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

Evapotranspiration (ET<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf>)—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 ET<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> forecasting is essential for designing efficient irrigation systems that enhance productivity while conserving resources. This study evaluates advanced time-series models for ET<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> forecasting—Nixtla TimeGPT-1, 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.