ارزیابی مدل‌های هیبریدی فراکاوشی در شبیه‌سازی اکسیژن محلول در آب رودخانه

نوع مقاله : پژوهشی

نویسندگان

1 استادیار گروه مهندسی آب، دانشکدۀ کشاورزی، دانشگاه لرستان، خرم‌آباد

2 دانشجوی دکتری سازه‌های آبی، دانشکدۀ کشاورزی، دانشگاه لرستان، خرم‌آباد

10.22059/ije.2022.330321.1547

چکیده

آب یکی از عناصر ضروری در طبیعت است که اساس زندگی انسان را تشکیل می‌دهد و به رشد و توسعۀ اقتصادی جوامع کمک می‌کند. آب سالم ارتباط تنگاتنگی با سلامت محیط زیست و فعالیت‌ها دارد. زندگی همۀ جانوران روی کرۀ زمین به آب و اکسیژن بستگی دارد. علاوه بر این، اکسیژن محلول کافی (DO) برای بقای جانوران آبزی بسیار مهم است. از این‌رو، در این پژوهش برای شبیه‏سازی اکسیژن محلول در آب رودخانۀ کامبرلند واقع در ایالات متحده از مدل ترکیبی شبکۀ عصبی مصنوعی (ANN) با موجک و الگوریتم‏های فراابتکاری گرگ خاکستری (GWO) و خفاش (BA) در مقیاس زمانی ماهانه طی دورۀ آماری 2010-2020 استفاده شد. معیارهای ضریب همبستگی (R2)، مجذور میانگین مربعات خطا (RMSE)، میانگین مطلق خطا (MAE) و ضریب بهره‏وری نش‌ـ ساتکلیف (NSE) برای ارزیابی و مقایسۀ عملکرد مدل‌ها مورد استفاده قرار گرفت. نتایج نشان داد هر سه مدل هیبریدی، در الگوهای ترکیبی نتایج بهتری نسبت به سایر الگوهای تعیین‏شده دارند. همچنین، با توجه به معیارهای ارزیابی مشخص شد که از بین مدل‌های به‏کاررفته در شبیه‌سازی اکسیژن محلول در آب رودخانه، مدل شبکۀ عصبی مصنوعی‌ـ موجک با ضریب تعیین (958/0R2=)، ریشۀ میانگین مربعات خطا ( 651/0RMSE=)، میانگین قدرمطلق خطا (334/0MAE= ) و ضریب نش‌ـ ساتکلیف (962/0NS=) در مرحلۀ صحت‌سنجی عملکرد بهتری نسبت به سایر مدل‌ها از خود نشان داده است.

کلیدواژه‌ها


عنوان مقاله [English]

Evaluation of hybrid metaheuristic models in simulation of dissolved oxygen in river water

نویسندگان [English]

  • hojatolh younesi 1
  • ahmad godarzi 2
1 Assistant Professor Department of Water Science and Engineering
2 phd student
چکیده [English]

Water is one of the most essential elements in nature that forms the basis of human life and contributes to the economic growth and development of societies. Healthy water is closely related to environmental health and activities. The life of all animals on Earth depends on water and oxygen. In addition, adequate dissolved oxygen (DO) is essential for the survival of aquatic animals. Therefore, in this study, to simulate the dissolved oxygen of the Cumberland River in the United States from the combined artificial neural network (ANN) model with wavelet and meta-heuristic algorithms of gray wolf (GWO) and bat (BA) on a monthly time scale during the statistical period. Used 2020-2010. The criteria of correlation coefficient (R2), squared mean square error (RMSE), absolute mean error (MAE) and Nash-Sutcliffe productivity coefficient (NSE) were used to evaluate and compare the performance of the models. The results showed that all three hybrid models have better results in hybrid models than the other designated models. Also, according to the evaluation criteria, it was found that among the models used in the simulation of dissolved oxygen in river water, the model of artificial neural network-wavelet with coefficient of determination (R2 = 0.958), the root mean square error (RMSE = 0.651), The mean absolute value of error (MAE = 0.334) and Nash Sutcliffe coefficient (NS = 0.962) in the validation stage showed better performance than other models.

کلیدواژه‌ها [English]

  • Dissolved Oxygen
  • Meta-heuristic algorithm
  • simulation
  • Hybrid Model
  • Krishna RS, Mishra J, Ighalo JO. Rising Demand for Rain Water Harvesting System in the World: A Case Study of Joda Town, India. World Scientific News,2020; 146(4): 47–59.
  • Forstinus NO, Ikechukwu NE, Emenike MP, Christiana AO. Water and waterborne diseases: A review, International Journal of Tropical Diseases and Health.2016; 12(4): 1–14.
  • Ighalo JO, Adeniyi AG, Adeniran JA, Ogunniyi S. A systematic literature analysis of the nature and regional distribution of water pollution sources in Nigeria”, Journal of Cleaner Production.2020; 124(3):566-576.
  • Khalil B, Adamowski J, Abdin A, Elsaadi A. A statistical approach for the estimation of water quality characteristics of ungauged streams/watersheds under stationary conditions. Journal of Hydrology.2019; 569(4):106–116.
  • Dizaji AR, Hosseini SA, Rezaverdinejad V, Sharafati A. Groundwater contamination vulnerability assessment using DRASTIC method, GSA, and uncertainty analysis. Arabian Journal of Geosciences.2020; 13(4):1–15.
  • Kisi O, Ay M. Comparison of ANN and ANFIS techniques in modeling dissolved oxygen. Sixteenth International Water Technology Conference, IWTC-16, Istanbul, Turkey,2012: 1–10.
  • Dogan E, Lent Sengorur B, Koklu R. Modeling biological oxygen demand of the Melen River in Turkey using an artificial neural network technique. Journal of Environmental Management. 2009; 90(5):19-35.
  • Chapman D. Water Quality Assessments”, ed f, editor. London: Chapman and Hall Ltd.1992:88-104.
  • Radwan M, Willems P, El-Sadek A, Berlamont J. Modelling of dissolved oxygen and biochemical oxygen demand in river water using a detailed and simplified model. Int J River Basin Manage. 2003; 1(4):97-103.
  • Ahmed AAM, Shah SMA. Application of adaptive neuro-fuzzy inference system (ANFIS) to estimate the biochemical oxygen demand (BOD) of Surma River. Journal of King Saud University-Engineering Sciences.2017; 29(3):237–243.
  • Yaseen ZM, Ehteram M, Sharafati A, Shahid S, Al-Ansari N, El-Shafie A. The integration of nature-inspired algorithms with least square support vector regression models: application to modeling river dissolved oxygen concentration. Water.2018; 10(3):11-24.
  • Diaz RJ, Rosenberg R. Spreading dead zones and consequences for marine ecosystems. Science.2008; 321(4):926–929.
  • Salcedo-Sanz S, Deo RC, Carro-Calvo L, Saavedra-Moreno B. Monthly prediction of air temperature in Australia and New Zealand with machine learning algorithms. Theoretical and Applied Climatology.2016; 125(4):13–25.
  • Afan HA, El-Shafie A, Yaseen ZM, Hameed MM, Mohtar HW, Hussain A. ANN based sediment prediction model utilizing different input scenarios. Water Resources Management.2015; 29(4):1231–1245.
  • Gocić M, Motamedi S, Shamshirband S, Petković D, Ch S, Hashim R, Arif M. Soft computing approaches for forecasting reference evapotranspiration. Comput. Electron. Agric.2015; 113(4):164–173
  • Ross AC, Stock AC. An assessment of the predictability of column minimum dissolved oxygen concentrations in Chesapeake Bay using a machine learning model. Coastal and Shelf Science.2019; 221:53-65
  • Shi P, Li G, Yuan Y, Huang G, Kuang L. Prediction of dissolved oxygen content in aquaculture using Clustering-based Softplus Extreme Learning Machine. Computers and Electronics in Agriculture.2019; 157(4):329-338
  • Li W, Fang H, Qin G, Tan X, Huang Z, Zeng F, Du H, Li S. Concentration estimation of dissolved oxygen in Pearl River Basin using input variable selection and machine learning techniques. Science of The Total Environment.2020;731:128-139
  • Adhaileh MH, Alsaade FW. Modelling and Prediction of Water Quality by Using Artificial Intelligence. Sustainability.2021; 13(4):42-59

 

  • Asadollah SB, Sharafati A, Motta D, Yaseen ZM. River water quality index prediction and uncertainty analysis: A comparative study of machine learning models. Journal of Environmental Chemical Engineering.2021; 9 (4): pp.228-245
  • Ahmed MH, Lin LS. Dissolved oxygen concentration predictions for running waters with different land use land cover using a quantile regression forest machine learning technique. Journal of Hydrology.2021; 597:324-341
  • Guo H, Hung JJ, Zhu X, Wang B, Tiang S, Xu W, Mai Y. A generalized machine learning approach for dissolved oxygen estimation at multiple spatiotemporal scales using remote sensing. Environmental Pollution.2021; 288(4): 58-69
  • Zhu N, Ji X, Tan J, Jiang Y, Gou Y. Prediction of dissolved oxygen concentration in aquatic systems based on transfer learning. Computers and Electronics in Agriculture.2021;180: 385-399
  • Tiyasha T, Tung TM, Bhagat SK, Tan ML, Jawad AH, Mohtar W, Yassen ZM. “Functionalization of remote sensing and on-site data for simulating surface water dissolved oxygen: Development of hybrid tree-based artificial intelligence models. Marine Pollution Bulletin.2021; 170:412-431
  • Huang J, Liu S, Gua Hassan S, Xu L, Hunag C. A hybrid model for short-term dissolved oxygen content prediction. Computers and Electronics in Agriculture. 2021; 186(4): 325-339
  • Liu H, Yang R, Duan Z, Wu H. A hybrid neural network model for marine dissolved oxygen concentrations time-series forecasting based on multi-factor analysis and a multi-model ensemble. Engineering.2021;19(4):112-128
  • Alizadeh MJ, Kavianpour MR. Development of wavelet-ANN models to predict water quality parameters in Hilo Bay, Pacific Ocean. Marine Pollution Bulletin.2015; 98(1–2):171-182
  • Rajaee T, Khani S, Ravansalar M. Artificial intelligence-based single and hybrid models for prediction of water quality in rivers: A review. Chemometrics and Intelligent Laboratory Systems.2020; 200(4):186-197
  • Xu C, Chen X, Zhang L. Predicting river dissolved oxygen time series based on stand-alone models and hybrid wavelet-based models. Journal of Environmental Management. 2021;295(4):166-178
  • Nourani V, Alami MT, and Aminfar MH. A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation. Engineering Applications of Artificial Intelligence.2009; 22(3): 466–472.
  • Nourani V, Kisi Ö, Komasi M. Two hybrid artificial intelligence approaches for modeling rainfall–runoff process. Journal of Hydrology.2011;402(3): 41–59.
  • Tokar A, Johnson P. Rainfall-Runoff Modeling Using Artificial Neural Networks. J Hydrol Eng.1999; 4(2): 232-239
  • Vapnik VN. Statistical learning theory. Wiley, New York.1998
  • Wang D, Safavi AA, Romagnoli JA. Wavelet-based adaptive robust M-estimator for non-linear system identification, AIChE Journal. 2000; 46(4):1607-1615.
  • Shin S, Kyung D, Lee S, Taik & Kim J, and Hyun J. An application of support vector machines in bankruptcy prediction model, Expert Systems with Applications. 2005;28(4):127-135.
  • Ostu N. A Threshold Selection Method from Gray-Level Histograms [J]. IEEE Transactions on Systems Man and Cybernetics. 1979; 9 (1): 62-66.
  • Amuda A, Brest J, Mezura-Montes E. Structured Population Size Reduction Differential Evolution with Multiple Mutation Strategies on CEC 2013 real parameter optimization. In Proceedings of the IEEE Congress on Evolutionary Computation, Cancun, Mexico.2013: 1925–1931
  • Nourani V, Kisi Ö, Komasi M. Two hybrid artificial intelligence approaches for modeling rainfall–runoff process. Journal of Hydrology.2011; 402(3): 41–59.
  • Nagy H, Watanabe K, Hirano M. Prediction of sediment load concentration in rivers using artificial neural network model, Journal of Hydraulics Engineering.2002; 128: 558-559.
  • Kisi O, Karahan M, Sen Z. River suspended sediment modeling using fuzzy logic approach.Hydrol Process.2006; 20: 4351-4362.
  • Zhu YM, Lu XX, Zhou Y. Suspended sediment flux modeling with artificial neural network: An example of the Longchuanjian River in the Upper Yangtze Catchment. Geomorphology.2007;84(1): 111-125.
  • Khosravi K, Nohani E, Maroufinia E, and Pourghasemi HRA. GIS-based flood susceptibility assessment and its mapping in Iran: a comparison between frequency ratio and weights of evidence bivariate statistical models with multi-criteria method. Natural Hazards.2016; 83(2):1-41
  • Dehghani R, Torabi H. Dissolved oxygen concentration predictions for running waters with using hybrid machine learning techniques.
    Modeling Earth Systems and Environment.2021;11(3):424-436
  • Babaali HR, Dehghani R. Evaluation of the performance of the wavelet neural network model in estimating daily discharge. Irrigation Science and Engineering.2017; 6(4):22-35 [Persian]
دوره 8، شماره 4
اسفند 1400
صفحه 1113-1125
  • تاریخ دریافت: 15 شهریور 1400
  • تاریخ بازنگری: 11 بهمن 1400
  • تاریخ پذیرش: 11 بهمن 1400
  • تاریخ اولین انتشار: 01 اسفند 1400