Application of Machine Learning Algorithms in Predicting Extreme Rainfall Events in Rwanda

Abstract

Precipitation is an essential component of the hydrological cycle that directly affects human lives. An accurate and early detection of a future rainfall event can help prevent social, environmental, and economic losses. Traditional methods for accurate rainfall prediction have faltered due to their weakness in quantifying nonlinear climatic conditions as they involve numerical weather prediction using radar to solve complex mathematical equations based on contemporary meteorological data. This study aims to develop a precise rainfall forecast model using machine learning (ML), and this model focuses on long short-term memory (LSTM) to enhance rainfall prediction accuracy. In recent years, machine learning (ML) algorithms have emerged as powerful tools for predicting extreme weather phenomena worldwide. For instance, long short-term memory (LSTM) is a forecast model that effectively estimates the amount of precipitation based on historical data. We analyzed 85,470 pieces of daily rainfall data from 1983 to 2021 collected from each of four synoptic stations in Rwanda (Kigali Aero, Ruhengeri Aero, Kamembe Aero, and Gisenyi Aero). Advanced ML algorithms, including convolutional neural networks (CNNs), gated recurrent units (GRUs), and LSTM, were applied to predict extreme rainfall events. LSTM outperforms the CNN and GRU with 99.7%, 99.8%, and 99.7% accuracy. LSTM’s ability to filter out noise showed important patterns by handling irregularities in rainfall data to improve forecast results. Our outcomes have significant implications for disaster preparedness and risk mitigation efforts in Rwanda, where frequent natural disasters, including floods, pose a challenge. Our research also demonstrates the superiority of LSTM-based ML algorithms in predicting extreme rainfall events, highlighting their potential to enhance disaster risk resilience and preparedness strategies in Rwanda.