Flooding and its relationship with land cover change, population growth, and road density

Geoscience Frontiers, Journal Year: 2021, Volume and Issue: 12(6), P. 101224 - 101224

Published: May 5, 2021

Bangladesh experiences frequent hydro-climatic disasters such as flooding. These are believed to be associated with land use changes and climate variability. However, identifying the factors that lead flooding is challenging. This study mapped flood susceptibility in northeast region of using Bayesian regularization back propagation (BRBP) neural network, classification regression trees (CART), a statistical model (STM) evidence belief function (EBF), their ensemble models (EMs) for three time periods (2000, 2014, 2017). The accuracy machine learning algorithms (MLAs), STM, EMs were assessed by considering area under curve—receiver operating characteristic (AUC-ROC). Evaluation levels aforementioned revealed EM4 (BRBP-CART-EBF) outperformed (AUC > 90%) standalone other analyzed. Furthermore, this investigated relationships among cover change (LCC), population growth (PG), road density (RD), relative (RCF) areas period between 2000 2017. results showed very high increased 19.72% 2017, while PG rate 51.68% over same period. Pearson correlation coefficient RCF RD was calculated 0.496. findings highlight significant association floods causative factors. could valuable policymakers resource managers they can improvements management reduction damage risks.

Language: Английский

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Flood Susceptibility Assessment Using Novel Ensemble of Hyperpipes and Support Vector Regression Algorithms

Water, Journal Year: 2021, Volume and Issue: 13(2), P. 241 - 241

Published: Jan. 19, 2021

Recurrent floods are one of the major global threats among people, particularly in developing countries like India, as this nation has a tropical monsoon type climate. Therefore, flood susceptibility (FS) mapping is indeed necessary to overcome natural hazard phenomena. With mind, we evaluated prediction performance FS Koiya River basin, Eastern India. The present research work was done through preparation sophisticated inventory map; eight conditioning variables were selected based on topography and hydro-climatological condition, by applying novel ensemble approach hyperpipes (HP) support vector regression (SVR) machine learning (ML) algorithms. HP-SVR also compared with stand-alone ML algorithms HP SVR. In relative importance variables, distance river most dominant factor for occurrences followed rainfall, land use cover (LULC), normalized difference vegetation index (NDVI). validation accuracy assessment maps five popular statistical methods. result evaluation showed that optimal model (AUC = 0.915, sensitivity 0.932, specificity 0.902, 0.928 Kappa 0.835) assessment, 0.885) SVR 0.871).

Language: Английский

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Urban waterlogging risk as an undervalued environmental challenge: An Integrated MCDA-GIS based modeling approach

Environmental Challenges, Journal Year: 2021, Volume and Issue: 4, P. 100194 - 100194

Published: June 27, 2021

In the last few decades, rainfall-induced urban waterlogging has become a significant environmental barrier and acquired global prominence worldwide due to its frequent threat, which results in infrastructure damage economic loss. This study aims model identify hazard, vulnerability, risk zones unplanned city of Siliguri, 'Gateway North-east India', with help an integrated Analytical hierarchy process (AHP) GIS techniques. Due lack comprehensive database, systematic assessment Siliguri not yet been carried out. However, is seasonal phenomenon city, especially during monsoon seasons, when short-duration high-intensity rainfall cause inundation low-lying areas causing mayhem city. Therefore, this present study, primary field investigation was conducted prepare inventory map along seventeen other parameters, including spatial attribute data from secondary sources delineate map. Further, final distribution slums locations, revealing that larger proportion slum households are under high-risk zones. The suggest about 46% high very hazard zones, while 38% highly vulnerable waterlogging. reveals around 35% area susceptible threat waterlogging, mostly concentrated central part center. Besides, consistency assessed by curve (AUC), gives accuracy 0.782 or 78.2%. study's overall strategy may be used for planning mitigation efforts reduce future incidents all world.

Language: Английский

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A novel hybrid of meta-optimization approach for flash flood-susceptibility assessment in a monsoon-dominated watershed, Eastern India

Environmental Earth Sciences, Journal Year: 2022, Volume and Issue: 81(5)

Published: Feb. 21, 2022

Language: Английский

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

International Journal of Disaster Risk Reduction, Journal Year: 2024, Volume and Issue: 108, P. 104503 - 104503

Published: April 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.

Language: Английский

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Flood susceptibility mapping using an improved analytic network process with statistical models

Geomatics Natural Hazards and Risk, Journal Year: 2020, Volume and Issue: 11(1), P. 2282 - 2314

Published: Jan. 1, 2020

Flooding is a natural disaster that causes considerable damage to different sectors and severely affects economic social activities. The city of Saqqez in Iran susceptible flooding due its specific environmental characteristics. Therefore, susceptibility vulnerability mapping are essential for comprehensive management reduce the harmful effects flooding. primary purpose this study combine Analytic Network Process (ANP) decision-making method statistical models Frequency Ratio (FR), Evidential Belief Function (EBF), Ordered Weight Average (OWA) flood City Kurdistan Province, Iran. frequency ratio was used instead expert opinions weight criteria ANP. ten factors influencing area slope, rainfall, slope length, topographic wetness index, aspect, altitude, curvature, distance from river, geology, land use/land cover. We identified 42 points area, 70% which modelling, remaining 30% validate models. Receiver Operating Characteristic (ROC) curve evaluate results. under obtained ROC indicates superior performance ANP EBF hybrid model (ANP-EBF) with 95.1% efficiency compared combination FR (ANP-FR) 91% OWA (ANP-OWA) 89.6% efficiency.

Language: Английский

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DEM resolution effects on machine learning performance for flood probability mapping

Journal of Hydro-environment Research, Journal Year: 2021, Volume and Issue: 40, P. 1 - 16

Published: Nov. 9, 2021

Floods are among the devastating natural disasters that occurred very frequently in arid regions during last decades. Accurate assessment of flood susceptibility mapping is crucial sustainable development. It helps respective authorities to prevent as much possible their irreversible consequences. The Digital Elevation Model (DEM) spatial resolution one most base layer factors for modeling Flood Probability Maps (FPMs). Therefore, main objective this study was assess influence DEMs 12.5 m (ALOS PALSAR) and 30 (ASTER) on accuracy probability prediction using three machine learning models (MLMs), including Random Forest (RF), Artificial Neural Network (ANN), Generalized Linear (GLM). This selected 14 causative independent variables, 220 locations were dependent variables. Dependent variables divided into training (70%) validation (30%) modeling. Receiver Operating Characteristic Curve (ROC), Kappa index, accuracy, other statistical criteria used evaluate models' accuracy. results showed resolving DEM alone cannot significantly affect regardless applied MLM independently model performance In contrast, such altitude, precipitation, distance from river have a considerable impact floods region. Also, evaluation RF (AUC12.5,30m = 0.983, 0.975) more accurate preparing FPM than ANN 0.949, 0.93) GLM 0.965, 0.949) models. study's solution-oriented findings might help water managers decision-makers make effective adaptation mitigation measures against potential flooding.

Language: Английский

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Ensemble approach to develop landslide susceptibility map in landslide dominated Sikkim Himalayan region, India

Environmental Earth Sciences, Journal Year: 2020, Volume and Issue: 79(20)

Published: Oct. 1, 2020

Language: Английский

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Flood risk assessment using geospatial data and multi-criteria decision approach: a study from historically active flood-prone region of Himalayan foothill, India

Arabian Journal of Geosciences, Journal Year: 2021, Volume and Issue: 14(11)

Published: May 27, 2021

Language: Английский

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Torrential rainfall-induced landslide susceptibility assessment using machine learning and statistical methods of eastern Himalaya

Natural Hazards, Journal Year: 2021, Volume and Issue: 107(1), P. 697 - 722

Published: Feb. 8, 2021

Language: Английский

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