A novel approach for predicting net irrigated area in India using hybrid deep learning architectures

Studying irrigation systems is crucial to ensuring efficient freshwater utilization and conservation. This study examines the efficacy of forecasting the net irrigated area for future generations to create a model of prediction that can efficiently exchange water demand. To improve the forecast, we generate a model using two-hybrid deep learning techniques to predict irrigation demands: Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) and Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU). These models effectively capture complex variables from diverse data sources, including rainfall patterns, irrigated area statistics and various irrigation system parameters. The main ideas, noteworthy contributions and crucial quantitative results from our study on net irrigated area projection are outlined in this publication. Our main contribution is the development of unique hybrid deep learning approaches that effectively integrate the CNN-LSTM and CNN-GRU architectures. Better predictions are made possible by the models’ design, which consists of parallel CNN layers that independently interpret certain input features. Thorough examinations of these situations validated the models’ effectiveness and led to notable decreases in important evaluation parameters, such as the RMSE, MSE, MAE and R2. Regarding excellent accuracy in predicting and overall performance, our CNN-GRU hybrid deep learning model outperformed the other models in the present research.