TOWARDS BUILDING AN INTELLIGENT MOBILE APP FOR RAINFALL PREDICTION USING WEIGHTED AVERAGE ENSEMBLE DEEP SUPERVISED LEARNING

Abstract

Predicting rainfall accurately and on time is essential for a variety of tasks, including electricity generation, building, flood warnings, aviation operations, and the sustainable use of water resources. Traditionally, rainfall forecasts were based on physical models of the atmosphere, which are imprecise over extended periods of time due to their instability to perturbations and thus, are inaccurate for large periods. Artificial Intelligence (AI) techniques as a means of predicting rainfall have been applied in most developed countries, these have been found to offer many possibilities. AI techniques are more robust to perturbations. Over the past 20 years, rainfall forecasting using AI approaches has demonstrated astounding precision. Rainfall forecasting accuracy could be increased by AI algorithms that use historical weather data elements to reveal hidden trends. This study thereafter proposed the development of an AI mobile app for predicting rainfall that is based on weighted average ensemble deep supervised learning that combines three machine learning algorithms namely; Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Gated Recurrent Unit (GRU). . In order to prevent future outbreaks of rainfall hazards, the proposed AI mobile app for rainfall prediction will aid in raising awareness among the public, government officials, planning agencies, disaster management agencies, and related organizations.