Risk assessment of flash flood and soil erosion impacts on electrical infrastructures in overcrowded mountainous urban areas under climate change

Reliability Engineering & System Safety, Journal Year: 2023, Volume and Issue: 236, P. 109302 - 109302

Published: April 14, 2023

Language: Английский

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Optimizing flood susceptibility assessment in semi-arid regions using ensemble algorithms: a case study of Moroccan High Atlas

Natural Hazards, Journal Year: 2024, Volume and Issue: 120(8), P. 7787 - 7816

Published: March 21, 2024

Abstract This study explores and compares the predictive capabilities of various ensemble algorithms, including SVM, KNN, RF, XGBoost, ANN, DT, LR, for assessing flood susceptibility (FS) in Houz plain Moroccan High Atlas. The inventory map past flooding was prepared using binary data from 2012 events, where “1” indicates a flood-prone area “0” non-flood-prone or extremely low area, with 762 indicating areas. 15 different categorical factors were determined selected based on importance multicollinearity tests, slope, elevation, Normalized Difference Vegetation Index, Terrain Ruggedness Stream Power Land Use Cover, curvature plane, profile, aspect, flow accumulation, Topographic Position soil type, Hydrologic Soil Group, distance river rainfall. Predicted FS maps Tensift watershed show that, only 10.75% mean surface predicted as very high risk, 19% 38% estimated respectively. Similarly, Haouz plain, exhibited an average 21.76% very-high-risk zones, 18.88% 18.18% low- very-low-risk zones applied algorithms met validation standards, under curve 0.93 0.91 learning stages, Model performance analysis identified XGBoost model best algorithm zone mapping. provides effective decision-support tools land-use planning risk reduction, across globe at semi-arid regions.

Language: Английский

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Quantitative improvement of streamflow forecasting accuracy in the Atlantic zones of Canada based on hydro-meteorological signals: A multi-level advanced intelligent expert framework

Ecological Informatics, Journal Year: 2024, Volume and Issue: 80, P. 102455 - 102455

Published: Jan. 4, 2024

Developing reliable streamflow forecasting models is critical for hydrological tasks such as improving water resource management, analyzing river patterns, and flood forecasting. In this research, the first time, an emerging multi-level TOPSIS (technique order preference by similarity to ideal solution) based hybridization comprised of Boruta classification regression tree (Boruta-CART) feature selection, multivariate variational mode decomposition (MVMD), a hybrid Convolutional Neural Network (CNN) Bidirectional Gated Recurrent Unit (CNN-BiGRU) deep learning was adopted multi-temporal (one three days ahead) forecast daily in Rivers Prince Edward Island, Canada. For aim, step, Boruta-CART selection technique determines most effective lagged components among all antecedent two-day information (i.e., t-1 t-2) hydro-meteorological features (from 2015 2020), including level, mean air temperature, heat degree days, total precipitation, dew point relative humidity Bear Winter Afterwards, (MVMD) decomposes input time series decrease complexity non-linearity non-stationary ones before feeding (DL) models. Here, CNN-GRU employed primary DL model, along with kernel extreme machine method (KELM), random function link (RVFL), CNN bidirectional recurrent neural network (CNN-BiRNN) comparative A scheme applying several performance measures like correlation coefficient (R), root square error (RMSE), reliability designed robustness assessment (MVM-CNN-BiGRU, MVM-CNN-BiRNN, MVM-RVFL, MVM-KELM) standalone The computational outcomes revealed that River, MVM-CNN-BiGRU, owing its best day ahead: score 1, R = 0.960, RMSE 0.098, 65.082; 0.999, 0.924, 0.33) outperformed other models, followed MVM-KELM, respectively. Moreover, MVM-CNN-BiGRU terms (one-day 0.890, 0.955, 0.274, 34.004; three-days 0.686, 0.330) superior provided expert system could be vital local decision-making process, absence modeling, during seasons reduce damage residential areas.

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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Comparison of the Fuzzy Analytic Hierarchy Process (F-AHP) and Fuzzy Logic for Flood Exposure Risk Assessment in Arid Regions

Hydrology, Journal Year: 2023, Volume and Issue: 10(7), P. 136 - 136

Published: June 26, 2023

Flood risk assessment is an important tool for urban planning, land development, and hydrological analysis. The flood risks are very high in arid countries due to the nature of rainfall resulting from thunderstorms cover, which comprises mostly dry soil. Several methods have been used assess risk, depending on various factors that affect likelihood occurrence. However, selection these weight assigned them remain rather arbitrary. This study assesses occurrence regions based soil type, precipitation, elevation, flow accumulation. Thematic maps aforementioned area were prepared using GIS. Fuzzy Analytic Hierarchy Process (F-AHP) was calculate occurrence, use exposure impact. Using map (i.e., probability) Fuzzy-AHP map, assessed. method applied Qatar as a case study. Results compared with those produced by fuzzy logic. To explore pairwise importance F-AHP, equal analysis performed. shows majority urbanized areas within high-risk zone, some smaller parts flood-risk area. country low-risk zone. Some areas, especially depressions, located intermediate-risk category. Comparison logic F-AHP showed both similarities differences zones. reveals probably more accurate than other it accounts higher variability.

Language: Английский

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An Improved Flood Susceptibility Assessment in Jeddah, Saudi Arabia, Using Advanced Machine Learning Techniques

Water, Journal Year: 2023, Volume and Issue: 15(14), P. 2511 - 2511

Published: July 9, 2023

The city of Jeddah experienced a severe flood in 2020, resulting loss life and damage to property. In such scenarios, forecasting model can play crucial role predicting events minimizing their impact on communities. proposed study aims evaluate the performance machine learning algorithms floods non-flood regions, including Gradient Boosting, Extreme AdaBoosting Gradient, Random Forest, Light Boosting Machine, using dataset from City, Saudi Arabia. This identified fourteen continuous parameters various classification variables assess correlation between these flooding incidents analyzed region. was measured matrices regression matrices. highest accuracy (86%) achieved by Forest classifier, lowest error rate (0.06) found with regressor machine. other also exceptional compared existing literature. results suggest that application significantly enhance prediction accuracy, enabling industries sectors make more informed decisions.

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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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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Ensemble machine learning models for predicting the CO2 footprint of GGBFS-based geopolymer concrete

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 472, P. 143463 - 143463

Published: Aug. 26, 2024

While geopolymer concrete (GPC) has gained popularity for its environmentally friendly attributes compared to ordinary Portland cement, the absence of a prediction model carbon footprint constituents presents challenges optimization within evolving industry.This study offers thorough CO 2 ground granulated blast-furnace slag (GGBFS)-based GPC, utilizing advanced AI techniques, including combination machine learning models and stacking ensembles.This research statistically examines crucial parameters responsible emissions in GGBFS-based GPC production, identifying factors like superplasticizer content, initial curing temperature, NaOH (dry) content as significant contributors.Emphasizing sustainability, advocates optimizing mixtures by considering ratio other activator materials.After rigorously evaluating 12 models, ensemble this identified M4-a Support Vector Regression (SVR) Neural Network (NN)-as weak Decision Tree (DT) meta-model, most effective predicting footprints.The choice M4 is supported various performance metrics such lowest Mean Squared Error 88.8 Root 9.42, alongside highest R , Adjusted Explained Variance scores, all approximately 0.95.Additional analyses, Euclidean distance Taylor diagrams, further substantiate selection M4.The findings have practical implications sustainable cleaner enabling businesses optimize GPC.

Language: Английский

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Interpretable flash flood susceptibility mapping in Yarlung Tsangpo River Basin using H2O Auto-ML

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 11, 2025

Flash flood susceptibility mapping is essential for identifying areas prone to flooding events and aiding decision-makers in formulating effective prevention measures. This study aims evaluate the flash Yarlung Tsangpo River Basin (YTRB) using multiple machine learning (ML) models facilitated by H2O automated ML platform. The best-performing model was used generate a map, its interpretability analyzed Shapley Additive Explanations (SHAP) tree interpretation method. results revealed that top four models, including both single ensemble demonstrated high accuracy tests. map generated eXtreme Randomized Trees (XRT) showed 8.92%, 12.95%, 15.42%, 31.34%, 31.37% of area exhibited very high, moderate, low, low susceptibility, respectively, with approximately 74.9% historical floods occurring classified as moderate susceptibility. SHAP plot identified topographic factors primary drivers floods, importance analysis ranking most influential such descending order DEM, wetness index, position normalized difference vegetation average multi-year precipitation. demonstrates benefits interpretable learning, which can provide guidance mitigation.

Language: Английский

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