Precipitation Prediction in Suqian City, Jiangsu Province of China Using LSTM Based on Grey Wolf Optimizer Algorithm

Abstract

To enhance urban planning and disaster management in the context of global climate change, we focused on precipitation forecasting in Suqian City, Jiangsu Province, and established a Long Short-Term Memory (LSTM) model optimized by the Grey Wolf Optimizer (GWO) algorithm. Firstly, we collected five years of precipitation data to ensure a solid foundation for model training. Secondly, we integrated the GWO algorithm to optimize the LSTM model parameters, thereby improving prediction accuracy. Through data preprocessing, model establishment, and validation, we successfully constructed a precipitation forecasting model tailored for both rainy and non-rainy seasons in Suqian. Lastly, utilizing this model, we predicted the precipitation volume for the upcoming 12 months in Suqian and proposed targeted countermeasures and suggestions based on the forecast results to guide urban planning and disaster management efforts, ensuring that Suqian can effectively address the challenges posed by future precipitation changes. Additionally, by combining actual conditions with predictive data, we refined and optimized Suqian's precipitation management strategies.