Scholarlink

<p>Floods have claimed lives and devastated communities and ecosystems. Due to their devastating impact and the heavy financial losses plus fatalities they trigger, floods have increasingly become a major global concern in recent years. This study aims to pinpoint inundated areas and deliver detailed flood risk mapping insights. To enable computation of the Flood Risk Index (FRI), the study identified key flood forecasting parameters including: stream power index, Normalized Difference Vegetation Index, elevation, distance from rivers, land use/land cover, topographic roughness index, slope, topographic wetness index, rainfall intensity, aspect, drainage capacity, soil texture, sediment transport index, flow accumulation, and runoff coefficient all carefully considered. The weighting of each forecasting factor within the Analytic Hierarchy Process (AHP) was established by soliciting expert opinions from relevant public agencies. Subsequently, a flood hazard map was generated by integrating the collected data via AHP methodology. Multicollinearity (MC) analysis was employed to assess the model’s forecasting accuracy. Findings reveal that flood risks are prevalent in high risk zones like Ibeju Lekki, Epe, Eti-Osa, Mainland, Amuwo, and Ajeromi regions within the areas notably marked by low elevation, gentle slopes, high drainage capacity, proximity to rivers, elevated topographic wetness index (TWI), and related factors. Results indicated that the model derived flood vulnerability maps aligned with historical flood events in the study area, thereby validating the methodology’s effectiveness in identifying and mapping flood risk zones. Hence, regular and sustained deployment of flood forecasting, early warning systems, and mitigation measures can be effectively implemented.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Assessing flood risk zones leveraging geospatial techniques and multi-criteria decision analysis (MCDA) in Lagos State, Nigeria

  • 期刊论文

  • 发表日期：

    2026年4月29日

查看全文
  • Olubusola Stephen Ilugbo,
  • Babatunde Adebo,
  • Olayinka Olanike Alada,
  • Boluwatife Precious Ajala,
  • Alaba Olatunde Olaniyi,
  • Adesola Musa Bawallah,
  • Nelson Ayobami Adeyemo

摘要

Floods have claimed lives and devastated communities and ecosystems. Due to their devastating impact and the heavy financial losses plus fatalities they trigger, floods have increasingly become a major global concern in recent years. This study aims to pinpoint inundated areas and deliver detailed flood risk mapping insights. To enable computation of the Flood Risk Index (FRI), the study identified key flood forecasting parameters including: stream power index, Normalized Difference Vegetation Index, elevation, distance from rivers, land use/land cover, topographic roughness index, slope, topographic wetness index, rainfall intensity, aspect, drainage capacity, soil texture, sediment transport index, flow accumulation, and runoff coefficient all carefully considered. The weighting of each forecasting factor within the Analytic Hierarchy Process (AHP) was established by soliciting expert opinions from relevant public agencies. Subsequently, a flood hazard map was generated by integrating the collected data via AHP methodology. Multicollinearity (MC) analysis was employed to assess the model’s forecasting accuracy. Findings reveal that flood risks are prevalent in high risk zones like Ibeju Lekki, Epe, Eti-Osa, Mainland, Amuwo, and Ajeromi regions within the areas notably marked by low elevation, gentle slopes, high drainage capacity, proximity to rivers, elevated topographic wetness index (TWI), and related factors. Results indicated that the model derived flood vulnerability maps aligned with historical flood events in the study area, thereby validating the methodology’s effectiveness in identifying and mapping flood risk zones. Hence, regular and sustained deployment of flood forecasting, early warning systems, and mitigation measures can be effectively implemented.