Predicting Evaporation Using Optimized Multilayer Perceptron

Mohammad Ehteram (), Akram Seifi () and Fatemeh Barzegari Banadkooki ()
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Mohammad Ehteram: Semnan University, Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering
Akram Seifi: Vali-e-Asr University of Rafsanjan, Department of Water Science and Engineering, College of Agriculture
Fatemeh Barzegari Banadkooki: Payame Noor University, Agricultural Department

Chapter Chapter 11 in Application of Machine Learning Models in Agricultural and Meteorological Sciences, 2023, pp 83-100 from Springer

Abstract: Abstract In this study, the sunflower algorithm (SUA), shark algorithm (SHA), and particle swarm optimization (PASO) were integrated with the multilayer perceptron (MULP) model to predict daily evaporation. The average temperature (AVT), relative humidity (REH), wind speed (WISP), number of sunny hours (NSH), and rainfall (RAI) were used to predict evaporation at the Hormozgan, Fars, Mazandaran, Yazd, and Isfahan stations located in Iran country. The accuracy of the models indicated that the MULP-SUA provided the highest accuracy at the different stations. Also, the AVT and NSH were the most important parameters in desert climates. The results indicated that the optimized MULP models performed better than the MULP models.

Keywords: Evaporation; Optimization algorithm; MULP model; Optimized models (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-19-9733-4_11

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DOI: 10.1007/978-981-19-9733-4_11

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