Flood risk assessment using deep learning integrated with multi-criteria decision analysis

Knowledge-Based Systems, Journal Year: 2021, Volume and Issue: 219, P. 106899 - 106899

Published: Feb. 26, 2021

Language: Английский

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Decision tree based ensemble machine learning approaches for landslide susceptibility mapping

Geocarto International, Journal Year: 2021, Volume and Issue: 37(16), P. 4594 - 4627

Published: March 5, 2021

The concept of leveraging the predictive capacity predisposing factors for landslide susceptibility (LS) modeling has been continuously improved in recent work focusing on computational and machine learning algorithms. This paper explores different approaches to LS modelling using artificial intelligence. key objective this study is estimate a map Taleghan-Alamut basin Iran Credal Decision Tree (CDT)-based (i.e. CDT-Bagging, CDT-Multiboost CDT-SubSpace) hybrid approaches, which are state-of-the-art soft computing that hardly ever utilized assessment LS. In study, we used eighteen (LPFs) considered be most important local morphological geo-environmental influencing occurrence landslides. We calculated significance each LPFs Random Forest Method. also employed Receiver Operating Characteristic curve, precision, performance, robustness measurement selection best-fitting models. results shows that, compared other models, excellent model perspective with an average area under curve (AUC) 0.993 based 4-fold cross-validation. We, therefore, consider models effective method improving spatial prediction where scarps or bodies not clearly identified during preparation inventory maps. Therefore, it will helpful preparing future maps mitigate damages.

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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Multi-hazard susceptibility mapping based on Convolutional Neural Networks

Geoscience Frontiers, Journal Year: 2022, Volume and Issue: 13(5), P. 101425 - 101425

Published: June 17, 2022

Multi-hazard susceptibility prediction is an important component of disasters risk management plan. An effective multi-hazard mitigation strategy includes assessing individual hazards as well their interactions. However, with the rapid development artificial intelligence technology, techniques based on machine learning has encountered a huge bottleneck. In order to effectively solve this problem, study proposes mapping framework using classical deep algorithm Convolutional Neural Networks (CNN). First, we use historical flash flood, debris flow and landslide locations Google Earth images, extensive field surveys, topography, hydrology, environmental data sets train validate proposed CNN method. Next, method assessed in comparison conventional logistic regression k-nearest neighbor methods several objective criteria, i.e., coefficient determination, overall accuracy, mean absolute error root square error. Experimental results show that outperforms algorithms predicting probability floods, flows landslides. Finally, maps three are combined create map. It can be observed from map 62.43% area prone hazards, while 37.57% harmless. hazard-prone areas, 16.14%, 4.94% 30.66% susceptible landslides, respectively. terms concurrent 0.28%, 7.11% 3.13% joint occurrence floods flow, respectively, whereas, 0.18% subject all hazards. The benefit engineers, disaster managers local government officials involved sustainable land mitigation.

Language: Английский

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Flash-flood hazard using deep learning based on H2O R package and fuzzy-multicriteria decision-making analysis

Journal of Hydrology, Journal Year: 2022, Volume and Issue: 609, P. 127747 - 127747

Published: March 24, 2022

Language: Английский

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