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Geoscience Frontiers 2021年第5期12卷 327-341页

作者： Sumit Das Amitesh Gupta Department of Geography Savitribai Phule Pune UniversityPune 411007India Marine and Atmospheric Science Department Indian Institute of Remote Sensing(ISRO)DehradunIndia Department of Remote Sensing and GIS JIS UniversityKolkataIndia

The Subarnarekha River in east India experiences frequent high magnitude flooding in monsoon *** this study,we present an in-depth analysis of flood hydrology and GIS-based flood susceptibility mapping of the entire *** 40 years of annual peak discharge data,historical cross-sections of different gauging sites,and 12 flood conditioning factors were *** flood susceptibility mapping followed an expert knowledge-based multi-parametric analytical hierarchy process(AHP)and optimized AHP-VIP *** hydrology data indicated more than 5 times higher discharge contrasted with the mean streamflow of the peak monsoon month in all hydro-monitoring stations that correspond to possible overbank flooding in the shallow semi-alluvial reaches of the Subarnarekha *** ratio revealed continuous changes on the channel cross-sections at decadal scale in all gauging *** flood susceptibility map through optimized AHP-VIP method showed a great amount of areas(38%)have a high probability of flooding and demands earnest attention of administrative *** AHP-VIP based flood susceptibility map was theoritically validated through AUC approach and it showed fairly high accuracy(AUC=0.93).Our study offers an exceptionally cost and time effective solution to the flooding issues in the Subarnarekha basin.

关键词： flood susceptibility AHP GIS Discharge Subarnarekha India

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Spatial flood susceptibility mapping using an explainable artificial intelligence(XAI)model

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Geoscience Frontiers 2023年第6期14卷 8-19页

作者： Biswajeet Pradhan Saro Lee Abhirup Dikshit Hyesu Kim Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS) School of Civil and Environmental EngineeringFaculty of Engineering&ITUniversity of Technology SydneySydneyNSW 2007Australia Earth Observation Center Institute of Climate ChangeUniversiti Kebangsaan Malaysia43600 UKMBangiSelangorMalaysia Geoscience Data Center Korea Institute of Geoscience and Mineral Resources(KIGAM)124 Gwahang-noYuseong-guDaejeon 34132South Korea Department of Resources Engineering Korea University of Science and Technology217 Gajeong-roYuseong-guDaejeon 34113South Korea Department of Astronomy Space Science and GeologyChungnam National University99 Daehak-roYuseong-guDaejeon 34134South Korea

floods are natural hazards that lead to devastating financial losses and large displacements of *** susceptibility maps can improve mitigation measures according to the specific conditions of a study *** design of flood susceptibility maps has been enhanced through use of hybrid machine learning and deep learning *** these models have achieved better accuracy than traditional models,they are not widely used by stakeholders due to their black-box *** this study,we propose the application of an explainable artificial intelligence(XAI)model that incorporates the Shapley additive explanation(SHAP)model to interpret the outcomes of convolutional neural network(CNN)deep learning models,and analyze the impact of variables on flood susceptibility *** study was conducted in Jinju Province,South Korea,which has a long history of flood *** performance was evaluated using the area under the receiver operating characteristic curve(AUROC),which showed a prediction accuracy of 88.4%.SHAP plots showed that land use and various soil attributes significantly affected flood susceptibility in the study *** light of these findings,we recommend the use of XAIbased models in future flood susceptibility mapping studies to improve interpretations of model outcomes,and build trust among stakeholders during the flood-related decision-making process.

关键词： flood susceptibility Explainable AI Deep learning South Korea

Evaluating Factors Affecting flood susceptibility in the Yangtze River Delta Using Machine Learning Methods

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International Journal of Disaster Risk Science 2024年第5期15卷 738-753页

作者： Kaili Zhu Zhaoli Wang Chengguang Lai Shanshan Li Zhaoyang Zeng Xiaohong Chen School of Civil Engineering and Transportation State Key Laboratory of Subtropical Building and Urban ScienceSouth China University of Technology Pazhou Lab Center for Water Resources and Environment Sun Yat-sen University

floods are widespread and dangerous natural hazards worldwide. It is essential to grasp the causes of floods to mitigate their severe eff ects on people and society. The key drivers of flood susceptibility in rapidly urbanizing areas can vary depending on the specific context and require further investigation. This research developed an index system comprising 10 indicators associated with factors and environments that lead to disasters, and used machine learning methods to assess flood susceptibility. The core urban area of the Yangtze River Delta served as a case study. Four scenarios depicting separate and combined eff ects of climate change and human activity were evaluated using data from various periods, to measure the spatial variability in flood susceptibility. The findings demonstrate that the extreme gradient boosting model outperformed the decision tree, support vector machine, and stacked models in evaluating flood susceptibility. Both climate change and human activity were found to act as catalysts for flooding in the region. Areas with increasing susceptibility were mainly distributed to the northwest and southeast of Taihu Lake. Areas with increased flood susceptibility caused by climate change were significantly larger than those caused by human activity, indicating that climate change was the dominant factor influencing flood susceptibility in the region. By comparing the relationship between the indicators and flood susceptibility, the rising intensity and frequency of extreme precipitation as well as an increase in impervious surface areas were identified as important reasons of heightened flood susceptibility in the Yangtze River Delta region. This study emphasized the significance of formulating adaptive strategies to enhance flood control capabilities to cope with the changing environment.

关键词： Climate change flood susceptibility Human activity Machine learning methods Yangtze River Delta core urban agglomeration

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

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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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Enhancing flood risk assessment in northern Morocco with tuned machine learning and advanced geospatial techniques

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Journal of Geographical Sciences 2024年第12期34卷 2477-2508页

作者： MOUTAOUAKIL Wassima HAMIDA Soufiane SALEH Shawki LAMRANI Driss MAHJOUBI Mohamed Amine CHERRADI Bouchaib RAIHANI Abdelhadi EEIS Laboratory ENSET of MohammediaHassan Ⅱ University of CasablancaMohammediaMorocco IACS Laboratory ENSET of MohammediaHassan Ⅱ University of CasablancaMohammediaMorocco GENIUS Laboratory SupMTI of RabatRabatMorocco STIE Team CRMEF Casablanca-SettatProvincial Section of El JadidaEl JadidaMorocco

Mapping floods is crucial for effective disaster management. This study focuses on flood assessment in northern Morocco, specifically Tangier, Tetouan, and Larache. Due to the lack of a comprehensive flood inventory map, we used unsupervised learning techniques, such as K-means clustering and fuzzy logic algorithms, to predict flood-prone areas. We identified nine conditioning factors influencing flood risk: elevation, slope, aspect, plan curvature, profile curvature, land use, soil type, normalized difference vegetation index(NDVI), and topographic position index(TPI). Using Landsat-8 imagery and a Digital Elevation Model(DEM) within a Geographic Information System(GIS), we analyzed topographic and geo-environmental variables. K-means clustering achieved silhouette scores of 0.66 in Tangier and 0.70 in Tetouan, while the fuzzy logic method in Larache produced a Davies-Bouldin Index(DBI) score of 0.35. The maps classified flood risk levels into low, moderate, and high categories. This research demonstrates the integration of machine learning and remote sensing for predicting flood-prone areas without existing flood inventory maps. Our findings highlight the main factors contributing to flash floods and assess their impact, enhancing the understanding of flood dynamics and improving flood management strategies in vulnerable regions.

关键词： remote sensing conditioning factors GIS flood susceptibility machine learning DEM

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