Towards Improved Rainfall Forecast Within the Ziz Basin Area: A Focused Exploration of Machine Learning Application

Communities and industries rely heavily on accurate precipitation forecasts, which are essential for planning and decision-making across various sectors. Conventional methodologies for predicting weather entail a comprehensive understanding of physical phenomena, historical climate patterns, and the application of statistical models. Nevertheless, the intricate and nonlinear rainfall nature poses significant challenges to achieving precise predictions. In recent years, Machine Learning (ML) techniques have appeared as advantageous methods for enhancing the accuracy of rainfall forecasts. This article offers an extensive and comparative examination of classic techniques and evolved ML models relevant to precipitation estimation. By identifying the strengths and limitations of each approach, the study sheds light on the progress made in rainfall prediction using these innovative techniques. In addition, the study emphasizes the importance of pre-processing in improving predictive capabilities, exploring careful steps such as data cleaning, handling missing values, and scaling. Leveraging a comprehensive dataset collected from the Ziz Basin region, incorporating historical weather data spanning numerous climatic variables (precipitation, humidity, wind, temperature, and evaporation), our objective is to conduct a rigorous comparative analysis to assess the effectiveness of various ML models in enhancing rainfall estimation accuracy within this specific geographic area.