Development of Monthly Reference Evapotranspiration Machine Learning Models and Mapping of Pakistan—A Comparative Study

Water

Ahmed Elbeltagi¹  Muhammad Shoaib²  Ram L. Ray³  Kouadri Saber⁴  Ali Raza⁵  Jizhang Wang⁵  Yongguang Hu⁵  Noman Ali Buttar⁵  Pingping Li⁶ 

[1] Agricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura 35516, Egypt;Department of Agricultural Engineering, Bahauddin Zakariya University, Multan 60000, Pakistan;Department of Agriculture, Nutrition and Human Ecology, College of Agriculture and Human Sciences, Prairie View A&M University, Prairie View, TX 77446, USA;Laboratory of Water and Environment Engineering in Saharan Environment, Department of Civil and Hydraulic Engineering, Faculty of Applied Sciences, University of Kasdi Merbah-Ouargla, PB 147 RP, Ouargla 30000, Algeria;School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China;School of Biology and the Environment, Nanjing Forestry University, Nanjing 210037, China;

关键词: reference evapotranspiration;    machine learning models;    neural networks-based models;    tree-based models;    multifunction-based models;    climatic regions;   

DOI  :  10.3390/w14101666

来源: DOAJ

【 摘 要 】

Accurate estimation of reference evapotranspiration (ET_o) plays a vital role in irrigation and water resource planning. The Penman–Monteith method recommended by the Food and Agriculture Organization (FAO PM56) is widely used and considered a standard to calculate ET_o. However, FAO PM56 cannot be used with limited meteorological variables, so it is compulsory to choose an alternative model for ET_o estimation, which requires fewer variables. This study built ten machine learning (ML) models based on multi-function, neural network, and tree-based structure against the FAO PM56 method. For this purpose, time series temperature data on a monthly scale are only used to train ML models. The developed ML models were applied to estimate ET_o at different test stations and the obtained results were compared with the FAO PM56 method to verify and validate their performance in ET_o estimation for the selected stations. In addition, multiple statistical indicators, including root-mean-square error (RMSE), coefficient of determination (R²), mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE), and correlation coefficient (r) were calculated to compare the performance of each ML model on ET_o estimation. Among the applied ML models, the ET_o tree boost (TB) ML model outperformed the other ML models in estimating ET_o in diverse climatic conditions based on statistical indicators (R², NSE, r, RMSE, and MAE). Moreover, the observed R², NSE, and r were the highest for the TB ML model, while RMSE and MAE were found to be the lowest at the study sites compared to other applied ML models. Lastly, ET_o point data yielded from the TB ML model was used in an interpolation process to create monthly and annual ET_o maps. Based on the ET_o maps, this study suggests mainly a focus on areas with high ET_o values and proper irrigation scheduling of crops to ensure water sustainability.

【 授权许可】

Unknown