Enhancing Precipitation Prediction in the Ziz Basin: A Comprehensive Review of Traditional and Machine Learning Approaches

Abstract

Accurate precipitation forecasting is paramount for various sectors. Traditional methods for rainfall prediction involve understanding physical processes, historical weather data, and statistical models. These methods utilize observations from ground-based weather stations, satellites, and weather radars to assess current conditions and predict future precipitation. However, accurate precipitation prediction remains challenging due to the intricate and non-linear characteristics of rainfall. Over the past few years, machine learning (ML) algorithms have shown promise in improving precipitation prediction accuracy. This research provides an overview of both traditional methods and advanced ML models applicable to rainfall prediction, including regression, classification, and time series models. We conducted a comprehensive review of related works that explore the impact of using ML algorithms for rainfall estimation. Through this analysis, we identified the strengths and limitations of ML models in this context and highlighted advancements in rainfall prediction using these algorithms. We possess a comprehensive dataset, spanning data from 1996 to 2015, comprising historical weather data from the Ziz basin, our designated study area. This dataset contains five key meteorological features: precipitation, humidity, wind, temperature, and evaporation. In terms of perspective, we plan to utilize this dataset and conduct a comprehensive comparative study to evaluate the performance of different ML models. Our objective is to demonstrate the effectiveness and potential of these algorithms in improving weather forecasting capabilities and enhancing the accuracy of rainfall estimation methods in the specific study area.