Daily Evapotranspiration Prediction at Arid and Semiarid Regions by Using Multiple Linear Regression Technique at Ramadi City in Iraq Region

Abstract

For many decades, because of the climate change, the interest in water resources management have been increased. The researchers curried out huge efforts to investigate the factors that influence the water resources quantities. Many of these efforts focused on studying the water losses by evapotranspiration. In the current study, a new mathematical model is introduced. It was built based on Multiple linear regression method (MLR). This model was utilized to estimate the daily evapotranspiration in Ramadi city which characterized with arid and semiarid environment and to investigate the influence of different parameters on the evapotranspiration process. The field data were collected from the digital meteorological station at the Upper Euphrates basin Developing Centre during the period from 23/11/2020 to 1/10/2022. These data include the evapotranspiration, maximum temperature, the minimum temperature, the average temperature, wind speed, relative humidity, and solar radiation. The model was calibrated using 80% of the data set, while 20% of the data set were used for validation. The solar radiation shows the highest impact on ET, while the lowest was recorded by relative humidity. High performance of the model was proved by testing it using Performance Indicators such as RMSE, NAE, MAPE, NSE, and R2.

Predictive Modeling of Daily Evapotranspiration in Arid Regions Using Artificial Neural Networks

“…Large number of such studies interested in the hydrological cycle parameters mostly the water losses that resulted from the evapotranspiration at the arid and semi-arid regions [6]. Since direct measurement of the rate of evapotranspiration is challenging and the process is imperceptible, number of models have been introduced to estimate it, based on the meteorological data that were collected under various climatic circumstances [7]. The physical-based complicated Penman-Monteith (PM) equation using full meteorological data is the only suggested approach for ET estimation [8,9].…”

“…Most studies indicate that the interrelationships that explain the occurrence of the process of evapotranspiration and the factors affecting its occurrence are unclear and non-linear [11]. The capability of artificial intelligence to mimic human brain functions gives it the ability to manage and understand complex problems and situations [7,11,12].…”