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

Hussein Bizimana*, Abdusselam Altunkaynak, Robert Kalin, Emmanuel Rukundo, Mathieu Mbati Mugunga, Osman Sönmez, Gamze Tuncer, Abdulkadir Baycan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)1229-1243
Number of pages15
JournalEarth Science Informatics
Volume17
Issue number2
DOIs
Publication statusPublished - Apr 2024

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

Funding

This research has received funding from the Islamic Development Bank under the postdoctoral fellowship number: IsDB Scholarship: 2020-279583 taken at Istanbul Technical University, Istanbul, Turkey under the supervision of Professor Abdusselam Altunkaynak.

FundersFunder number
Islamic Development Bank

    Keywords

    • Climate change, Resilience
    • Fuzzy Systems (FS)
    • Machine Learning
    • Precipitation
    • Support Vector Machine

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