Evaluation of Time-Series Models for Evapotranspiration Prediction in Smart Agriculture

Abstract

—Evapotranspiration (ET0)—the sum of evaporation and plant transpiration—is a key variable for optimizing water use in precision agriculture. With increasing challenges due to climate change and water scarcity, accurate ET0 forecasting is essential for designing efficient irrigation systems that enhance productivity while conserving resources. This study evaluates advanced time-series models for ET0 forecasting—Nixtla TimeGPT1, Long Short-Term Memory Networks (LSTM), and Kolmogorov–Arnold Networks (KAN)—using IoT data from Campo de Cartagena (Murcia, Spain). Results show that KAN achieves superior performance for multi-step forecasting (MSE: 0.045), while Nixtla Linear excels in one-step predictions (MSE: 0.009). These findings provide practical insights into model selection for adaptive irrigation strategies under diverse climatic conditions.