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

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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.

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Data availability

The gauged rainfall data used in this study will be available upon request from the corresponding author.

References

  • Aboniyo J et al (2017) 2017 National water resources management authority for a sustainable water use in Rwanda. Sustainable Resour Manage J 2(3):01–15

    Google Scholar 

  • Altunkaynak A (2019) Predicting water level fluctuations in lake van using hybrid season-neuro approach. J Hydrol Eng 24:4019021

    Google Scholar 

  • Altunkaynak A, Kartal E (2019) Performance comparison of continuous wavelet-fuzzy and discrete wavelet-fuzzy models for water level predictions at northern and southern boundary of Bosphorus. Ocean Eng 186:106097

    Google Scholar 

  • Altunkaynak A (2010) A predictive model for well loss using fuzzy logic approach. Hydrol Process 24(17):2400–2404

    ADS  Google Scholar 

  • Altunkaynak A, Nigussie TA (2015) Prediction of daily rainfall by a hybrid wavelet-season-neuro technique. J Hydrol 529:287–301

    Google Scholar 

  • Altunkaynak A, Şen Z (2007) Fuzzy logic model of lake water level fluctuations in Lake Van. Turkey Theore Appl Climatol 90(3-4):227–233

    ADS  Google Scholar 

  • Atiah, W. A., Bendito, E. G., & Muthoni, F. K. (2023). Evaluating Seasonal Rainfall Forecast Gridded Models over Sub-Saharan Africa

    Google Scholar 

  • Belohlavek R, Urbanec T (2023) On the concept of fuzzy order I: Remarks and observations. Int J Gen Syst 52(8):948–971

    MathSciNet  Google Scholar 

  • Bibri SE, Krogstie J, Kaboli A, Alahi A (2024) Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review. Environ Sci Ecotechnol 19:100330

    CAS  PubMed  Google Scholar 

  • Bizimana H, Altunkaynak A (2020) Modeling the initiation of sediment motion under a wide range of flow conditions using a Geno-Mamdani Fuzzy Inference System method. Int J Sediment Res 35(5):467–483

    Google Scholar 

  • Bizimana H, Altunkaynak A (2019) A novel approach for the prediction of the incipient motion of sediments under smooth, transitional and rough flow conditions using Geno-Fuzzy Inference System model. J Hydrol 577:123952

    Google Scholar 

  • Bridgman HA, Oliver JE (2014) The global climate system: patterns, processes, and teleconnections. Cambridge University Press

    Google Scholar 

  • Deltares (2021): Inception Report. Stormwater and Wetland Management Model for the delineated flood prone areas in Kigali City

  • Donigian AS, Love JT (2003) Sediment calibration procedures and guidelines for watershed modeling. Proceedings of the Water Environment Federation 2003(4):728–747

  • FONERWA (2023): Final Report. Strategic Integrated Water Resources Planning and Management

  • Gebrechorkos SH, Taye MT, Birhanu B, Solomon D, Demissie T (2023) Future changes in climate and hydroclimate extremes in East Africa. Earth's. Future 11(2):e2022EF003011

    Google Scholar 

  • Gholami A, Bonakdari H, Ebtehaj I, Akhtari AA, 2017. Design of an adaptive neuro-fuzzy computing technique for predicting flow variables in a 90° sharp bend. J. Hydroinformatics jh2017200. https://doi.org/10.2166/hydro.2017.200

  • Gustafson DE, Kessel WC, 1979 Fuzzy clustering with a fuzzy covariance matrix. In 1978 IEEE conference on decision and control including the 17th symposium on adaptive processes (pp. 761- 766). IEEE.

  • Habonimana HV et al (2015) Integrated flood modeling for flood hazard assessment in CoK. Rwanda, GeoTechRwanda

    Google Scholar 

  • Habumugisha V, Mukashyaka MM (2023) Flood Management in Rwanda: An Analysis of Policy Implementation Perspective. African J Climate Change Res Sustain 2(1):117–131

    Google Scholar 

  • Henninger, S. (2013a). Relief, nocturnal cold-air flow and air quality in Kigali, Rwanda. In EGU General Assembly Conference Abstracts (pp. EGU2013-28)

  • Henninger SM (2013b) When air quality becomes deleterious—a case study for, Kigali, Rwanda

  • Huang S, Cai N, Pacheco PP, Narrandes S, Wang Y, Xu W (2018) Applications of support vector machine (SVM) learning in cancer genomics. Cancer Geno Proteom 15(1):41–51

    CAS  Google Scholar 

  • Jakkula V (2006) Tutorial on support vector machine (svm). Washington State University, School of EECS, p 37

    Google Scholar 

  • Icyimpaye G, Abdelbaki C (2022) GIS-Based Hydrological and Hydraulic Models to Forecast River Flood Risks and Proposition of Management Measures. In: Spatial Modelling of Flood Risk and Flood Hazards: Societal Implications. Springer International Publishing, Cham, pp 143–159

    Google Scholar 

  • IFRC, “Dref Operation- Rwanda, Floods and Landslides,” 2023

    Google Scholar 

  • Iradukunda P, Mwanaumo EM, Kabika J (2023). Hydroclimatic trend analysis and projection in Africa tropical urban regions: Cases of Lusaka, Zambia and Kigali, Rwanda Urban Climate, 52, 101680

  • Jang JS (1992) Neuro-fuzzy modeling: architectures, analyses, and applications. University of California, Berkeley

    Google Scholar 

  • Jang JS (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst, man, Cybern 23(3):665–685

    Google Scholar 

  • Jonah K, Wen W, Shahid S, Ali M A, Bilal M, Habtemicheal BA, ... & Tiwari P. (2021)Spatiotemporal variability of rainfall trends and influencing factors in Rwanda. J Atmospheric Solar-Terrestrial Phy, 219, 105631

  • Jovanovic BB, Reljin IS, Reljin BD (2004) Modified ANFIS architecture-improving efficiency of ANFIS technique. In: In 7th Seminar on Neural Network Applications in Electrical Engineering, 2004. NEUREL 2004. IEEE, pp 215–220

    Google Scholar 

  • Karamage F, Zhang C, Fang X, Liu T, Ndayisaba F, Nahayo L et al (2017) Modeling rainfall-runoff response to land use and land cover change in Rwanda (1990–2016). Water 9(2):147

    Google Scholar 

  • Karamage F et al (2016) The need for awareness of drinking water loss reduction for sustainable water resource management in Rwanda. J Geosci Environ Prot 4:74–87

    Google Scholar 

  • Lake RW, Shaeri S, Senevirathna STMLD (2023) Review of the limitations and potential empirical improvements of the parametric group method of data handling for rainfall modelling. Environ Sci Pollut Res 30(44):98907–98921

    Google Scholar 

  • Loh J, Wackernagel M (2004) Living Planet Rep:2004

  • Laknath DPC, Josiah NR, Sirisena TAJG (2015) Analysis of Long-term Trends of Climatological Parameters in Kigali, Rwanda

  • Mindje R, Li L, Kayumba PM, Mupenzi C, Mindje M, Hao J (2023) Exploring a form of pixel-based information value model for flood probability assessment and geo-visualization over an East African basin: a case of Nyabarongo in Rwanda. Environ Earth Sci 82(17):402

    ADS  Google Scholar 

  • Ministry of Infrastructure (MININFRA), Rwanda. Forward-looking joint sector report (FLSP) Water and Sanitation sector 2016/2017. Kigali, Rwanda; 2016. Available from: http://www.mininfra.gov.rw, [Accessed on 08/11/ 2017]

  • Moriasi DN, Arnold JG, Van Liew MW, Bingner RL, Harmel RD, Veith TL (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans ASABE 50(3):885–900

    Google Scholar 

  • Moosavi V, Vafakhah M, Shirmohammadi B, Behnia N (2013) A wavelet-ANFIS hybrid model for groundwater level forecasting for different prediction periods. Water Resour Manag 27(5):1301–1321

    Google Scholar 

  • Mugiraneza T, Ban Y, Haas J (2019) Urban land cover dynamics and their impact on ecosystem services in Kigali, Rwanda using multi-temporal Landsat data. Remote Sens Appli: Soc Environ 13:234–246

    Google Scholar 

  • Munyaneza O, Nzeyimana YK, Wali UG (2013) Hydraulic structures design for flood control in the Nyabugogo wetland, Rwanda

  • Nafi A, Brans J (2019) Cost–benefit prediction of asset management actions on water distribution networks. Water 11(8):1542

    Google Scholar 

  • Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I—A discussion of principles. J Hydrol 10(3):282–290

    Google Scholar 

  • National Policy for Water Resources Management (NPWRM), Rwanda, 2011

  • National Water Resources Masterplan (NWRMP), Rwanda, 2013

  • Nazarenko LS, Tausnev N, Russell GL, Rind D, Miller RL, Schmidt GA et al (2022) Future climate change under SSP emission scenarios with GISS-E2. 1. J Adv Model Earth Syst 14(7):e2021MS002871

    ADS  Google Scholar 

  • Nduwayezu G, Manirakiza V, Mugabe L, Malonza JM (2021) Urban Growth and Land Use/Land Cover Changes in the Post-Genocide Period, Kigali, Rwanda. Environ Urban ASIA 12(1_suppl):S127–S146

    Google Scholar 

  • Niyonsenga D (2012). Assessing public transport supply for Kigali, Rwanda (Master's thesis, University of Twente)

  • Ntirenganya F, 2018 Analysis of Rainfall Variability in Rwanda for Smallscale farmers Coping Strategies to Climate Variability. East African Journal of Science and Technology, Vol.8 Issue1, (P.75-96)

  • Özger M (2009) Comparison of fuzzy inference systems for streamflow prediction. Hydrol Sci J 54(2):261–273

    Google Scholar 

  • Rema (2010). Rwanda Environmental Education for Sustainable Development Strategy: A Strategy and Action Plan for 2010–2015. Kigali, Rwanda; Rema 2011. Guidelines for Mainstreaming Climate Change Adaptation and Mitigation in the Environment and Natural Resources Sectors. Kigali

  • Rukundo E, Doğan A (2016) Assessment of Climate and Land Use Change Projections and their Impacts on Flooding. Pol J Environ Stud 25(6)

  • Rwanda Natural Resources Authority. (2013). National Water Resources Masterplan

  • Saidi L, Ali JB, Fnaiech F (2015) Application of higher order spectral features and support vector machines for bearing faults classification. ISA Trans 54:193–206

    PubMed  Google Scholar 

  • Schwing FB, Murphree T, Dewitt L, Green PM (2002) The evolution of oceanic and atmospheric anomalies in the northeast Pacific during the El Niño and La Niña events of 1995–2001. Prog Oceanogr 54(1-4):459–491

    ADS  Google Scholar 

  • Sern CC, Nasir AFA, Majeed APA, Zakaria MA, Razman MAM, Azmi A (2020) Comparison of Support Vector Machine and Friis Equation For Identification of Pallet-Level Tagging Using RFID Signal. In: In 2020 IEEE 10th Symposium on Computer Applications & Industrial Electronics (ISCAIE). IEEE, pp 215–219

    Google Scholar 

  • Solomatine DP, Shrestha DL (2009) A novel method to estimate model uncertainty using machine learning techniques. Water Resour Res 45(12)

  • Sugeno M, Kang GT (1988) Structure identification of fuzzy model. Fuzzy Sets Syst 28(1):15–33

    MathSciNet  Google Scholar 

  • Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern SMC-15(1):116–132

  • Theobald B, 2018 Water Demand Management as a Solution to Water Resources Challenges in Rwanda

  • Theobald B et al (2018) Water Demand Management as a Solution to Water Resources Challenges in Rwanda. Ijsrm Human 9(1):51–70

    Google Scholar 

  • Thrasher B, Wang W, Michaelis A, Melton F, Lee T, Nemani R (2022) NASA global daily downscaled projections, CMIP6. Scientific Data 9(1):262

    PubMed  PubMed Central  Google Scholar 

  • Tsinda A, Kind C, Hess JS, Mugisha R, Bizoza AR (2019) Estimating damage costs of flooding on small-and medium-sized enterprises in Kigali, Rwanda. Jàmbá: J Disaster Risk Stud 11(1):1–11

    Google Scholar 

  • Twizeyimana (2023) Field data collection Technical report of 2 days from 05th to 06th May 2023

  • UNDP, ‘Turning Vision 2020 into Reality: From Recovery to Sustainable Human Development. National Human Development Report’, Kigali, 2007

  • USAID (2014) Planning for Resilience in East Africa through Policy. Adaptation, Research, And Economic Development

    Google Scholar 

  • Uwera C (2013) Water Demand and Financing in Rwanda: An Empirical Analysis. University of Gothenburg

    Google Scholar 

Download references

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.

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Authors and Affiliations

  1. Istanbul Technical University, Civil Engineering Department, Hydraulic and Water Resources Engineering Division, 34467 Sariyer, Istanbul, Turkey

    Hussein Bizimana & Abdusselam Altunkaynak

  2. University of Rwanda, College of Science and Technology, Civil, Environmental and Geomatic Engineering, 3900 Nyarugenge, Kigali, Rwanda

    Hussein Bizimana

  3. Knowledge, and Forecasting Hub Department, Rwanda Water Resources Board, 6213 Nyarugenge, Kigali, Rwanda

    Hussein Bizimana

  4. University of Strathclyde, Civil and Environmental Engineering Department, Glasgow, Scotland, UK

    Robert Kalin

  5. Rwanda Water Resources Board, 6213 Nyarugenge, Kigali, Rwanda

    Emmanuel Rukundo

  6. Meteo Rwanda, Kigali, Rwanda

    Mathieu Mbati Mugunga

  7. Sakarya University, Sakarya, Serdivan, Turkey

    Osman Sönmez, Gamze Tuncer & Abdulkadir Baycan

Authors
  1. Hussein Bizimana
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  2. Abdusselam Altunkaynak
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  3. Robert Kalin
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  4. Emmanuel Rukundo
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  5. Mathieu Mbati Mugunga
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  6. Osman Sönmez
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  7. Gamze Tuncer
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  8. Abdulkadir Baycan
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Contributions

All authors appearing in the manuscript contributed to the research conceptualization, data collection interpretation, and design of the methodologies. Modeling-based materials, and model preparation, data review, calibration, and analysis were performed by Hussein Bizimana, Abdusselam Altunkaynak, and Mathieu Mbati Mugunga. The first draft of the manuscript was written by Hussein Bizimana, Robert Kalin, and Emmanuel Rukundo, and the second draft was updated by Osman Sönmez, Gamze Tuncer, and Abdulkadir Baycan who also improved the climate change modeling and interpretation. Furthermore, all authors commented on all versions of the manuscript, and read and approved the final manuscript.

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Correspondence to Hussein Bizimana.

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Communicated by: H. Babaie

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Bizimana, H., Altunkaynak, A., Kalin, R. et al. Assessment of rainfall and climate change patterns via machine learning tools and impact on forecasting in the City of Kigali. Earth Sci Inform 17, 1229–1243 (2024). https://doi.org/10.1007/s12145-024-01231-8

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