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Field based index of flood vulnerability (IFV): A new validation technique for flood susceptible models

Geoscience Frontiers 2021年第5期12卷 110-123页

作者： Susanta Mahato Swades Pal Swapan Talukdar Tamal Kanti Saha Parikshit Mandal Department of Geography University of GourBangaMaldaIndia

The flood hazard management is one of the major challenges in the floodplain regions *** the rise in population growth and the spread of infrastructural development,the level of risk has increased over ***,the prediction of flood susceptible area is a key challenge for the adoption of management *** susceptibility modeling is technically a common work,but it is still a very tough job to validate flood susceptible models in a very rigorous and scientific ***,the present work in the Atreyee River Basin of India and Bangladesh was planned to establish artificial neural network(ANN),radial basis function(RBF),random forest(RF)and their ensemble-based flood susceptibility *** flood susceptible models were constructed based on nine flood conditioning *** flood susceptibility models were validated in a conventional way using the receiver operating curve(ROC).To validate the flood-susceptible models,a two dimensional(2D)hydraulic flood simulation model was ***,the index of flood vulnerability model was developed and applied for validating the flood susceptible models,which was a very unique way to validate the predictive *** test and Wilcoxon Signed rank test were employed to compare the generated flood susceptible *** showed that 11.95%-12.99%of the entire basin area(10188.4 km^(2))comes under very high flood-susceptible *** evaluation results have shown that the performance of ensemble flood susceptible models outperforms other standalone machine learning *** flood simulation model and IFV model were also spatially adjusted with the flood susceptibility ***,the present study recommended for the ensemble flood susceptibility prediction and IFV based validation along with conventional ways.

关键词： flood susceptibility flood vulnerability Machine learning Index of flood vulnerability flood simulation model

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Geospatial Analysis of flood vulnerability Levels Based on Physical Characteristics and Resilience Capacity of Peri-Urban Settlements in Nigeria

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Journal of Geoscience and Environment Protection 2022年第8期10卷 267-288页

作者： Omobolaji Oluwamuyiwa Afolabi Victoria Ojone Emelu Elekwachi Wali Maureen Chidinma Orji Lilian Chidinma Bosco-Abiahu Olushola Idowu Tonye Yemi-Jonathan Odinaka Amadi Wali Sunny Oghenefegor Asomaku Institute of Natural Resources Environment and Sustainable Development University of Port Harcourt Port Harcourt Nigeria Centre for Disaster Risk Management and Development Studies University of Port Harcourt Port Harcourt Nigeria Department of Geography University of Nigeria Nsukka Nigeria Development Studies University of Passau Passau Germany Department of Environmental Management Federal University of Technology Owerri Owerri Nigeria Department of Geography and Environmental Management Ignatius Ajuru University of Education Port Harcourt Nigeria

flooding is becoming a yearly reoccurring event in many communities and cities in Nigeria, leading to the destruction of properties and deaths;hence, we must take measures to either prepare for the impact or curb the occurrence. The study identified flood vulnerability levels of communities in Isoko North LGA based on physical environmental domains such as land use, elevation, and proximity to river channel (drainage) using geospatial techniques. Also, attributes that could contribute to the resilience capacity building of the communities were assessed. From the study, 73.93% of the entire area is moderately and highly vulnerable to flood, while among the communities, seventeen (17) are categorized as moderately vulnerable, and four (4) are lowly vulnerable. The overall resilience capacity of the communities indicated can build a substantial capacity towards community resilience (3.02, 0.06). However, there is a need to encourage collaboration with stakeholders to improve mitigation action and enhance various shortcomings toward resilience capacity building.

关键词： Community Resilience flood vulnerability Geospatial Analysis Physical Environment Resilience Capacity

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A Study on Anthropogenic Activities Influencing flood vulnerability in Ala Riverfront Residential Areas of Akure, Nigeria

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Journal of Geographical Research 2020年第4期3卷 45-54页

作者： Owoeye J.O. Abe F.S. Olasemojo R.O. Department of Urban and Regional Planning School of Environmental TechnologyFederal University of TechnologyAkure.PMB 704Akure Ondo StateNigeria

This research investigates anthropogenic activities influencing flood vulnerability in Ala riverfront residential areas of Akure,Nigeria with a view to identifying area susceptible to flood hazard in the river basin and to suggest possible mitigation *** respect to data gathering for the study,265 questionnaires which amounted to 1%of the research population were administered using simple random *** from the survey revealed erection of buildings on riparian land and indiscriminate dumping of wastes into river bodies as factors responsible for the blockage of river channels and *** also exposed resident’s poor dispositions to flood forecasts and non-adherence to other management *** study concludes by recommending public enlightenment campaign to reducing denizen’s vulnerability to flood *** is the need for provision of waste management and drainage facilities as well as regular dredging of the watercourse to stimulate its absorptive capacity in the event of heavy *** further suggests creation of artificial lake as natural basin to collect huge volume of water discharged from all watersheds leading to the ***,stringent efforts of environmental and town planning officers are needed to enforce total compliance to all intended management regulations to check the menace.

关键词： Anthropogenic activities flood vulnerability Riparian land Ala riverfront Akure

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flood susceptibility modelling using advanced ensemble machine learning models

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Geoscience Frontiers 2021年第3期12卷 60-77页

作者： Abu Reza Md Towfiqul Islam Swapan Talukdar Susanta Mahato Sonali Kundu Kutub Uddin Eibek Quoc Bao Pham Alban Kuriqi Nguyen Thi Thuy Linh Department of Disaster Management Begum Rokeya UniversityRangpurBangladesh Department of Geography University of Gour BangaMaldaWest BengalIndia Environmental Quality Atmospheric Science and Climate Change Research GroupTon Duc Thang UniversityHo Chi Minh CityVietnam Faculty of Environment and Labour Safety Ton Duc Thang UniversityHo Chi Minh CityVietnam CERIS Instituto Superior TécnicoUniversidade de LisboaLisbonPortugal Institute of Research and Development Duy Tan UniversityDanang 550000Vietnam Faculty of Environmental and Chemical Engineering Duy Tan UniversityDanang 550000Vietnam

floods are one of nature's most destructive disasters because of the immense damage to land,buildings,and human *** is difficult to forecast the areas that are vulnerable to flash flooding due to the dynamic and complex nature of the flash ***,earlier identification of flash flood susceptible sites can be performed using advanced machine learning models for managing flood *** this study,we applied and assessed two new hybrid ensemble models,namely Dagging and Random Subspace(RS)coupled with Artificial Neural Network(ANN),Random Forest(RF),and Support Vector Machine(SVM)which are the other three state-of-the-art machine learning models for modelling flood susceptibility maps at the Teesta River basin,the northern region of *** application of these models includes twelve flood influencing factors with 413 current and former flooding points,which were transferred in a GIS *** information gain ratio,the multicollinearity diagnostics tests were employed to determine the association between the occurrences and flood influential *** the validation and the comparison of these models,for the ability to predict the statistical appraisal measures such as Freidman,Wilcoxon signed-rank,and t-paired tests and Receiver Operating Characteristic Curve(ROC)were *** value of the Area Under the Curve(AUC)of ROC was above 0.80 for all *** flood susceptibility modelling,the Dagging model performs superior,followed by RF,the ANN,the SVM,and the RS,then the several benchmark *** approach and solution-oriented outcomes outlined in this paper will assist state and local authorities as well as policy makers in reducing flood-related threats and will also assist in the implementation of effective mitigation strategies to mitigate future damage.

关键词： flood hazard flood vulnerability Flash floods Debris flow Teesta River basin Bangladesh

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