Application of Artificial Intelligence Models for Evapotranspiration Prediction along the Southern Coast of Turkey

Mohammed Majeed Hameed,Mohammed Abd Kareem Khalaf,Mohamed Khalid Alomar,Siti Fatin Mohd Razali,Wajdi Jaber Baniya,Mohammed Abdulhakim Alsaadi,Ahmad Sharafati,Honglei Xu

doi:10.1155/2021/8850243

Abstract

Reference evapotranspirationETo is one of the most significant factors in the hydrological cycle since it has a great influence on water resource planning and management, agriculture and irrigation management, and other processes in the hydrological sector. In this study, an efficient and local predictive model was established to forecast the monthly meanETo tover Turkey based on the data collected from 35 locations. For this purpose, twenty input combinations including hydrological and geographical parameters were introduced to three different approaches called multiple linear regressionMLR, random forestRF, and extreme learning machineELM. Moreover, in this study, large investigation was done, involving the establishment of 60 models and their assessment using ten statistical measures. The outcome of this study revealed that the ELM approach achieved high accurate estimation in accordance with the Penman–Monteith formula as compared to other models such asMLRandRF. Moreover, among the 10 statistical measures, the uncertainty at 95%U95indicator showed an excellent ability to select the best and most efficient forecast model. The superiority ofELMin the prediction of mean monthlyETo overMLRandRFapproaches is illustrated in the reduction of theU95parameter to 49.02% and 34.07% forRFandMLRmodels, respectively. Furthermore, it is possible to develop a local predictive model with the help of computer to estimate theETo using the simplest and cheapest meteorological and geographical variables with acceptable accuracy.

Highlights

Results and Discussion is section of the study is dedicated to illustrating the forecast results obtained for mean monthly EToover Turkey via three different predictive models, namely, multiple linear regression (MLR), random forest (RF), and extreme learning machine (ELM)
Khoob [94] conducted a study using artificial neural network (ANN) to predict ETOin Safiabad station, which is located in Southern Iran. e outcome of this study revealed that the suggested ANN model performed very well when compared with the actual values ofETO. e most significant observation is that the accuracy of the model was very good with a high R2of 0.9135
The wellknown Penman–Monteith equation for computing the evapotranspiration exists, there are some difficulties in accurately calculating some of its parameters such as solar radiation and sensible heat flux into the soil. erefore, in this study, three different approaches were employed, namely, ELM, RF, and MLR, based on geographical and meteorological parameters for the prediction of the mean monthly evapotranspiration over southern Mediterranean coast of Turkey

Summary

Introduction

Hydrological cycles, and, water availability are directly affected by global warming [3,4,5]. Us, one of the most essential indicators of climate change is the referenced evapotranspiration (ETo), which is considered as the most complicated element in the hydrological cycle [6,7,8]. E first is when water evaporates from the surface of the soil, lakes, rivers, etc., and this process is called physical evaporation. Erefore, the main parameters that affect the ETo process is the sun radiation, wind speed and direction, air temperature, and humidity [9,10,11]. Based on the stated literature, a precise measurement and prediction of ETois essential for quantifying surface energy and water reserves worldwide [14,15,16]

Methods

Results

Conclusion