Exploring Use of Machine Learning Regressors for Daily Rainfall Prediction in the Sahel Region: A Case Study of Matam, Senegal

Publication Type

Book Chapter

Publication Date (Issue Year)

2023

Journal Name

Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, 459 LNICST. In Pan-African Artificial Intelligence and Smart Systems: Second EAI International Conference, PAAISS 2022, Dakar, Senegal, November 2-4, 2022. Cham: Springer Nature Switzerland.

Abstract

Rainfall is the major source of water for rain-fed agricultural production in Sub-Saharan Africa. Overdependency on rain-fed agriculture renders Sub-Saharan Africa more prone to adverse climate change effects. Consequently, timely and correct long-term daily rainfall forecasting is fundamental for planning and management of rainwater to ensure maximum production. In this study, we explored use of regressors: Gradient Boosting, CatBoost, Random Forest and Ridge Regression to forecast daily rainfall for Matam in the northern geographical part of Senegal. Gradient Boosting model is therefore considered a better model with smaller values of Mean Absolute Error, Mean Squared Error and Root Mean Squared Error of 0.1873, 0.1369 and 0.3671 respectively. Further, Gradient Boosting model produced a higher score of 0.69 for Coefficient of Determination. Relative Humidity is perceived to highly influence rainfall prediction.

Keywords

Machine Learning Regressors, Daily Rainfall Prediction, Sahel Region, Matam, Senegal

Rsif Scholar Name

Chimango Nyasulu

Rsif Scholar Nationality

Malawi

Cohort

Cohort 2

Thematic Area

ICTs Including Big Data and Artificial Intelligence

Africa Host University (AHU)

University of Gaston Berger (UGB), Senegal

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