Flood risk mapping and urban infrastructural susceptibility assessment using a GIS and analytic hierarchical raster fusion approach in the Ona River Basin, Nigeria

Nkeki, Felix Ndidi; Innocent, Bello Ehiaguina; Ganiy, Agbaje Ishola

doi:10.1016/j.ijdrr.2022.103097

Cited by 21 publications

(14 citation statements)

References 23 publications

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“…The permeability of the soil in an area to a large extent determines the infiltration rate. Different soil types manifest different magnitudes of permeability [5]. Furthermore, slope affects the amount of surface runoff and infiltration since water flows from higher to lower altitudes.…”

Section: Literature Reviewmentioning

confidence: 99%

GIS-Based flood susceptibility mapping in Wawar Watershed, Purworejo Regency

Damayanti,

Wikan,

Annurhutami

2024

IOP Conf. Ser.: Earth Environ. Sci.

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Purworejo is one of the regency in Central Java which vulnerable to being affected by flood in rainy season. Wawar watershed is one of the watersheds in Purworejo Regency, the land use in Wawar watershed is dominated by agricultural land so the potential for flooding is quite high in the downstream area. This study aims to determine the distribution of flood-prone locations and classify the level of flood susceptibility that occurs in Wawar Watershed. The method used in this study is data processing on GIS (Geographic Information Systems) using scoring method and weighting of each parameter. These parameters include land use, altitude, rainfall, slope, soil type, and river flow buffers. The resulting hazard map has 3 categories, low, moderate, and high vulnerable level. The results also show that highly vulnerable areas in the southern part of Wawar Watershed that dominated by lowlands area, flat slope, settlement and ricefield land use.

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Section: Literature Reviewmentioning

confidence: 99%

GIS-Based flood susceptibility mapping in Wawar Watershed, Purworejo Regency

Damayanti,

Wikan,

Annurhutami

2024

IOP Conf. Ser.: Earth Environ. Sci.

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“…Flood problems have occured in many countries, especially in fast-growing countries. It happens in several country like in Asia aspects (Ishiwatari & Sasaki, 2021;Venkatappa et al, 2021;Yang et al, 2021), Western Europe (Figueiredo et al, 2020), Africa (Nkeki et al, 2022), and America (Palacio-Aponte et al, 2022) that are growing economically strong but ignore environmental sustainability, Indonesia, one of Asian country, has a similar problem, which is on the north coast of Java (Bott et al, 2020;Irawan et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Flood hazard risk assessment based on multi-criteria spatial analysis GIS as input for spatial planning policies in Tegal Regency, Indonesia

Wahyu¹,

Putri²,

Rahayu³

et al. 2023

Geographica Pannonica

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Recent discussions on flood disasters concern the risk factors and causes between nature and anthropogenic activities. This disaster requires serious handling, which needs to be analysed, especially in areas affected by flooding with the Tegal Regency, Indonesia case study. The weakness of the existing mitigation efforts still needed comprehensive analyses, requiring a multi-criteria assessment based on GIS spatial analysis. The GIS method used is a raster calculator and weighted superimpose by setting several calculation variables from both physical and non-physical aspects to support the multicriteria spatial analysis. The results show that spatially, more than 30% of areas with a high-risk index are located in the downstream or coastal regions of Tegal Regency. However, the index of capacity and resilience in several flood-affected sub-districts is at an index above 0.5, so they have good strength to disasters such as the four sub-districts of Adiwerna, Bumijawa, Bojong, and Kramat. From the analysis results, land use change is the biggest problem that affects the number of the flood event. With this condition, the appropriate mitigation effort for Tegal Regency is strengthening the spatial planning policy and increasing the capacity, especially in disaster governance in a high-risk area. Thus, the vulnerability and hazard factors will be anticipated with high community participation in strengthening the capacity index.

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“…According to Nkeki et al [37], In Nigeria, the overriding issues with flood susceptibility prediction are based on intelligence and the capability to know the heterogeneous ecological effect on the watershed generating the flood, in relation to the prevalent dynamics and spatial disparity between them. Due to these complex factors, diverse flood risk models have been suggested in several literatures [37,38]. Some of the suggested models aline with genetic factors (hydraulic flood models) while others put into consideration hydrographical factors (Integrated flood models).…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Based Prediction of Urban Flood Susceptibility from Selected Rivers in a Tropical Catchment Area

Ekwueme

2022

Civ Eng J

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Unexpected flood due to climate change has caused tremendous damage to both lives and properties, especially in tropical areas. Nigeria Southeastern region has been devastated by flood from extreme weather conditions. Flood mitigation involves accurate forecasting, precise prediction, evaluation, and intervention strategy. This study aims at using machine learning solutions to investigate and predict flood susceptibility from selected rivers in the south-eastern region of Nigeria. The regional hydrogeological data from 1981–2019 was collected and analysed. The remote sensing datasets from the National Aeronautics and Space Administration (NASA), Modern-Era Retrospective Analysis for Research and Applications (MERRA) version 2 & 3 platforms from five selected rivers were processed. With the data output of the hydrology, streamline flows, and exposed geology, the ARIMA model was built and used to forecast the flood. The result shows that the flooding pattern would increase by 15-150% within 2020-2024. The forecast indicated that within five years, the river discharge for Adada, Ajali, Imo, Ivo, and Otanmiri will increase within ranges 200-702 m3s-1, 16-26 m3s-1, 508-1280 m3s-1, 43-68.5 m3s-1, and 22-35.1 m3s-1 respectively. Climate change has impacted severely on flood in the region. This knowledge will help the regional agencies and authorities in adapting to flood innuendoes and assessment of hydrologic extremes. Doi: 10.28991/CEJ-2022-08-09-08 Full Text: PDF

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Flood risk mapping and urban infrastructural susceptibility assessment using a GIS and analytic hierarchical raster fusion approach in the Ona River Basin, Nigeria

Cited by 21 publications

References 23 publications

GIS-Based flood susceptibility mapping in Wawar Watershed, Purworejo Regency

GIS-Based flood susceptibility mapping in Wawar Watershed, Purworejo Regency

Flood hazard risk assessment based on multi-criteria spatial analysis GIS as input for spatial planning policies in Tegal Regency, Indonesia

Machine Learning Based Prediction of Urban Flood Susceptibility from Selected Rivers in a Tropical Catchment Area

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