Document Type : Research Article
Authors
1 university yazd- yazd-iran
2 Faculty of Natural Resources, UniversityYazd
3 Faculty of Natural Resources, University of Jiroft, Jiroft
Abstract
Keywords
Main Subjects
]1[ Jha M.K, Chowdhury A, Chowdary V.M and Peiffer S. Groundwater management and development by integrated
remote sensing and geographic information systems: prospects and constraints. Water Resource Management.
2006; 21(2):427-467.
]2[ Han J.Ch, Huang Y, Li Zh, Zhao Ch, Cheng G and Huang P. Groundwater level prediction using a SOM-aided stepwise
cluster inference model. Journal of Environmental Management. 2016; 182:308-321.
]3[ Nourani V and Mousavi Sh. Spatiotemporal groundwater level modeling using hybridartificial intelligence-meshless
method. Journal of Hydrology. 2016; 536:10-25.
]4[ Recep C. Temporal Changes in the Groundwater Level in the Upper Tigris Basin, Turkey, Determined by a GIS
Technique. Journal of African Earth Sciences. 2015; 107:134-143.
]5[ Lijzen J.P, Otte P and Van Dreumel M. Towards sustainable management of groundwater: policy developments in
The Netherlands. Sci. Total Environ.2014; (485-486):804-809.
]6[ Sun R, Jin M, Giordano M and Villholth K.G. Urban and rural groundwater use in Zhengzhou, China: challenges in
joint management. Hydrogeol. J. 2009; 17(6):1495-1506.
]7[ Hua Sh, Liang J, Zeng G, Xu M, Zhang Ch, Yuan Y, et al. How to manage future groundwater resource of China
under climate change and urbanization: An optimal stage investment design from modern portfolio theory. Water
Research.2015; 85:31-37.
]8[ Samadi R., Behmanesh J and Rezaei H. Investigation of groundwater level changes trend (Case study: Urmia plain).
]9[ Choi M, Jacobs J.M, Anderson M.C, Bosch D.D. Evaluation of drought indices via remotely sensed data with
hydrological variables. Journal of Hydrology. 2013; 476 : 265-273.
]10[ Alizadeh A. Principles of Applied Hydrology. 40th Reprint, 7th Edition, Astan Quds Razavi Publication. 2015;
941pp (Persian).
]11[ Bordi I. and Sutera A. Drought variability andits climatic implications. Global and PlanetaryChange. 2004; 40 ( 1,
2): 115- 127.
]12[ Dracup J.A., Lee K.S and Paulson E.G. on the definition of drought. Water Resources Research. 1980; 16(2): 297-
302.
]13[ Wilhite D.A. and Glantz M.H. Understanding the drought phenomenon: the role of definitions. Water Internationa.
1985; 10 (3):111-120.
]14[ Villholth K.G, Tottrup C, Stendel M, Maherry A. Integrated mapping ofgroundwater drought risk in the Southern
African Development Community (SADC) region, Hydrogeology Journal, 21(4):863-885.
]15[ Peters E, Bier G, Van Lanen H.A.J and Torfs P.J.J.F. Drought Propagation and Spatial Distribution of Drought in a
Groundwater Catchment. Hydrology Journal. 2006; 321(1-4):257-275.
]16[ Allard D, D’Or D, Froidevau R. An efficient maximum entropy approach for categorical variable prediction. Europ
J Soil Sci. 2011; 62 (3): 381–393.
]17[ Rajabzadeh F, Ghiasi S, Zahmati A. Effectiveness of maximum entropy algorithm and geographic information
system in assessing the sensitivity of shallow landslides. Water and Soil Resources Protection Quarterly. 2018; 8
(2): 57-74 (Persian).
]18[ Teimuri M, Asadi Nelivan A. Sensitivity zoning and prioritization of factors affecting the occurrence of landslides
using the maximum entropy model (case study: Lorestan province). Hydrogeomorphology Journal. 2019; 6 (21):
155-179 (Persian).
]19[ Park N.W. Using maximum entropy modeling for landslide susceptibility mapping with multiple geoenvironmental
data sets. Environ Earth Sci. 2015; 73: 937-949.
]20[ Kornejady A, Ownegh M, Bahremand A. Landslide susceptibility assessment using maximum entropy model with
two different data sampling methods. Catena. 2017; 152: 144-162.
]21[ Kerekes A.H, Poszet S.L, Andrea G.Á.L. Landslide susceptibility assessment using the maximum entropy model
in a sector of the Cluj–Napoca Municipality, Romania. Revista de Geomorfologie. 2018; 20 (1): 130-146.
]22[ Pandey V.K, Pourghasemi H.R, Sharma M.C. Landslide susceptibility mapping using maximum entropy and
support vector machine models along the Highway Corridor, Garhwal Himalaya. Geocarto International. 2020;
35(2): 168-187.
]23[ Rahmati O, Pourghasemi H.R, Meless A.M. Application of GIS-Based Data Driven Random Forest and Maximum
Entropy Models for Groundwater Potential Mapping: A case Study at Mehran Region, Iran. Catena. 2016; 137:
360-372.
]24[ Razandi Y, Farokhzadeh B, Yousefzadeh Chabok M, Teimurian T. Applying Maximum Entropy algorithm
(MAXENT) in Ground water potential mapping Case study: Hamedan-Bahar Plain. Journal of Irrigation and Water
Engineering. 2017; 8(29): 110-123 (Persian).
]25[ Zabihi M, Purghasemi H.R, Behzadfar M. Preparation of groundwater potential map using Shannon entropy models
and random forest in Bojnourd plain. Ecohydrology. 2015; 2 (2): 221-232.
]26[ Oliyayi A, Parvian N, Khsravi O. Identifying the potential of groundwater resources in hard formations as a solution
to manage the water crisis (Case study: Kalat Naderi watershed). Geography and Environmental Hazards. 2017;
24: 143-158 (Persian).
]27[ Castillo J.L, Martínez Cruz D. A, Ramos Leal J. A, Tuxpan Vargas J, Rodríguez Tapia, S. A & Marín Celestino A. E.
Delineation of groundwater potential zones (GWPZs) in a semi-arid basin through remote sensing, GIS, and AHP
approaches. Water. 2022; 14(13): 2138 (Persian).
]28[ Rahaman M. H, Sajjad H, Masroor M, Bhuyan N. & Rehman S. Delineating groundwater potential zones using
geospatial techniques and fuzzy analytical hierarchy process (FAHP) ensemble in the data-scarce region: evidence
from the lower Thoubal river watershed of Manipur, India. Arabian Journal of Geosciences.2022; 15(8): 677
(Persian).
]29[ Haghighi A, Nohtani M, Dahmardeh Ghaleno M.R. & Rafiei Sardooi E. Evaluating and detecting potential of
groundwater resources using Fuzzy-AHP method and remote sensing data (Case study: Bam-Narmashir plain).
Integrated Watershed Management. 2023; 3 (1): 16-37 (Persian).
]30[ Zakerinejad R, Maerker M. Prediction of Gully erosion susceptibilities using detailed terrain analysis and maximum
entropy modeling: a case study in the Mazayejan Plain. Southwest Iran. Geografia Fisica e Dinamica Quaternaria.
2014; 37(1): 67-76.
]31[ Deo R.C, Sahin M. Application of the extreme learning machine algorithm for the prediction of monthly Effective
Drought Index in eastern Australia. Atmospheric Research. 2015; 153: 512-525.
]32[ Park S, Im J, Jang E, RheeJ. Drought assessment and monitoring through blending of multi-sensor indices using
machine learning approaches for different climate regions. Agricultural and Forest Meteorology. 2016; 216: 157-
169.
]33[ Jaafarzadeh M, Tahmasebi N, Hagizaeh A, Pourghasemi H.R, Rouhani H Prediction of susceptible areas for
groundwater recharge based on maximum entropy model. Adv. Appl. Geol. 2022; 11(4): 723-739 (Persian).
]34[ Rahmati A, Tahmasbpour N, Haqizadeh A, Pourqasmi H, Faizizadeh B. Evaluation of the effectiveness of the
maximum entropy model in predicting the potential for watershed erosion in the Keshkan-Poldakhter watershed.
Scientific-Research Journal of Watershed Engineering and Management. 2019; 10(4): 727-738 (Persian).
]35[ Faryabi M. Investigating the impact of large floods of the Halilroud river on the groundwater level of the aquifer in
the Jiroft plain. The 10th International Conference on Rain Catchment Surface Systems, November 1400 (Persian).
]36[ Porsidi A, Kashkouli H. A. study of the underground water condition of Jiroft Plain with the help of PMWIN model,
Irrigation Science and Engineering (Scientific Journal of Agriculture). 35(2) (Persian).
]37[ Saif M, Mosaedi A, and Mohammadzadeh H. Investigation of hydrological drought of Fasa plain aquifer using
underground water resource index (GRI). 2013;The 15th conference of the Geological Society of Iran.
]38[ Mendicino G, Senatore A, Versace P. A Groundwater Resource Index (GRI) for drought monitoring and forecasting
in a Mediterranean climate. Hydrology Journal. 2008; 357: 282-302 (Persian).
]39[ Fatehi Merj A, Vahidian S. Survey of meteorological drought, hydrological agriculture using GIS in Khuzestan
province. Iran Watershed Science and Engineering Journal. 2014; 7(23): 19-32 (Persian).
]40[ Vijith H. Groundwater potential in the hard rock terrain of Western Ghats: a case study from Kottayam District,
Kerala using resourcesat (IRS-P6) data and GIS techniques. Journal of Indian Society of Remote sensing. 2007;
35(2): 163–171.
]41[ Banavar J.R, Maritan A, Volkov I. Applications of the principle of maximum entropy: from physics to ecology.
Journal of Physics: Condensed Matter. 2010; 22 (6): p.063101.
]42[ Luca F, Conforti M, Robustelli G. Comparison of GIS-based gullying susceptibility mapping using bivariate and
multivariate statistics: Northern Calabria, South Italy. Geomorphology. 2011;134 (3): 297-308.
]43[ Shannon C. A mathematical theory of communication, Bulletin System. Technology Journal. 1948; 27: 379– 423.
]44[ Khalasi Ahvazi L, Zare Chahouki M, Hosseini Z. Modeling the geographical distribution of the habitat of Artemisia
aucheri and Artemisia sieberi based on presence-based methods (MaxEnt and ENFA). Journal of Renewable Natural
Resources Research. 2015; 6 (1): 57-74 (Persian).
]45[ Agrawal D, Singh J.K, Kumar A. Maximum entropy-based conditional probability distribution runoff model.
Biosystem Engeenering. 2005; 90 (1): 103–113.
]46[ MirAbbasi Najafabadi R, Din Pajhooh Y, Fakheri Fard A. Runoff modeling using the principle of maximum entropy
(Case study: Kasilian basin). Journal of Agricultural Science and Technology and Natural Resources, Soil and Water
Sciences. 2011; 15 (58): 39-51 (Persian).
]47[ Yesilnacar E.K. The application of computational intelligence to landslide susceptibility mapping in Turkey. PhD
Thesis, University of Melbourne.2005; 423 pages.
]48[ Damavandi A.A, Rezaei F. & Panahi M. Potential of Groundwater Resources Using Remote Sensing and
Geographic Information System Case Study: Saleh Ben Watershed. 2011; 2nd Earth Sciences Conference. 1-9 (
Persian).
]49[ Hosmer D.W, Lemeshow S. Applied Logistic Regression. Second edition. New York 392 pp, NY, USA: John Wiley
and Sons. 2000.
]50[ Kheirkhah M, Mohebi Tafreshi GH, Majidi A, Asadian F. Using Integration GIS and Remote Sensing Techniques
by Decision Support System to Locate Suitable Areas construction of Underground Dam (The Case Study of Qazvin
province). Iranian Journal of Watershed Management Science. 2014; 8 (26): 35-50 (Persian)
)