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

The flood hazard management is one of the major challenges in the floodplain regions worldwide.With the rise in population growth and the spread of infrastructural development,the level of risk has increased over time.Therefore,the prediction of flood susceptible area is a key challenge for the adoption of management plans.Flood 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 manner.Therefore,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 models.The flood susceptible models were constructed based on nine flood conditioning parameters.The 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 developed.Also,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 models.Friedman test and Wilcoxon Signed rank test were employed to compare the generated flood susceptible models.Results showed that 11.95%-12.99%of the entire basin area(10188.4 km^(2))comes under very high flood-susceptible zones.Accuracy evaluation results have shown that the performance of ensemble flood susceptible models outperforms other standalone machine learning models.The flood simulation model and IFV model were also spatially adjusted with the flood susceptibility models.Therefore,the present study recommended for the ensemble flood susceptibility prediction and IFV based validation along with conventional ways.

A Study on Anthropogenic Activities Influencing Flood Vulnerability in Ala Riverfront Residential Areas of Akure, Nigeria

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 measures.With respect to data gathering for the study,265 questionnaires which amounted to 1%of the research population were administered using simple random sampling.Findings 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 waterways.It also exposed resident’s poor dispositions to flood forecasts and non-adherence to other management measures.The study concludes by recommending public enlightenment campaign to reducing denizen’s vulnerability to flood disaster.There 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 downpour.It further suggests creation of artificial lake as natural basin to collect huge volume of water discharged from all watersheds leading to the river.Ultimately,stringent efforts of environmental and town planning officers are needed to enforce total compliance to all intended management regulations to check the menace.

Geospatial Analysis of Flood Vulnerability Levels Based on Physical Characteristics and Resilience Capacity of Peri-Urban Settlements in 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.

Flood susceptibility modelling using advanced ensemble machine learning models

Floods are one of nature's most destructive disasters because of the immense damage to land,buildings,and human fatalities.It is difficult to forecast the areas that are vulnerable to flash flooding due to the dynamic and complex nature of the flash floods.Therefore,earlier identification of flash flood susceptible sites can be performed using advanced machine learning models for managing flood disasters.In 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 Bangladesh.The application of these models includes twelve flood influencing factors with 413 current and former flooding points,which were transferred in a GIS environment.The information gain ratio,the multicollinearity diagnostics tests were employed to determine the association between the occurrences and flood influential factors.For 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 employed.The value of the Area Under the Curve(AUC)of ROC was above 0.80 for all models.For flood susceptibility modelling,the Dagging model performs superior,followed by RF,the ANN,the SVM,and the RS,then the several benchmark models.The 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.

Assessing Multidimensional Vulnerability of Rural Areas to Flooding:An Index‑Based Approach

Vulnerability assessment is essential for understanding and launching effective flood risk reduction strategies.This study aimed to examine the vulnerability of flood-prone rural communities in southern Punjab,Pakistan to external shocks.The concept of vulnerability encompasses a range of dimensions,including physical,social,institutional,environmental,economic,and attitudinal.Using a composite index method,indices were developed for each dimension and combined to create a multidimensional measure of vulnerability.A sample of 365 communities was selected using the Yamane sampling technique,and data were collected through a questionnaire containing 65 indicators across all dimensions.Descriptive statistics and ANOVA tests were used to analyze the data.The results show that communities near the Chenab River had higher attitudinal and institutional vulnerability compared to other communities.High attitudinal vulnerabilities were as sociated with poorly perceived flood risks and low preparedness measures,whereas institutional vulnerabilities were driven by conventional flood protection strategies,lack of institutional trust,and lack of flood risk awareness.This research provides insights into the various components of vulnerability in flood-prone rural communities in Pakistan and demonstrates a useful methodology that can be applied to other disasters at different spatial scales.

French Insurance and Flood Risk: Assessing the Impact of Prevention Through the Rating of Action Programs for Flood Prevention

French insurance is part of a public-private partnership with the French State to compensate flood-related damages, and there is growing concern among insurers about damaging climatic events. To promote preventive actions as well as to take them into account in flood risk assessment, insurers have recently expressed the need to develop a rating system of preventive actions. This study identified Action Programs for Flood Prevention (Programme d’Action pour la Prévention des Inondations—PAPI) as the key French public policy instrument: they provide an overview of the diversity of actions that are conducted locally at the relevant risk basin scale. A database of all intended actions in the 145 launched PAPIs was constructed and the actions were coded (88 codes). The analysis consisted of two steps: (1) We conducted an expert valuation using the Analytic Network Process and the Analytic Hierarchy Process, aiming at creating an experimental national rating system. The results show the importance of implementing an integrated strategy that particularly emphasizes land use planning and urban planning. This appears even sounder as individual actions show relatively limited effectiveness. (2) The ratings of actions were applied to each PAPI according to their implemented actions. The resulting scores for individual PAPIs, as local risk coping capacity indicators, show great variance ranging from 8 to 324 points. This confirms the need to take prevention into account in flood risk assessment.