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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Enhancing flood-prone area mapping: fine-tuning the K-nearest neighbors (KNN) algorithm for spatial modelling

International Journal of Digital Earth, Journal Year: 2024, Volume and Issue: 17(1)

Published: March 4, 2024

This study focuses on determining the optimal distance metric in K-Nearest Neighbors (KNN) algorithm for spatial modelling of floods. Four metrics KNN algorithm, namely KNN-Manhattan, KNN-Minkowski, KNN-Euclidean, and KNN-Chebyshev, were utilized flood susceptibility mapping (FSM) Estahban, Iran. A database comprising 509 occurrence points extracted from satellite images 12 factors influencing floods was created analysis. The particle swarm optimization (PSO) employed hyperparameter feature selection, considering eight influential as inputs. results revealed that KNN-Manhattan exhibited superior accuracy (root mean squared error (RMSE) = 0.169, absolute (MAE) 0.051, coefficient determination (R2) 0.884, area under curve (AUC) 0.94) compared with other algorithms identifying flood-prone areas. KNN-Minkowski followed closely, an RMSE 0.175, MAE 0.056, R2 0.876, AUC 0.939. KNN-Euclidean achieved 0.183, 0.061, 0.842, 0.929, whereas KNN-Chebyshev 0.198, 0.075, 0.924.

Language: 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, Journal Year: 2024, Volume and Issue: 371, P. 123094 - 123094

Published: Nov. 2, 2024

Language: Английский

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Flood hazards and risk mapping using geospatial technologies in Jimma City, southwestern Ethiopia

Heliyon, Journal Year: 2023, Volume and Issue: 9(4), P. e14617 - e14617

Published: March 17, 2023

Cities in Ethiopia are suffering from unprecedented floods due to climate change and other anthropogenic activities. Failure include land use planning poorly designed urban drainage system aggravates the problem of flood. The integration geographic information system, multi-criteria evaluation (MCE) technique were used for flood hazards risk mapping. Five factors namely slope, elevation, density, cover, soil data Agrowing population increases victims during rainy season. Results revealed that about 25.16 24.38% study area is categorized under very high hazards, respectively. topographic nature hazards. increaseing number people living city has led conversion previously occupied green lands into residential areas risk. Flood mitigation measures such as better planning, public awareness creation on risks, delineation seasons, increasing greenery, strengthening river side development, watershed management catchment urgently required. findings this can provide a theoretical background prevention.

Language: Английский

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

Geocarto International, Journal Year: 2023, Volume and Issue: 38(1)

Published: Aug. 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.

Language: Английский

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Flood susceptibility mapping leveraging open-source remote-sensing data and machine learning approaches in Nam Ngum River Basin (NNRB), Lao PDR

Geomatics Natural Hazards and Risk, Journal Year: 2024, Volume and Issue: 15(1)

Published: May 28, 2024

Frequent floods caused by monsoons and rainstorms have significantly affected the resilience of human natural ecosystems in Nam Ngum River Basin, Lao PDR. A cost-efficient framework integrating advanced remote sensing machine learning techniques is proposed to address this issue enhancing flood susceptibility understanding informed decision-making. This study utilizes geo-datasets algorithms (Random Forest, Support Vector Machine, Artificial Neural Networks, Long Short-Term Memory) generate comprehensive maps. The results highlight Random Forest's superior performance, achieving highest train test Area Under Curve Receiver Operating Characteristic (AUROC) (1.00 0.993), accuracy (0.957), F1-score (0.962), kappa value (0.914), with lowest mean squared error (0.207) Root Mean Squared Error (0.043). Vulnerability particularly pronounced low-elevation low-slope southern downstream areas (Central part PDR). reveal that 36%–53% basin's total area highly susceptible flooding, emphasizing dire need for coordinated floodplain management strategies. research uses freely accessible data, addresses data scarcity studies, provides valuable insights disaster risk sustainable planning

Language: 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, Journal Year: 2024, Volume and Issue: 17(5), P. 4419 - 4440

Published: July 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.

Language: Английский

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Flood hazard mapping using GIS-based statistical model in vulnerable riparian regions of sub-tropical environment

Geocarto International, Journal Year: 2023, Volume and Issue: 38(1)

Published: Nov. 20, 2023

Floods are a recurrent natural calamity that presents substantial hazards to human lives and infrastructure. The study indicates significant proportion of the area, specifically 27.05%, is classified as moderate flood risk zone (FRZ), while 20.78% designated high or very FRZ. region's low FRZ at 52.17%. GIS-based AHP model demonstrated exceptional predictive precision, achieving score 0.749 (74.90%) determined by AUC-ROC, widely used statistical evaluation tool. current has identified areas with in affected CD blocks, which situated low-lying plains, regions gentle slopes, drainage density, TWI, NDVI, MNDWI, population intensive agricultural land. findings this research offer perspectives for decision-makers, city planners, emergency management agencies devising efficient measures mitigate risks.

Language: Английский

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Evaluation of urban flood susceptibility through integrated Bivariate statistics and Geospatial technology

Environmental Monitoring and Assessment, Journal Year: 2024, Volume and Issue: 196(6)

Published: May 9, 2024

Language: Английский

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A novel voting ensemble model empowered by metaheuristic feature selection for accurate flash flood susceptibility mapping

Geomatics Natural Hazards and Risk, Journal Year: 2024, Volume and Issue: 15(1)

Published: June 12, 2024

This study addresses the challenges of flash flood susceptibility mapping in Yemen's Qaa'Jahran Basin, characterized by complex terrain and limited hydro-meteorological data. To enhance predictive accuracy, we integrate metaheuristic feature selection with ensemble learning models. Initially, fifteen variables were retrieved using Geographic Information System (GIS) based remote sensing, setting stage for a novel algorithm. Then, Memo Search Algorithm (MSA), approach is proposed to efficiently reduce space. Through comprehensive comparisons established algorithms such as Artificial Bee Colony (ABC) Gray Wolf Optimizer (GWO), MSA refined selection, identifying 'elevation' 'distance streams' optimal factors. Statistical validations Friedman Wilcoxon signed-rank tests confirmed significant superiority over competing algorithms. Ensemble classifiers (bagging, boosting, stacking) then applied reduced Comprehensive evaluation revealed boosting outperformed traditional techniques reaching 98.75% 0.9896 Area Under Curve (AUC), 98.95% harmonic mean precision recall (F1-score). Precision high-risk zones was underlined via spatial prediction, confirming integrated framework's ability significantly improve forecast accuracy. The findings aid disaster management powerful geographic data-poor regions. framework adaptable globally flood-prone areas similar constraints. As climate change expected increase extreme rainfall events, communities will need robust data-driven methodologies mapping. Key recommendations current include investigating hybrid methods better inputs analyzing transferability across hydro-climatic zones.

Language: Английский

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