابوالحسن، 1387، پیشبینی دبی رودخانه با استفاده از روشهای نوروفازی و مدلهای سریهای زمانی، علوم و مهندسی آبخیزداری ایران، سال دوم، شمارۀ 5: 30-21.
1
[2]. هنر تورج، ترازکار محمد حسن، و طرازکار محمدرضا، 1389، برآورد ضریب دبی سرریزهای جانبی با استفاده از سیستم استنتاج فازی– عصبی (ANFIS)، پژوهشهای حفاظت آب و خاک، جلد هفدهم، شمارۀ 2: 176-169.
2
[3]. عراقینژاد، شهاب؛ کارآموز، محمد، 1384، پیشبینی بلندمدت رواناب با استفاده از شبکههای عصبی مصنوعی و سیستم استنتاج فازی، تحقیقات منابع آب ایران، جلد 1، شمارۀ 2: 100-88.
3
[4]. نورانی، وحید؛ کینژاد، محمدعلی؛ ملکانی، لیلا، 1388، استفاده از سیستم فازی- عصبی تطبیقی در مدلسازی بارش- رواناب، نشریۀ مهندسی عمران و محیط زیست، جلد 39، شمارۀ 4: 81-75
4
[5]. نبیزاده، مرتضی؛ مساعدی، ابوالفضل؛ حسام، موسی؛ دهقانی امیراحمد، 1391، مقایسۀ عملکرد مدلهای مبتنی بر منطق فازی در پیشبینی آبدهی روزانه رودخانة لیقوان، مجلۀ پژوهشهای حفاظت آب و خاک، جلد 19، شمارۀ 1: 134-117.
5
[6]. زارع ابیانه، حمید؛ بیات ورکشی، مریم؛ 1390، ارزیابی مدلهای هوشمند عصبی و تجربی در تخمین رواناب سالانه، نشریۀ آب و خاک (علوم و صنایع کشاورزی)، جلد 25، شمارۀ 2: 379-365.
6
[7]. احمدزاده قره گویز، کاوه؛ میرلطیفی، سید مجید؛ محمدی، کوروش، 1389، مقایسه سیستم های هوش مصنوعی (ANFIS و ANN) در تخمین میزان تبخیر و تعرق گیاه مرجع در مناطق بسیار خشک ایران، نشریۀ آب و خاک، جلد 26، شمارۀ 4: 689-679.
7
[8]. سماعی رشتیزند، 1386، بارشهای مولد سیل در حوضة آبخیز خرمآباد، پایاننامۀ کارشناسی ارشد، دانشگاه آزاد اسلامی، واحد خرمآباد: 102.
8
[9]. Kurtulus, B. and M. Razack, 2010. Modeling daily discharge responses of a large karstic aquifer using soft computing methods: artificial neural network and neurofuzzy. Journal of Hydrology, 381: 101-111.
9
[10]. Conrads, P.A., et al, 1999, Comparing physics–based and neural network models for simulating salinity, temperature and dissolved oxygen in a complex, tidally affected river basin proceeding of the South Carolina environmental conference, South Carolina, Unites state.
10
[11]. Rajurkar, M.P., U.C. Kothyari and U.C. Chube. 2004. Modeling of the daily rainfall-runoff relationship with artificial neural network. Journal of Hydrology, 285(4): 96-113.
11
[12]. Firat, M. and M. Gungor. 2007. River flow estimation using adaptive neuro-fuzzy inference system. Mathematics and Computers in Simulation, 75(3-4): 87-96.
12
[13]. Dorum, A., Yarar, A., Faik Sevimli, M and Onüçyildiz, M., 2010. Modelling the rainfall–runoff data of Susurluk basin, Expert Systems with applications, 37(9): 6587-6593.
13
[14]. Kisi, O., Shiri and J., Tombul, M., 2012. Modeling rainfall-runoff process using soft computing techniques, Computers & Geosciences, 51: 108-117.
14
[15]. Bhatia, N., Sharma, L., Srivastava, S., Katyal, N., Srivastav, R., 2013. Streamflow Decomposition Based Integrated ANN Model, Open Journal of Modern Hydrology, 3: 15-19.
15
[16]. Vafakhah, M., 2012. Application of artificial neural networks and adaptive neuro-fuzzy inference system models to short-term stream flow forecasting, Canadian Journal of Civil Engineering, 39(4): 402-414.
16
[17]. Jang, J. S. R., Sun, C. T. and Mizutani, E. 1997. "Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence".
17
ORIGINAL_ARTICLE
English Abstracts
https://ije.ut.ac.ir/article_57183_73713a32ddf107c70913b0e1f9358522.pdf
2015-06-22
1
10
10.22059/ije.2015.57183