16 research outputs found

    Flood zoning and developing strategies to increase resilience against floods with a crisis management approach

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    Assessment and planning of crisis management with the approach to natural flood disasters include many factors. In this regard, one of the basic principles of crisis management is based on the resilience of urban infrastructure against floods. This study developed strategies to increase resilience by flood zoning and crisis management. The investigation of the current situation shows that despite the efforts being made, the climatic and environmental conditions of the rivers, the settlements of the infiltration basin, the constructions, and the location inaccuracy of the following structures indicate many challenges in managing the current situation in various components of crisis management. In this regard, the main direction of this article is to evaluate the urban resilience of the Khuzestan region against floods based on a crisis management approach and technique for order preference by similarity to ideal solution (TOPSIS) and Fuzzy weighting methods using geographic information system (GIS)

    Flood Susceptibility Mapping Using Random Forest Machine Learning and Generalized Bayesian Linear Model

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    Today, the phenomenon of flooding is one of the most complex hazardous events that, more than any other natural disaster, causes deaths and finances every year in different parts of the world. Therefore, flood susceptibility mapping is the first step in a flood management program. The purpose of this study was to identify flood susceptible areas using two methods of random forest (RF) and Bayesian generalized linear model (GLMbayesian) machine learning in the Tajan watershed in Mazandaran province, Sari. Past flood distribution maps were prepared to predict future floods. Of the 263 flood locations, 80% (210 flood locations) was used for modeling and 20% (53 flood locations) was used for validation. Based on previous studies and surveying of the study area, 13 conditional factors were selected for flood zoning. The results showed that three factors of elevation (21.55), distance from the river (15.28) and slope (11.18) had the highest impact on flood occurrence in the study area, respectively. The results also showed that the AUC values for RF and GLMbayesian models were 0.91 and 0.847, respectively, indicating the superiority of the RF model and the accuracy of this model in flood susceptibility mapping in the study area. The highest flood susceptibility area in the RF model is in the very low class and the high class in the GLMbayesian model

    Spatial prediction of flood susceptible areas using machine learning approach: a focus on west african region

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    Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesThe constant change in the environment due to increasing urbanization and climate change has led to recurrent flood occurrences with a devastating impact on lives and properties. Therefore, it is essential to identify the factors that drive flood occurrences, and flood locations prone to flooding which can be achieved through the performance of Flood Susceptibility Modelling (FSM) utilizing stand-alone and hybrid machine learning models to attain accurate and sustainable results which can instigate mitigation measures and flood risk control. In this research, novel hybridizations of Index of Entropy (IOE) with Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF) was performed and equally as stand-alone models in Flood Susceptibility Modelling (FSM) and results of each model compared. First, feature selection and multi-collinearity analysis were performed to identify the predictive ability and the inter-relationship among the factors. Subsequently, IOE was performed as bivariate and multivariate statistical analysis to assess the correlation among the flood influencing factor’s classes with flooding and the overall influence (weight) of each factor on flooding. Subsequently, the weight generated was used in training the machine learning models. The performance of the proposed models was assessed using the popular Area Under Curve (AUC) and statistical metrics. Percentagewise, results attained reveals that DT-IOE hybrid model had the highest prediction accuracy of 87.1% while the DT had the lowest prediction performance of 77.0%. Among the other models, the result attained highlight that the proposed hybrid of machine learning and statistical models had a higher performance than the stand-alone models which reflect the detailed assessment performed by the hybrid models. The final susceptibility maps derived revealed that about 21% of the study area are highly prone to flooding and it is revealed that human-induced factors do have a huge influence on flooding in the region

    Flood susceptibility assessment using extreme gradient boosting (EGB), Iran

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    Flood occurs as a result of high intensity and long-term rainfalls accompanied by snowmelt which flow out of the main river channel onto the flood prone areas and damage the buildings, roads, and facilities and cause life losses. This study aims to implement extreme gradient boosting (EGB) method for the first time in flood susceptibility modelling and compare its performance with three advanced benchmark models including Frequency Ratio (FR), Random Forest (RF), and Generalized Additive Model (GAM). Flood susceptibility map is an efficient tool to make decision for flood control. To do this, the altitude, slope degree, profile curvature, topographic wetness index (TWI), distance from rivers, normalized difference vegetation index, plan curvature, rainfall, land use, stream power index, and lithology were fed to the models. To run the models, 243 flood locations were detected by field surveys and national reports. The same number of locations were randomly created in the study regions and considered as non-flood locations. The flood and non-flood locations were split in 70% ratio for the training dataset and 30% ratio for the testing dataset. Both flood and non-flood locations were fed into the models and output flood susceptibility maps were produced. In order to evaluate the performance of the algorithms, receiver operating characteristics (ROC) curve was implemented. The results of the current research show that the RF model and EGB have the best performances with the area under ROC curve (AUC) of 0.985, and 0.980, followed by the GAM and FR algorithms with AUC values of 0.97, and 0.953, respectively. The results of variable importance by the RF model show that distance from rivers has an important influence on flood susceptibility mapping (FSM), followed by profile curvature, slope, TWI, and altitude. Considering the high performances of the RF and EGB models in flood susceptibility modelling, application of these models is recommended for such studies

    Spatial landslide susceptibility mapping using integrating an adaptive neuro-fuzzy inference system (ANFIS) with two multi-criteria decision-making approaches

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    Landslide is a type of slope process causing a plethora of economic damage and loss of lives worldwide every year. This study aimed to analyze spatial landslide susceptibility mapping in the Khalkhal-Tarom Basin by integrating an adaptive neuro-fuzzy inference system (ANFIS) with two multi-criteria decision-making approaches, i.e., the best-worst method (BWM) and the stepwise weight assessment ratio analysis (SWARA) techniques. For this purpose, the first step was to prepare a landslide inventory map, which was then divided randomly into the ratio of 70/30% for model training and validation. Thirteen conditioning factors were selected based on the previous studies and available data. In the next step, the BWM and the SWARA methods were utilized to determine the relationships between the sub-criteria and landslides. Finally, landslide susceptibility maps were generated by implementing ANFIS-BWM and ANFIS-SWARA ensemble models, and then several quantitative indices such as positive predictive value, negative predictive value, sensitivity, specificity, accuracy, root-mean-square-error, and the ROC curve were employed to appraise the predictive accuracy of each model. The results indicated that the ANFIS-BWM ensemble model (AUC = 75%, RMSE = 0.443) has better performance than ANFIS-SWARA (AUC = 73.6%, RMSE = 0.477). At the same time, the ANFIS-BWM model had the maximum sensitivity, specificity, and accuracy with values of 87.1%, 54.3%, and 40.7%, respectively. As a result, the BWM method was more efficient in training the ANFIS. Evidently, the generated landslide susceptibility maps (LSMs) can be very efficient in managing land use and preventing the damage caused by the landslide phenomenon. Graphical abstract: [Figure not available: see fulltext.

    Application of Analytical Hierarchy Process and Frequency Ratio Model for Predictive Flood Susceptibility Mapping Using GIS for the Khazir River Basin, Northern Iraq

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    Flood damage assessment is considered the essential tool for evaluating risk to civil and agricultural systems in land use planning. The validity of the studies’ outcome depends on the availability of data and their spatial distribution. The present study came to compute flood susceptibility maps utilizing two application models: (i) the frequency ratio, and (ii) the analytical hierarchy process. These models were then tested in the Khazir River basin using GIS with a selection of twelve flood conditioning factors. The flood inventory variables layer and flood-causing factors were created using remote sensing data, a digital elevation model, and secondary data from various sources. Then, the flood inventory map was highlight divided into training and test data, with 105 flood sites (70%) used for training and 45 sites (30%) used for testing. After applying the areas under the curve for the frequency ratio and analytical hierarchy process models, which were 90.6% and 88.9%, respectively, the final flood sensitivity maps showed similar results for the two models, which confirm the effectiveness of the adopted methodology. The study found a considerable spatial variance in flood sensitivity maps, as (21.06%) of the flooded areas are classified as having very low sensitivity to flooding, (24.09%) are classified as having low vulnerability to floods, and (23.79%) are classified as having moderate vulnerability, (24.10%) classified as highly vulnerable to flooding, and (6.96%) classified as highly vulnerable to flooding. Flood danger ranged from very low in mountain locations to very high in plain areas close to the riverbanks. Obtained results could be improved if a land-use planning policy will be applied, in order to establish a master plan for water resources development to avoid flood damage

    ArcDrain: A GIS Add-In for Automated Determination of Surface Runoff in Urban Catchments

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    ABSTRACT: Surface runoff determination in urban areas is crucial to facilitate ex ante water planning, especially in the context of climate and land cover changes, which are increasing the frequency of floods, due to a combination of violent storms and increased imperviousness. To this end, the spatial identification of urban areas prone to runoff accumulation is essential, to guarantee effective water management in the future. Under these premises, this work sought to produce a tool for automated determination of urban surface runoff using a geographic information systems (GIS). This tool, which was designed as an ArcGIS add-in called ArcDrain, consists of the discretization of urban areas into subcatchments and the subsequent application of the rational method for runoff depth estimation. The formulation of this method directly depends on land cover type and soil permeability, thereby enabling the identification of areas with a low infiltration capacity. ArcDrain was tested using the city of Santander (northern Spain) as a case study. The results achieved demonstrated the accuracy of the tool for detecting high runoff rates and how the inclusion of mitigation measures in the form of sustainable drainage systems (SuDS) and green infrastructure (GI) can help reduce flood hazards in critical zonesThis research was funded by the Spanish Ministry of Science, Innovation, and Universities, with funds from the State General Budget (PGE) and the European Regional Development Fund (ERDF), grant number RTI2018-094217-B-C32 (MCIU/AEI/FEDER, UE)

    A novel framework for addressing uncertainties in machine learning-based geospatial approaches for flood prediction

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    Globally, many studies on machine learning (ML)-based flood susceptibility modeling have been carried out in recent years. While majority of those models produce reasonably accurate flood predictions, the outcomes are subject to uncertainty since flood susceptibility models (FSMs) may produce varying spatial predictions. However, there have not been many attempts to address these uncertainties because identifying spatial agreement in flood projections is a complex process. This study presents a framework for reducing spatial disagreement among four standalone and hybridized ML-based FSMs: random forest (RF), k-nearest neighbor (KNN), multilayer perceptron (MLP), and hybridized genetic algorithm-gaussian radial basis function-support vector regression (GA-RBF-SVR). Besides, an optimized model was developed combining the outcomes of those four models. The southwest coastal region of Bangladesh was selected as the case area. A comparable percentage of flood potential area (approximately 60% of the total land areas) was produced by all ML-based models. Despite achieving high prediction accuracy, spatial discrepancy in the model outcomes was observed, with pixel-wise correlation coefficients across different models ranging from 0.62 to 0.91. The optimized model exhibited high prediction accuracy and improved spatial agreement by reducing the number of classification errors. The framework presented in this study might aid in the formulation of risk-based development plans and enhancement of current early warning systems

    Geospatial-based analysis for soil erosion susceptibility evaluation : application of a hybrid decision model

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    DATA AVAILABILITY : Data will be made available on request.Erosion hazard is a major environmental change in developing countries and therefore necessitates investigations for effective erosion control measures. This study is hinged on the numerous advantages of a hybrid Multi-Criteria Decision Model (MCDM) to assess erosion vulnerability using remote-sensed data and the application of Geographical Information System (GIS). Nine risk factors of erosion were selected for this study and their thematic maps were utilized to produce a spatial distribution of erosion hazard in the state. An integrated IVFRN–DEMATEL–ANP model was used to investigate the interrelationships between the risk factors and also obtain their final weights. The assessment model identified Rainfall, Erosivity Index, Stream Power Index, Sediment Transport Index, Topographic Wetness Index, and Soil as the most influential factors of erosion in the study area. The weighted linear combination method was used to integrate the risk factors to produce the spatial distribution of erosion vulnerability model. The method was validated using Anambra State of Nigeria. The findings from the study revealed that Anambra State is vulnerable to erosion hazard with 45% of the state lying between Very High and Medium vulnerable zones. A good predictive model performance of 89.7% was obtained using the AUC-ROC method. The feasibility of integrating the IVFRN, DEMATEL, and ANP models as an assessment model for mapping erosion vulnerability has been determined in this study, and this is vital in managing the impact of erosion hazards globally. The model’s identification of hydrological and topographical factors as major causes of erosion hazard emphasizes the importance of critical analysis of risk factors as done in this study for effective management of erosion. This study is a veritable tool for implementation of erosion mitigation measures.https://link.springer.com/journal/40808hj2023Future Afric

    An Overview of Multi-Criteria Decision Analysis (MCDA) Application in Managing Water-Related Disaster Events: Analyzing 20 Years of Literature for Flood and Drought Events

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    This paper provides an overview of multi-criteria decision analysis (MCDA) applications in managing water-related disasters (WRD). Although MCDA has been widely used in managing natural disasters, it appears that no literature review has been conducted on the applications of MCDA in the disaster management phases of mitigation, preparedness, response, and recovery. Therefore, this paper fills this gap by providing a bibliometric analysis of MCDA applications in managing flood and drought events. Out of 818 articles retrieved from scientific databases, 149 articles were shortlisted and analyzed using a Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) approach. The results show a significant growth in MCDA applications in the last five years, especially in managing flood events. Most articles focused on the mitigation phase of DMP, while other phases of preparedness, response, and recovery remained understudied. The analytical hierarchy process (AHP) was the most common MCDA technique used, followed by mixed-method techniques and TOPSIS. The article concludes the discussion by identifying a number of opportunities for future research in the use of MCDA for managing water-related disasters
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