Two Comprehensive and Practical Methods for Simulating Pan Evaporation under Different Climatic Conditions in Iran

Abstract

Evaporation from surface water plays a crucial role in water accounting of basins, water resource management, and irrigation systems management. As such, the simulation of evaporation with high accuracy is very important. In this study, two methods for simulating pan evaporation under different climatic conditions in Iran were developed. In the first method, six experimental relationships (linear, quadratic, and cubic, with two input combinations) were determined for Iran’s six climate types, inspired by a multilayer perceptron neural network (MLP-NN) neuron and optimized with the genetic algorithm. The best relationship of the six was selected for each climate type, and the results were presented in a three-dimensional graph. The best overall relationship obtained in the first method was used as the basic relationship in the second method, and climatic correction coefficients were determined for other climate types using the genetic algorithm optimization model. Finally, the accuracy of the two methods was validated using data from 32 synoptic weather stations throughout Iran. For the first method, error tolerance diagrams and statistical coefficients showed that a quadratic experimental relationship performed best under all climatic conditions. To simplify the method, two graphs were created based on the quadratic relationship for the different climate types, with the axes of the graphs showing relative humidity and temperature, and with pan evaporation, were drawn as contours. For the second method, the quadratic relationship for semi-dry conditions was selected as the basic relationship. The estimated climatic correction coefficients for other climate types lay between 0.8 and 1 for dry, semi-dry, semi-humid, Mediterranean climates, and between 0.4 and 0.6 for humid and very humid climates, indicating that one single relationship cannot be used to simulate pan evaporation for all climatic conditions in Iran. The validation results confirmed the accuracy of the two methods in simulating pan evaporation under different climatic conditions in Iran.

Evaluating Three Supervised Machine Learning Algorithms (LM, BR, and SCG) for Daily Pan Evaporation Estimation in a Semi-Arid Region

“…Several studies have been conducted in the field of estimating evaporation and evapotranspiration, and all of them have confirmed the efficiency of the MLP model [22,30,[33][34][35][36][57][58][59][60], and their results are in line with the present study. In regions with the same climate as Shiraz station, fewer studies have been conducted on evaporation estimation by the MLP model.…”

“…This is while the RMSE value obtained from the estimation of evaporation in the current research is equal to 1.629-1.742 mm/day. The reason for this difference can be seen in the training algorithms of the MLP; Where Dehghanipour et al [22] achieved their results by the LM algorithm, and the current research by BR. Additionally, Ashrafzadeh et al [3] used MLP-LM in a study in the very humid climate of northern Iran and achieved an accuracy of RMSE = 1.088-1.197 mm/day and WI = 0.903-0.942.…”

“…In regions with the same climate as Shiraz station, fewer studies have been conducted on evaporation estimation by the MLP model. Dehghanipour et al [22] used this model for semi-arid and arid regions of Iran. They also considered variables such as wind speed and sunshine hours as model inputs and reached an accuracy of RMSE = 1.971-3.897 mm/day.…”

“…Therefore, powerful and consistent estimation methods should be able to analyze nonlinear patterns of evaporation processes. Recently, many artificial intelligence models have been proposed to estimate evaporation, including the multilayer perceptron (MLP) model [3,6,18,[20][21][22][23][24][25], support vector machine (SVM) model [26], M5tree model [27], adaptive neuro-fuzzy inference system (ANFIS) model [28], random forests (RF) model [6], relevance vector machine (RVM) model [29]. Additionally, various hybrid artificial intelligence-based models have been employed in evaporation simulation [30,31].…”

Assessment of Algorithm Performance on Predicting Total Dissolved Solids Using Artificial Neural Network and Multiple Linear Regression for the Groundwater Data

“…The ANN is a nonlinear model designed to deal with large datasets with multiple variables as input [18][19][20]. Multi-layer perceptron (MLP) is the widely used ANN architectural design that has been used abundantly in hydrological modeling to predict and forecast the dataset [21][22][23][24][25][26]. The current investigation used MLP architecture to predict the TDS values of the groundwater sources.…”