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

Mind’je, Richard; Li, Lanhai; Kayumba, Patient Mindje; Mupenzi, Christophe; Mindje, Mapendo; Hao, Jinkai

doi:10.1007/s12665-023-11088-7

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Environ Earth Sci

2023

DOI: 10.1007/s12665-023-11088-7

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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

Richard Mind’je

Lanhai Li

Patient Mindje Kayumba

et al.

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2024

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Cited by 3 publications

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“…In addition, our study extends beyond susceptibility mapping, integrating ecosystem-based solutions and community engagement for effective resilience planning. Complementing prior works byMind'je et al (2019Mind'je et al ( , 2023 andHirwa et al (2023), our research provides practical insights tailored to the unique vulnerabilities of Ngororero District. By contextualizing findings with identified objectives, the study establishes a robust foundation for evidencebased decision-making, contributing to proactive flood risk reduction and community resilience enhancement in Rwanda.In conclusion, the study on flood vulnerability in Ngororero District revealed crucial insights into the region's vulnerability to flooding and provided evidence-based recommendations for enhancing resilience.…”

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confidence: 78%

Assessing Flood Vulnerability Zones and Their Driving Factors to Guide Community-Based Resilience Planning Across Ngororero District, Rwanda

2024

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Floods are recognized as devastating and deleterious natural disasters which are contemporary substantial threats to communities, exerting profound impacts on human lives, infrastructure, and the environment. Hence, the aims of this research were to assess and map the flood vulnerability zones, determine the primary local factors driving flood vulnerability, and evaluate the existing flood mitigation measures and propose evidence-based recommendations for enhancing community-based flood resilience. Using an integrated approach, this study combined remote sensing, Geographic Information Systems (GIS) techniques and expert knowledge, to map and model flood vulnerability zones, determining the risk levels using the AHP model considering a set of driving independent variables including elevation, slope, rainfall, land use/land cover (LULC), soil type, distance to streams, distance to roads, flow accumulation, and topographic wetness index (TWI). The results of the study for the first objective showed that 66.6% of the area classified as very high and high vulnerability zones, mainly in the northern and north-western regions. Moderately vulnerable areas covered 18.1%, predominantly in the southern and central parts, while low and very low vulnerability zones comprised 15.3%, mainly in the central and north-eastern regions. This highlights the heterogeneous distribution of vulnerability across the district, emphasizing the need for tailored mitigation strategies. The results of the second objective revealed rainfall (26.7%), proximity to rivers (21.4%), flow accumulation (13.3%), and LULC (12.5%) as the most influencing factors to flood vulnerability in the study area. In addition, the soil texture (9.4%) and elevation (8.2%) exhibited moderate influence, while TWI (4.1%), proximity to roads (2.8%), and slope (1.6%) disclosed a low influence. Understanding these factors enables prioritization of mitigation efforts, focusing on addressing rainfall patterns and proximity to rivers. The third objective suggested sustainable land management practices such as afforestation, agroforestry, terracing, and engineering strategies such as dam construction, resilient drainage systems in addition to strengthening

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confidence: 78%

Assessing Flood Vulnerability Zones and Their Driving Factors to Guide Community-Based Resilience Planning Across Ngororero District, Rwanda

2024

JOA

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

Bizimana,

Altunkaynak,

Kalin

et al. 2024

Earth Sci Inform

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Suitability Analysis for Resettlement Potential Sites of Flood Vulnerable Community in Kigali city, Rwanda

Izere,

Li,

Mind’je

et al. 2024

Earth Syst Environ

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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

Cited by 3 publications

References 108 publications

Assessing Flood Vulnerability Zones and Their Driving Factors to Guide Community-Based Resilience Planning Across Ngororero District, Rwanda

Assessing Flood Vulnerability Zones and Their Driving Factors to Guide Community-Based Resilience Planning Across Ngororero District, Rwanda

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

Suitability Analysis for Resettlement Potential Sites of Flood Vulnerable Community in Kigali city, Rwanda

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