Optimizing Water Resource Utilization in agriculture Using Temporal Residual Convolutional Networks of Weather parameters - Suriya, U.; Yesudoss, J (Journal of Neonatal Surgery, 2025)
Accurate weather prediction is crucial for optimizing water irrigation systems, ensuring efficient resource utilization, and improving agricultural productivity. This research explores various deep learning algorithms, including Convolutional Neural Networks (CNN), Temporal Convolutional Networks (TCN), TabNet, Fully Convolutional Neural Networks (FCNN), and a novel Temporal Residual Convolutional Network (TRCN), to enhance predictive accuracy in irrigation forecasting. The data includes key weather variables such as temperature, humidity, rainfall, and wind speed, which were pre-processed to handle missing values and normalize features for optimal model performance. Experimental results revealed that traditional FCNN models performed suboptimally, while CNN, TabNet, and TCN demonstrated significant improvements in accuracy and F1-score. The proposed TRCN model outperformed all other models, achieving an accuracy of 0.99 and an F1-score of 0.97. These findings highlight the effectiveness of deep learning models in weather-based irrigation prediction, with TRCN offering superior predictive capabilities. This research advances precision agriculture by integrating deep learning techniques to enhance irrigation management and promote water conservation.