Deep Learning Applications in Forecasting Agricultural Water Demand Under Climate Variability

Abstract

Agriculture accounts for the largest share of global freshwater consumption, making accurate forecasting of agricultural water demand a critical component of sustainable resource management. Climate variability—characterized by fluctuations in rainfall patterns, temperature, and evapotranspiration—poses significant challenges to conventional forecasting models. Recent advances in deep learning provide powerful tools to address these challenges by capturing complex, non-linear relationships between climate variables and crop water requirements. This study investigates the application of deep learning models, including Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNN), and hybrid architectures, to forecast agricultural water demand under varying climatic conditions. A dataset comprising historical weather records, soil moisture indices, and irrigation data was used to train and validate the models. The results demonstrate that LSTM-based models outperform traditional statistical methods and machine learning approaches in terms of predictive accuracy, particularly in capturing seasonal variability and extreme events. This work highlights the potential of deep learning in supporting data-driven irrigation planning, improving resilience to climate change, and ensuring efficient water resource management in agriculture.