Enhancing irrigation water management based on ETo prediction using machine learning to mitigate climate change

Abstract

AbstractThis study addressed the increasing challenges of climate change by exploring the use of machine learning (ML) algorithms to predict the reference evapotranspiration (ETo). Accurate ETo prediction is crucial for optimizing irrigation water management. This research aimed to assess the reliability and accuracy of ML algorithms in predicting ETo values. Three ETo calculation methods were employed: Penman-Monteith (PM), Hargreaves (HA), and Blaney-Criddle (BC). The study analyzed ETo and other climate variables using the modified Mann-Kendall test (m-MK) and Theil Sen’s slope estimator methods to identify trends. Multiple ML algorithms, including Support Vector Regression (SVR), Random Forest (RF), XGboost, K-Nearest Neighbor (KNN), Decision Trees (DT), Linear Regression (LR), and Multiple Linear Regression (MLR) were utilized for ETo prediction. The ML algorithms exhibited excellent performance, with coefficients of determination (R2) values ranging from 0.97 to 0.99 for PM, 0.99 for HA, and from 0.91 to 0.92 for BC. The models demonstrated a high value of the Kling-Gupta efficiency (KGE) with low Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values. Strong correlations between the predicted and calculated daily ETo were observed with R2 values of 0.99, 0.99, and 0.92 for PM, HA, and BC methods, respectively. In conclusion, this study affirmed the accuracy and reliability of ML algorithms to match that of standard ETo prediction equations.

Keywords

• Climate change
• reference evapotranspiration
• machine learning algorithms
• modified Mann-Kendall test
• Kling-Gupta efficiency
• Manuel Tejada, Universidad de Sevilla, Spain