Machine learning and remote sensing integration for leveraging urban sustainability: A review and framework

Sustainable Cities and Society, Journal Year: 2023, Volume and Issue: 96, P. 104653 - 104653

Published: May 15, 2023

Climate change and rapid urbanisation exacerbated multiple urban issues threatening sustainability. Numerous studies integrated machine learning remote sensing to monitor develop mitigation strategies for However, few comparatively analysed joint applications of This paper presents a systematic review formulates framework integrating in studies. The literature analysis reveals: Most occurred Asia, Europe, North America, driven by technical ethical factors, highlighting responsible approaches data-scarce regions; Reviewed prioritised physical spatial aspects over socioeconomic requiring multi-source data comprehensive analysis; Conventional satellite, aerial images, Lidar are prevalent due affordability, quality, accessibility; Although supervised dominates, unsupervised methods algorithm selection paradigms require exploration; Integration offers accurate results thorough image processing analytics, while acquisition decision-making necessitate human supervision. provides an integrative sensing, enriching insights into their potential analytics. study informs planning policymaking promoting efficient management via enhanced integration, bolstering data-driven decision-making.

Language: Английский

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Application of genetic algorithm in optimization parallel ensemble-based machine learning algorithms to flood susceptibility mapping using radar satellite imagery

The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 873, P. 162285 - 162285

Published: Feb. 17, 2023

Language: Английский

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A GIS-Based Flood Risk Assessment Using the Decision-Making Trial and Evaluation Laboratory Approach at a Regional Scale

Environmental Processes, Journal Year: 2024, Volume and Issue: 11(1)

Published: Feb. 13, 2024

Abstract This paper introduces an integrated methodology that exploits both GIS and the Decision-making Trial Evaluation Laboratory (DEMATEL) methods for assessing flood risk in Kosynthos River basin northeastern Greece. The study aims to address challenges arising from data limitations provide decision-makers with effective management strategies. integration of DEMATEL is crucial, providing a robust framework considers interdependencies among factors, particularly regions where conventional numerical modeling faces difficulties. preferred over other due its proficiency handling qualitative ability account interactions studied factors. proposed method based on two developed causality diagrams. first diagram crucial hazard absence data. second offers multidimensional analysis, considering criteria. Notably, referring vulnerability can adapt local (or national) conditions, ill-defined nature vulnerability. Given identifies highly hazardous vulnerable areas, not only provides essential insights but also supports formulating approaches mitigate impacts communities infrastructure. Validation includes sensitivity analysis comparison historical Effective weights derived enhance precision Flood Hazard Index (FHI) Vulnerability (FVI).

Language: Английский

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Integrating Machine Learning Ensembles for Landslide Susceptibility Mapping in Northern Pakistan

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(6), P. 988 - 988

Published: March 12, 2024

Natural disasters, notably landslides, pose significant threats to communities and infrastructure. Landslide susceptibility mapping (LSM) has been globally deemed as an effective tool mitigate such threats. In this regard, study considers the northern region of Pakistan, which is primarily susceptible landslides amid rugged topography, frequent seismic events, seasonal rainfall, carry out LSM. To achieve goal, pioneered fusion baseline models (logistic regression (LR), K-nearest neighbors (KNN), support vector machine (SVM)) with ensembled algorithms (Cascade Generalization (CG), random forest (RF), Light Gradient-Boosting Machine (LightGBM), AdaBoost, Dagging, XGBoost). With a dataset comprising 228 landslide inventory maps, employed classifier correlation-based feature selection (CFS) approach identify twelve most parameters instigating landslides. The evaluated included slope angle, elevation, aspect, geological features, proximity faults, roads, streams, was revealed primary factor influencing distribution, followed by aspect rainfall minute margin. models, validated AUC 0.784, ACC 0.912, K 0.394 for logistic well 0.907, 0.927, 0.620 XGBoost, highlight practical effectiveness potency results superior performance LR among XGBoost ensembles, contributed development precise LSM area. may serve valuable guiding risk-mitigation strategies policies in geohazard-prone regions at national global scales.

Language: Английский

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Spatial decision-making for urban flood vulnerability: A geomatics approach applied to Al-Ain City, UAE

Urban Climate, Journal Year: 2025, Volume and Issue: 59, P. 102297 - 102297

Published: Jan. 31, 2025

Language: Английский

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Urban flood risk assessment using AHP and geospatial techniques in swat Pakistan

SN Applied Sciences, Journal Year: 2023, Volume and Issue: 5(8)

Published: July 22, 2023

Abstract The rapid urbanization and changing climate patterns in Swat, Pakistan have increased the vulnerability of urban areas to flood events. Accurate assessment risk is crucial for effective planning disaster management. In current research study hazard index was developed using analytic hierarchy process (AHP) technique combination with geographical information system (GIS) environment Pakistan. integrates various data sources, including topographic maps, land use/land cover information, rainfall data, infrastructure develop a comprehensive model. weights obtained from AHP analysis are combined geospatial geographic generate maps. levels were categorized into five distinct classes: very low, moderate, high, high. Using GIS-AHP approach, higher assigned rainfall, distance river, elevation, slope comparison NDVI, TWI, LULC, curvature, soil type. map then reclassified each parameter. By overlaying these it determined that 5.6% total area classified as high risk, 52% 39.3% moderate 3.1% low risk. model can identify high-risk areas, prioritize mitigation measures, aid

Language: Английский

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Uncertainty Reduction in Flood Susceptibility Mapping Using Random Forest and eXtreme Gradient Boosting Algorithms in Two Tropical Desert Cities, Shibam and Marib, Yemen

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(2), P. 336 - 336

Published: Jan. 15, 2024

Flooding is a natural disaster that coexists with human beings and causes severe loss of life property worldwide. Although numerous studies for flood susceptibility modelling have been introduced, notable gap has the overlooked or reduced consideration uncertainty in accuracy produced maps. Challenges such as limited data, due to confidence bounds, overfitting problem are critical areas improving accurate models. We focus on mapping, mainly when there significant variation predictive relevance predictor factors. It also noted receiver operating characteristic (ROC) curve may not accurately depict sensitivity resulting map overfitting. Therefore, reducing was targeted increase improve processing time prediction. This study created spatial repository test models, containing data from historical flooding twelve topographic geo-environmental conditioning variables. Then, we applied random forest (RF) extreme gradient boosting (XGB) algorithms susceptibility, incorporating variable drop-off empirical loop function. The results showed function crucial method resolve model associated factors methods. approximately 8.42% 9.89% Marib City 9.93% 15.69% Shibam were highly vulnerable floods. Furthermore, this significantly contributes worldwide endeavors focused hazards linked disasters. approaches used can offer valuable insights strategies risks, particularly Yemen.

Language: Английский

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Exploring a GIS-based analytic hierarchy process for spatial flood risk assessment in Egypt: a case study of the Damietta branch

Environmental Sciences Europe, Journal Year: 2024, Volume and Issue: 36(1)

Published: Oct. 15, 2024

Abstract Floods are the most common and costly disasters worldwide, while spatial flood risk assessment is still challenging due to fewer observations method limitations. In this study, zonation in Nile districts of Damietta branch, Egypt, delineated assessed by integrating remote sensing with a geographic information system, an analytical hierarchy process (AHP). Twelve thematic layers (elevation, slope, normalized difference vegetation index, topographic wetness modified water positioning stream power Fournier drainage density, distance river, sediment transport lithology) used for producing susceptibility (FSZ) six parameters (total population, hospital, land use/land cover, population road road) utilized vulnerability zonation. Multicollinearity analysis applied identify highly correlated independent variables. Sensitivity studies have been assess effectiveness AHP model. The results indicate that high very classes cover 21.40% 8.26% area, respectively. 14.07%, 27.01%, 29.26% research respectively, zones classified as low, moderate found. Finally, FSZ validated using receiver operating characteristics curve area under (AUC) analysis. A higher AUC value (0.741) validation findings demonstrated validity approach. study will help planners, hydrologists, managers resources manage areas susceptible flooding reduce potential harm.

Language: Английский

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Flood Susceptibility Mapping: Integrating Machine Learning and GIS for Enhanced Risk Assessment

Applied Computing and Geosciences, Journal Year: 2024, Volume and Issue: 23, P. 100183 - 100183

Published: Aug. 3, 2024

Flooding presents a formidable challenge in the United States, endangering lives and causing substantial economic damage, averaging around $5 billion annually. Addressing this issue improving community resilience is imperative. This project employed machine learning techniques publicly available data to explore factors influencing flooding develop flood susceptibility maps at various spatial resolutions. Six algorithms, including Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), K-nearest neighbor (KNN), Adaptive Boosting (Ada Boost), Extreme Gradient (XGB) were used. Geospatial datasets comprising thirteen predictor variables 1528 inventory collected since 1996 analyzed. The are rainfall, elevation, slope, aspect, flow direction, accumulation, Topographic Wetness Index (TWI), distance from nearest stream, evapotranspiration, land cover, impervious surface, surface temperature, hydrologic soil group. Five hundred twenty-eight non-flood points randomly created using stream buffer for two scenarios. A total of 2964 classified into flooded (1) non-flooded (0) categories used as target. Overall, testing results showed that XGB RF models performed relatively well both cases over multiple resolutions compared other models, with an accuracy ranging 0.82 0.97. Variable importance analysis depicted such streams, type, surfaces significantly affected prediction, suggesting strong association underlying driving process. improved performance variation susceptible areas across scenarios considering appropriate non-flooding training critical developing flood-susceptibility models. Furthermore, tree-based ensemble algorithms like XG boost stack generalization approach can help achieve robustness model where being evaluated.

Language: Английский

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Implication of novel hybrid machine learning model for flood subsidence susceptibility mapping: A representative case study in Saudi Arabia

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 630, P. 130692 - 130692

Published: Jan. 24, 2024

Language: Английский

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