Assessment of rainfall and climate change patterns via machine learning tools and impact on forecasting in the City of Kigali

Tools

Bizimana, Hussein and Altunkaynak, Abdusselam and Kalin, Robert and Rukundo, Emmanuel and Mugunga, Mathieu Mbati and Sönmez, Osman and Tuncer, Gamze and Baycan, Abdulkadir (2024) Assessment of rainfall and climate change patterns via machine learning tools and impact on forecasting in the City of Kigali. Earth Science Informatics, 17 (2). pp. 1229-1243. ISSN 1865-0481 (https://doi.org/10.1007/s12145-024-01231-8)

[thumbnail of Bizimana-etal-ESI-2024-Assessment-of-rainfall-and-climate-change-patterns-via-machine-learning-tools-and-impact-on-forecasting]
Preview
 Text. Filename: Bizimana-etal-ESI-2024-Assessment-of-rainfall-and-climate-change-patterns-via-machine-learning-tools-and-impact-on-forecasting.pdf
Preprint
License: Creative Commons Attribution 4.0 logo
Download (1MB)| Preview

Abstract

Rainfall is changing in intensity and abundance for much of the world as a result of global climate change. Rwanda has been negatively affected by a changing climate, exacerbated by human impact on land and water resources. In most parts of the country, the rainfall pattern has changed over the last decades resulting in both enhanced flooding and water shortage/scarcity in much of the country, especially in the Capital City of Kigali and peripheries which is the main economic hub of the country with strong links to the East African region. Changes in precipitation have affected agricultural production, hydropower production, and water supplies, and has been a result of increased flash floods in the city. This study developed a new predictive model of rainfall patterns in the City of Kigali (CoK) in the Republic of Rwanda using evolutionary methodologies that apply machine learning techniques of Fuzzy Inference Systems (FIS) trained via Genetic Algorithms, Neuro Network Systems and a comparative Support Vector Machine tool, and assessment downscaled climate change combinations with predicted rainfall patterns. The models were calibrated and validated using measured rainfall data in the City of Kigali from 1991 through 2023. The model results show the developed Geno Fuzzy Inference System (GENOFIS) model performed better than the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM) models. The Coefficient of Efficiency (CE), and Root Mean Square Error (RMSE) were used as diagnostic measures for model performance evaluation. Models generated with GENOFIS are therefore recommended for rainfall and related prediction patterns in the City of Kigali for climate change adaptation and resilience policy and planning.

ORCID iDs

Bizimana, Hussein, Altunkaynak, Abdusselam, Kalin, Robert ORCID logoORCID: https://orcid.org/0000-0003-3768-3848, Rukundo, Emmanuel, Mugunga, Mathieu Mbati, Sönmez, Osman, Tuncer, Gamze and Baycan, Abdulkadir;
CORE (COnnecting REpositories)