Flood susceptibility mapping contributes to disaster risk reduction: A case study in Sindh, Pakistan

International Journal of Disaster Risk Reduction, Год журнала: 2024, Номер 108, С. 104503 - 104503

Опубликована: Апрель 23, 2024

Floods are a widespread and damaging natural phenomenon that causes harm to human lives, resources, property has agricultural, eco-environmental, economic impacts. Therefore, it is crucial perform flood susceptibility mapping (FSM) identify susceptible zones mitigate reduce damage. This study assessed the damage caused by 2022 flash in Sindh identified flood-susceptible based on frequency ratio (FR) analytical hierarchy process (AHP) models. Flood inventory maps were generated, containing 150 sampling points, which manually selected from Landsat imagery. The points split into 70% for training 30% validating results. Furthermore, fourteen conditioning factors considered analysis developing FSM. final FSM categorized five zones, representing levels very low high. results areas under high Ghotki (FR 4.42% AHP 5.66%), Dadu 21.40% 21.29%), Sanghar 6.81% 6.78%). Ultimately, accuracy was evaluated using receiver operating characteristics area curve method, resulting 82%, 83%), 91%, 90%), 96%, 95%). enhances scientific understanding of impacts across diverse regions emphasizes importance accurate informed decision-making. findings provide valuable insights supportive policymakers, agricultural planners, stakeholders engaged risk management adverse consequences floods.

Язык: Английский

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GIS-based flood susceptibility mapping using bivariate statistical model in Swat River Basin, Eastern Hindukush region, Pakistan

Frontiers in Environmental Science, Год журнала: 2023, Номер 11

Опубликована: Июль 6, 2023

Frequent flooding can greatly jeopardize local people’s lives, properties, agriculture, economy, etc. The Swat River Basin (SRB), in the eastern Hindukush region of Pakistan, is a major flood-prone basin with long history devastating floods and substantial socioeconomic physical damages. Here we produced flood susceptibility map SRB, using frequency ratio (FR) bivariate statistical model. A database was created that comprised inventory as dependent variable causative factors (slope, elevation, curvature, drainage density, topographic wetness index, stream power land use cover, normalized difference vegetation rainfall) independent variables association between them were quantified. Data collected remote sensing sources, field surveys, available literature, all studied resampled to 30 m resolution spatially distributed. results show about 26% areas are very high highly susceptible flooding, 19% moderate, whereas 55% low SRB. Overall, southern SRB compared their northern counterparts, while slope, curvature vital susceptibility. Our model’s success prediction rates 91.6% 90.3%, respectively, based on ROC (receiver operating characteristic) curve. findings this study will lead better management control risk region. study’s assist decision-makers make appropriate sustainable strategies for mitigation future damage

Язык: Английский

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Flood vulnerability assessment in the flood prone area of Khyber Pakhtunkhwa, Pakistan

Frontiers in Environmental Science, Год журнала: 2024, Номер 12

Опубликована: Янв. 19, 2024

Floods are among the most destructive natural disasters, causing extensive damage to human lives, property, and environment. Pakistan is susceptible calamities, such as floods, resulting in millions of people being impacted yearly. It has been demonstrated that flood severity rising may continue escalate coming years because climate change-induced changes monsoon precipitation country. Given country’s exposure flooding, it essential assess vulnerability floods prepare for mitigate their impact Pakistan. This study provides a new conceptual framework assessing risk Charsadda, flood-prone district evaluates settlements based on four indicators: population density, average gross domestic product (GDP) land, distance between rivers, land use cover (LULC). The analytical hierarchy process (AHP) technique was integrated with geographical information system (GIS) level area. results reveal higher degree region. spatial pattern vulnerable areas reveals significant connection high-risk densely populated during different seasons. further more than 60% area arable highly flood. land-use setup show high extremely values normalized threshold 0.3–0.4, respectively. an in-depth comprehensive analysis chosen indicators, evaluation methods, results, making this valuable contribution field assessment. findings also include thematic maps related stakeholders effective management

Язык: Английский

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Synergistic assessment of multi-scenario urban waterlogging through data-driven decoupling analysis in high-density urban areas: A case study in Shenzhen, China

Journal of Environmental Management, Год журнала: 2024, Номер 369, С. 122330 - 122330

Опубликована: Сен. 3, 2024

Язык: Английский

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Leveraging GIS-based AHP, remote sensing, and machine learning for susceptibility assessment of different flood types in peshawar, Pakistan

Journal of Environmental Management, Год журнала: 2024, Номер 371, С. 123094 - 123094

Опубликована: Ноя. 2, 2024

Язык: Английский

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An increasing trend in daily monsoon precipitation extreme indices over Pakistan and its relationship with atmospheric circulations

Frontiers in Environmental Science, Год журнала: 2023, Номер 11

Опубликована: Сен. 8, 2023

This study assessed spatiotemporal trends in daily monsoon precipitation extremes at seasonal and sub-seasonal scales (June, July, August, September) their links with atmospheric circulations over Pakistan. The used observed data from fifty in-situ stations reanalysis products the European Centre for Medium-Range Weather Forecasts (ECMWF) National Centers Environmental Prediction/the Center Atmospheric Research (NCEP/NCAR) during 1981–2018. A suite of seven extreme indices non-parametric statistical techniques were to infer frequency intensity indices. An increase overall was evident, a maximum tendency country’s northwestern (z-score=>2.5), central, eastern (z-score > 4) monsoon-dominant parts. northern southwestern parts country exhibited slight decrease <–2) intensity. Sen’s Slope estimator (SSE) shows an western (0.20 days) indicating shift maxima precipitation. regional wet days (R1 mm) higher values mMK (3.71) SSE (0.3) region 2 Similar results moderate are evident except regions 1 3. 1-day increased 3 (mMK: 1.39, SSE: 2.32). extremely (R99p TOT) has all 1. temporal mutations showed dynamic changes, clearly reflecting historical events. negatively correlated altitude (R = −0.00039). probability density function (PDF) significant June September probabilistic positive July August. intensified mid-latitude westerlies subtropical zonal easterlies teleconnections, strengthening trough, land-ocean thermal contrast potential drivers increasing trend extremes. current could serve as benchmark future researchers policymakers devise effective mitigation strategies sustainable development.

Язык: Английский

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Flood hazard assessment in Yemen using a novel hybrid approach of Grey Wolf and Levenberg Marquardt optimizers

Geocarto International, Год журнала: 2023, Номер 38(1)

Опубликована: Авг. 3, 2023

This study aims to map flood susceptibility in the Qaa'Jahran watersheds located Dhamar, Yemen, using geoprocessing and computational techniques. Historical data SAR imagery were used monitor create a inventory map. The Artificial Neutral Network (ANN) was trained novel algorithm called GWO_LM, which is hybridization between Levenberg-Marquardt (LM) Grey Wolf Optimizer (GWO) meta-heuristic compared results with state of art machine learning algorithms. GWO_LM_ANN model exhibited excellent performance evaluation, achieving precision 97.92%, sensitivity 100%, specificity F1 score 98.95%, accuracy 98.75%, AUC 98.48. indicates that GWO_LM for training ANN enhanced searching process optimal weights, resulting outperforming other state-of-the-art models. findings hold significant implications disaster preparedness response watersheds, enabling targeted efficient non-structural solutions mitigate detrimental effects flash floods particularly sensitive locations. use previously unexplored represents notable advancement assessment, surpassing traditional methods offering insights existing literature.

Язык: Английский

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Multi-hazard could exacerbate in coastal Bangladesh in the context of climate change

Journal of Cleaner Production, Год журнала: 2024, Номер 457, С. 142289 - 142289

Опубликована: Апрель 23, 2024

Язык: Английский

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Attribution analysis of urban social resilience differences under rainstorm disaster impact: Insights from interpretable spatial machine learning framework

Sustainable Cities and Society, Год журнала: 2024, Номер unknown, С. 106029 - 106029

Опубликована: Дек. 1, 2024

Язык: Английский

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One-dimensional deep learning driven geospatial analysis for flash flood susceptibility mapping: a case study in North Central Vietnam

Earth Science Informatics, Год журнала: 2024, Номер 17(5), С. 4419 - 4440

Опубликована: Июль 6, 2024

Abstract Flash floods rank among the most catastrophic natural disasters worldwide, inflicting severe socio-economic, environmental, and human impacts. Consequently, accurately identifying areas at potential risk is of paramount importance. This study investigates efficacy Deep 1D-Convolutional Neural Networks (Deep 1D-CNN) in spatially predicting flash floods, with a specific focus on frequent tropical cyclone-induced Thanh Hoa province, North Central Vietnam. The 1D-CNN was structured four convolutional layers, two pooling one flattened layer, fully connected employing ADAM algorithm for optimization Mean Squared Error (MSE) loss calculation. A geodatabase containing 2540 flood locations 12 influencing factors compiled using multi-source geospatial data. database used to train check model. results indicate that model achieved high predictive accuracy (90.2%), along Kappa value 0.804 an AUC (Area Under Curve) 0.969, surpassing benchmark models such as SVM (Support Vector Machine) LR (Logistic Regression). concludes highly effective tool modeling floods.

Язык: Английский

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