Effective Rainfall Prediction LSTM Model for Enhancing Textile Industry Sustainability

Abstract

This study presents an innovative deep learning model designed to accurately predict rainfall patterns, aimed at enhancing the sustainability of the textile industry. By leveraging advanced machine learning techniques, the model analyzes historical weather data, climatic factors, and seasonal trends to provide reliable rainfall forecasts. This research article focuses on predicting rainfall frequency using deep learning and suggests measures that can be taken by the Pakistan cities Faisalabad, Multan and Tando Adam textile industries to minimize the associated negative effects. Rainfall prediction is important for decision-making among stakeholders who are affected by wet weather conditions. These predictions enable textile manufacturers to optimize water usage, reduce waste, and plan production schedules more effectively. After selecting relevant key performance indicators and using this data to train weighted and stateful LSTM and CNN models, a validation accuracy of between 89% and 100%, precision of 83%, and a recall of 86% was predicted for various classes, which almost matches the ground truth data of two years of weather data. The results showed that LSTMs and CNNs can provide high performance in real-time rainfall prediction applications.  The integration of this predictive model not only fosters resource efficiency but also supports the industry's transition towards sustainable practices in the face of climate variability. The findings of Random Forest, XGB Regression, and Decision Boosted Tree machine learning models underscore in addressing environmental challenges within the textile sector.