Estimation of Daily Evaporation Rate using Artificial Neural Network in Shiraz and Zarghan Cities
Subject Areas : Regional Planning
Mohammad Shabani
1
(Department of Water Engineering, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran)
Keywords: Artificial neural network, Shiraz city, Evaporation, Model generalizability, Zarghan city,
Abstract :
Evaporation is one of the most important components of the hydrological cycle that plays a very important role in the management of water resources and the environment. Knowing the amount of water lost due to the evaporation process in an area, especially in arid and semi-arid areas that face shortages of water resources, is one of the most important management principles in regional planning. The aim of this study was to evaluate the accuracy of artificial neural network method in estimating daily evaporation in Shiraz meteorological station and its generalizability in Zarghan meteorological station located in Fars province. For this purpose, 1775 data on a daily scale from meteorological factors including temperature, relative humidity, wind speed, sunshine were collected and then the amount of daily evaporation was estimated using 4 models of artificial neural network. For modeling in this study, multilayer perceptron neural network and sigmoid function were used. The results obtained from four models of artificial neural network were evaluated based on the criteria of coefficient of determination (R2), Nash-Sutcliffe coefficient (NSC) and Root Mean Square Error (RMSe). The results showed that in Shiraz meteorological station, model 4 with a structure of 5-6-1 neurons has less RMSe and higher R2 and NSC in both training and testing stages than other models, so as a superior model to predict the rate of evaporation Was selected daily at Shiraz meteorological station. The results of the generalizability of Model 4 with 5-6-1 structure in Zarghan meteorological station also show the high accuracy of this model in predicting daily evaporation in this station, so it can be used as a suitable model to predict daily evaporation values in This station was used during periods when evaporation was not measured.
Alsumaiei A A. (2019). Utility of Artificial Neural Networks in Modeling Pan Evaporation in Hyper-Arid Climates, Water, 12 (1508), PP:1-17.
Babita, M, Diwakar, N. (2021). Pan evaporation modeling in different agro-climatic zones using functional link artificial neural network, Information Processing in Agriculture, 8(1), PP: 134-147.
Biazar, S M, Ghorbani, M A, Shahedi, K. (2019). Uncertainty of Artificial Neural Networks for Daily Evaporation Prediction (Case Study: Rasht and Manjil Stations), Journal of Watershed Management Research, 10 (19), PP: 1-12. [In Persian]
Chang, F J, Chang, K Y, Chang, L C. (2008). Counter propagation fuzzy-neural network for city flood control system, Journal of Hydrology, 358 (1–2), PP: 24–34.
Chang, F J, Sun, W, Chung, C H. (2013). Dynamic factor analysis and artificial neural network for estimating pan evaporation at multiple stations in northern Taiwan, Hydrological Sciences Journal, 58 (4), PP: 813–825.
Dehghani, A A, Piri, M, Hesam, M, Dehghani, D. (2011). Estimation of Daily Pan Evaporation by Using MLP,RBF and Recuurent Neural Networks, Journal of Water and Soil Conservation, 17 (2), PP: 49-67. [In Persian]
Deswal, S, Pal, M. (2008). Artificial neural network-based modeling of evaporation losses in reservoirs, World academy of Science, Engineering and Technology, 39, PP: 279-383.
Dou,X, Yang, Y. (2018). Evapotranspiration estimation using four different machine learning approaches in different terrestrial ecosystems, Computers and Electronics in Agriculture 148 (2018), PP: 95–106.
Goel, A. (2009). ANN based modeling for prediction of evaporation in reservoirs, Internatinal Journal of Engineering, 22(4), PP: 351-358.
Kashefipour, S M. (2003). Use of Artificial Neural Networks (ANNs) in River Engineering, River Engineering Conference, January 2003, Ahwaz, IRAN, PP: 1-10. [In Persian]
Kashefipour, S M. (2007). Modeling Longitudinal Dispersion in Open Natural Channels Using Anns, Journal of Hydraulics, (3), PP: 15-25. [In Persian]
Keskin, M E, Terzi Ö. (2006). Artificial neural network models of daily pan evaporation, Journal Hydrology Engineering, 11(1), pp:65–70.
Khrshieddoust, A M, Mirhashemi, H, Nazari, M. (2019). Estimating evapotranspiration using neural networks and genetic algorithms (case study:Tabriz station), Journal of Geography and Planning, 23 (68), PP: 71-90. [In Persian]
KişI, Ö. (2009). Daily pan evaporation modelling using multi-layer perceptrons and radial basis neural networks, Hydrol Process, 23, PP:213–23.
Kişi, Ö. (2013). Evolutionary neural networks for monthly pan evaporation modeling, Journal Hydrology Engineering, 498, pp:36–45.
Landeras, G, Ortiz-Barredo, A, Lopez, J J. (2008). Comparison of artificial neural network models and empirical and semi-empirical equations for daily reference evapotranspiration estimation in the Basque Country (NorthernSpain), ournal of Agricultural Water Management, 95 (5), 553–565.
Maier, H R, Dandy, G C. (2000). Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications, Environmental Modelling and software, 15(1), PP: 101-24.
Mansouri, N. (2012). Environmental Pollution (Air, Water and Wastewater, Solid Waste and Noize), Arad Ketab Publication, First ed, Iran.
Mohamadi, S, Ehteram,M, El‑Shafie, A. (2020. Accuracy enhancement for monthly evaporation predicting model utilizing evolutionary machine learning methods, International Journal of Environmental Science and Technology, Volume 17, issue 7, Pages: 3373 – 3396.
Nourani, V, Elkiran, G, Abdullahi, J. (2019). Multi-station artificial intelligence-based ensemble modeling of reference evapotranspiration using pan evaporation measurements, Journal of Hydrology, 577, pp: 1-20.
Nourani, V, Sayyah Fard, M. (2013). Sensitivity Analysis of ANN Inputs in Estimating Daily Evaporation, Journal of Water and Wastewater, 24(3), PP: 88-100. [In Persian]
Ozkan, C, Kisi, O, Akay, B. (2011). Neural networks with artificial bee colony algorithm for modeling daily reference evapotranspiration, Irrigation Science, 29 (6), PP: 431–441.
Piri, J, Mohammadi, K, Shamshirband, S, Akib, S. (2016). Expression of concern: assessing the suitability of hybridizing the Cuckoo optimization algorithm with ANN and ANFIS techniques to predict daily evaporation, Environmental Earth Sciences , 78(8), PP: 8.
Qasem, S N, Samadianfard S, Kheshtgar, S, Jarhan, S. (2019). Modeling monthly pan evaporation using wavelet support vector regression and wavelet artificial neural networks in arid and humid climates, Engineering Applications of Computational Fluid Mechanics, 13 (1), PP:177-187.
Razavizadeh, S, Dargahian, F. (2019). Optimization of Artificial Neural Network Structure in Prediction of Sediment Discharge Using Taguchi Method, Iiranian Journal of Watershed Management Science and Engineering, 12 (43), PP: 2019. 89-97. [In Persian]
Seyed Vahid Shahoei, S V, Porhemmat, J, Sedghi, H, Hosseini, M, Saremi, A. (2018). Monthly runoff simulation through SWAT hydrological model and evaluation of model in calibration and validation periods, case study: Ravansar Sanjabi Basin in Kermanshah Province, Iran Journal of Watershed Engineering and Management, 10 (3), PP: 464-477. [In Persian]
Shabani,M. (2013).The accuracy evaluation of artificial neural networks in estimating instantaneous peak flow, Case study: Fars province watersheds, Watershed Engineering and Management, 4(4), pp: 180-187. [In Persian]
Shahi1, S, Mousavi1, S F, Hosseini, K H. (2021). Simulation of Pan Evaporation Rate by ANN Artificial Intelligence Model in Damghan Region, Journal of Soft Computing in Civil Engineering, 5 (3), PP: 75-87.
Shamsodini, A, Shakoor, A, Gorjian, P. (2013). An analysis of the living conditions of rural migrants in cities (Case study: Rostam village 2- Rostam city), Quarterly Journal of based Territorial Planning, (20), PP: 75-110. [In Persian]
Sudheer, K P, Gosain, A K, Mohana Rangan, D, Saheb, S M. (2002). Modelling evaporation using an artificial neural network algorithm,Hydrological Processes, 16(16), pp:3189–3202.
Tabari, H, Marofi, S, Savziparvar, A A. (2010). Estimation of daily pan evaporation using artificial neural networks and multivariate non-linear regression, Journal. of Irrigation. Science, 28, 399-406. [In Persian]
Trajkovic, S, Kolakovic, S. (2010). Comparison of simplified panbased equations for estimating reference evapotranspiration, Journal of Irrigation and Drainage Engineering, 136 (2), PP: 137–140.
Wang, K, Dickinson, R E. (2012). A review of global terrestrial evapotranspiration: Observation, modeling, climatology, and climatic variability. Reviews. Geophysics, 50 (2), pp:1-54.
Wang, L, Kisi, O, Zounemat-Kermani, M, Li, H. (2017). Pan evaporation modeling using six different heuristic computing methods in different climates of China, Journal of Hydrol, 544, pp: 407–427.
Warnaka, K, Pochop, L. (1988). Analyses of equations for freewater evaporation estimates, Water Resources Research, 24 (7), PP: 979–984.
Yang, C T, Marsooli, R, Aalami, M T. (2009). Evaluation of total load sediment transport formulas using ANN, International Journal of Sedime
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