Document Type : Original Article
Authors
1
Associate Professor of Water Resources Engineering, Department of Water Engineering, Faculty of Agriculture, Tabriz University, Tabriz, Iran
2
Department of Water Engineering, Faculty of Agriculture, Tabriz University, Tabriz, Iran
3
Department of Computer Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran,
4
Department of Water Engineering, Faculty of Agriculture, Tabriz University, Tabriz, Iran.
10.30488/ccr.2024.433394.1192
Abstract
Precipitation is one of the most important components of hydrology and meteorology, and the prediction of its values are important in various fields, such as agriculture and the environment. Considering that the occurrence of precipitation and its amount depend on many factors, the modeling of precipitation has many complications. In this research, the meteorological data of Tabriz synoptic station including minimum, maximum and average temperature, relative humidity, air pressure, maximum wind speed and precipitation in the period of 1986-2020 were used. The machine learning and ensemble learning methods including Multi Layer Perceptron (MLP), Random Forest (RF), Ada Boost (AB), Gradient Boost (GB), Extra Trees (ET) and Decision Tree Regression (DTR) models were used for rainfall modeling. 70% of the data was used for training and 30% for testing the models. The statistical criteria of Coefficient of Correlation (R), Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Kling-Gupta Efficiency (KGE) were used to evaluate the models. According to the results, on daily scale the MLP model with R=0.993, RMSE=0.184, MAE=0.184 and KGE=0.82, and the ET model with R=0.986, RMSE=0.324, MAE=0.324 and KGE=0.75, respectively, and on monthly scale the MLP model with R=0.999, RMSE=0.153, MAE=0.222 KGE=0.88, and the ET model with R=0.981, RMSE=0.266, MAE=0.197 and KGE=0.71, respectively, have the highest accuracy. Overall, the results show that machine learning and ensemble learning models perform well in predicting daily and monthly rainfall.
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