Flood Susceptibility Mapping Using Remote Sensing and Integration of Decision Table Classifier and Metaheuristic Algorithms DOI Open Access
Shavan Askar, Sajjad Zeraat Peyma, Mohanad Mohsen Yousef

et al.

Water, Journal Year: 2022, Volume and Issue: 14(19), P. 3062 - 3062

Published: Sept. 28, 2022

Flooding is one of the most prevalent types natural catastrophes, and it can cause extensive damage to infrastructure environment. The primary method flood risk management susceptibility mapping (FSM), which provides a quantitative assessment region’s vulnerability flooding. objective this study develop new ensemble models for FSM by integrating metaheuristic algorithms, such as genetic algorithms (GA), particle swarm optimization (PSO), harmony search (HS), with decision table classifier (DTB). proposed were applied in province Sulaymaniyah, Iraq. Sentinel-1 synthetic aperture radar (SAR) data satellite images used monitoring (on 27 July 2019), 160 occurrence locations prepared modeling. For training validation datasets, coupled 1 flood-influencing parameters (slope, altitude, aspect, plan curvature, distance from rivers, land cover, geology, topographic wetness index (TWI), stream power (SPI), rainfall, normalized difference vegetation (NDVI)). certainty factor (CF) approach was determine spatial association between effective floods, resulting weights employed modeling inputs. According pairwise consistency technique, NDVI altitude are significant factors area under receiver operating characteristic (AUROC) curve evaluate accuracy effectiveness models. DTB-GA model found be accurate (AUC = 0.889), followed DTB-PSO 0.844) DTB-HS 0.812). This research’s hybrid provide reliable estimate risk, maps early-warning control systems.

Language: Английский

Random forest and nature-inspired algorithms for mapping groundwater nitrate concentration in a coastal multi-layer aquifer system DOI
Quoc Bao Pham, Dang An Tran, Nam Thang Ha

et al.

Journal of Cleaner Production, Journal Year: 2022, Volume and Issue: 343, P. 130900 - 130900

Published: Feb. 12, 2022

Language: Английский

Citations

44
Explainable step-wise binary classification for the susceptibility assessment of geo-hydrological hazards DOI
Ömer Ekmekcioğlu, Kerim Koç

CATENA, Journal Year: 2022, Volume and Issue: 216, P. 106379 - 106379

Published: May 19, 2022

Language: Английский

Citations

44
Multi-hazard spatial modeling via ensembles of machine learning and meta-heuristic techniques DOI Creative Commons
Mojgan Bordbar, Hossein Aghamohammadi, Hamid Reza Pourghasemi

et al.

Scientific Reports, Journal Year: 2022, Volume and Issue: 12(1)

Published: Jan. 27, 2022

Abstract Considering the large number of natural disasters on planet, many areas in world are at risk these hazards; therefore, providing an integrated map as a guide for multiple hazards can be applied to save human lives and reduce financial losses. This study designed multi-hazard three important (earthquakes, floods, landslides) identify endangered Kermanshah province located western Iran using ensemble SWARA-ANFIS-PSO SWARA-ANFIS-GWO models. In first step, flood landslide inventory maps were generated at-risk areas. Then, occurrence places each hazard divided into two groups training susceptibility models (70%) testing (30%). Factors affecting hazards, including altitude, slope aspect, degree, plan curvature, distance rivers, roads, faults, rainfall, lithology, land use, used generate maps. The SWARA method was weigh subclasses influencing factors floods landslides. addition, peak ground acceleration (PGA) investigate earthquakes area. next ANFIS machine learning algorithm combination with PSO GWO meta-heuristic algorithms train data, separately hazards. predictive ability implemented validated receiver operating characteristics (ROC), root mean square error (RMSE), (MSE) methods. results showed that model had best performance generating ROC = 0.936, RMS 0.346, MSE 0.120. Furthermore, this excellent (ROC 0.894, 0.410, 0.168) map. Finally, PGA combined, (MHM) obtained Province. by managers planners practical sustainable development.

Language: Английский

Citations

41
A comparison of performance measures of three machine learning algorithms for flood susceptibility mapping of river Silabati (tropical river, India) DOI
Md Hasanuzzaman, Aznarul Islam, Biswajit Bera

et al.

Physics and Chemistry of the Earth Parts A/B/C, Journal Year: 2022, Volume and Issue: 127, P. 103198 - 103198

Published: July 11, 2022

Language: Английский

Citations

41
Flood Susceptibility Mapping Using Remote Sensing and Integration of Decision Table Classifier and Metaheuristic Algorithms DOI Open Access
Shavan Askar, Sajjad Zeraat Peyma, Mohanad Mohsen Yousef

et al.

Water, Journal Year: 2022, Volume and Issue: 14(19), P. 3062 - 3062

Published: Sept. 28, 2022

Flooding is one of the most prevalent types natural catastrophes, and it can cause extensive damage to infrastructure environment. The primary method flood risk management susceptibility mapping (FSM), which provides a quantitative assessment region’s vulnerability flooding. objective this study develop new ensemble models for FSM by integrating metaheuristic algorithms, such as genetic algorithms (GA), particle swarm optimization (PSO), harmony search (HS), with decision table classifier (DTB). proposed were applied in province Sulaymaniyah, Iraq. Sentinel-1 synthetic aperture radar (SAR) data satellite images used monitoring (on 27 July 2019), 160 occurrence locations prepared modeling. For training validation datasets, coupled 1 flood-influencing parameters (slope, altitude, aspect, plan curvature, distance from rivers, land cover, geology, topographic wetness index (TWI), stream power (SPI), rainfall, normalized difference vegetation (NDVI)). certainty factor (CF) approach was determine spatial association between effective floods, resulting weights employed modeling inputs. According pairwise consistency technique, NDVI altitude are significant factors area under receiver operating characteristic (AUROC) curve evaluate accuracy effectiveness models. DTB-GA model found be accurate (AUC = 0.889), followed DTB-PSO 0.844) DTB-HS 0.812). This research’s hybrid provide reliable estimate risk, maps early-warning control systems.

Language: Английский

Citations

41