Spatial Landslide Susceptibility Assessment Based on Novel Neural-Metaheuristic Geographic Information System Based Ensembles

Moayedi, Hossein; Osouli, Abdolreza; Bui, Dieu Tien; Foong, Loke Kok

doi:10.3390/s19214698

Cited by 34 publications

(11 citation statements)

References 74 publications

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“…The basic concepts of this algorithm were based on biography topics, including species migration, species emergence, and extinction [84]. The BBO Algorithm starts by creating habitat, then the migration and mutation steps are performed [85].…”

Section: Biogeography-based Optimization (Bbo)mentioning

confidence: 99%

A New Modeling Approach for Spatial Prediction of Flash Flood with Biogeography Optimized CHAID Tree Ensemble and Remote Sensing Data

et al. 2020

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Flash floods induced by torrential rainfalls are considered one of the most dangerous natural hazards, due to their sudden occurrence and high magnitudes, which may cause huge damage to people and properties. This study proposed a novel modeling approach for spatial prediction of flash floods based on the tree intelligence-based CHAID (Chi-square Automatic Interaction Detector)random subspace, optimized by biogeography-based optimization (the CHAID-RS-BBO model), using remote sensing and geospatial data. In this proposed approach, a forest of tree intelligence was constructed through the random subspace ensemble, and, then, the swarm intelligence was employed to train and optimize the model. The Luc Yen district, located in the northwest mountainous area of Vietnam, was selected as a case study. For this circumstance, a flood inventory map with 1866 polygons for the district was prepared based on Sentinel-1 synthetic aperture radar (SAR) imagery and field surveys with handheld GPS. Then, a geospatial database with ten influencing variables (land use/land cover, soil type, lithology, river density, rainfall, topographic wetness index, elevation, slope, curvature, and aspect) was prepared. Using the inventory map and the ten explanatory variables, the CHAID-RS-BBO model was trained and verified. Various statistical metrics were used to assess the prediction capability of the proposed model. The results show that the proposed CHAID-RS-BBO model yielded the highest predictive performance, with an overall accuracy of 90% in predicting flash floods, and outperformed benchmarks (i.e., the CHAID, the J48-DT, the logistic regression, and the multilayer perception neural network (MLP-NN) models). We conclude that the proposed method can accurately estimate the spatial prediction of flash floods in tropical storm areas.

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Section: Biogeography-based Optimization (Bbo)mentioning

confidence: 99%

A New Modeling Approach for Spatial Prediction of Flash Flood with Biogeography Optimized CHAID Tree Ensemble and Remote Sensing Data

et al. 2020

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“…The MLP model is based on the back-propagation learning algorithm and has the ability to model nonlinear systems, which meets the complicated characteristics of landslides disaster systems. Therefore, it is suitable for the regional landslides susceptibility assessment (Cong 1998;Moayedi et al 2019;ThaiPham et al 2017;Zare et al 2012). Logistic regression model is used to build the probability model of landslides occurrences in the study area.…”

Section: Methodsmentioning

confidence: 99%

Regional landslide hazard assessment through integrating susceptibility index and rainfall process

Wang

Guo

et al. 2020

Nat Hazards

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Due to the difference of the spatial and temporal distribution of rainfall and the complex diversity of the disaster-prone environment (topography, geological, fault, and lithology), it is difficult to assess the hazard of landslides at the regional scale quantitatively only considering rainfall condition. Based on detailed landslide inventory and rainfall data in the hilly area in Sichuan province, this study analyzed the effects of both rainfall process and environmental factors on the occurrence of landslides. Through analyzing environmental factors, a landslide susceptibility index (LSI) was calculated using multiple layer perceptron (MLP) model to reflect the regional landslide susceptibility. Further, the characteristics of rainfall process and landslides were examined quantitatively with statistical analysis. Finally, a probability model integrating LSI and rainfall process was constructed using logistical regression analysis to assess the landslide hazard. Validation showed satisfactory results, and the inclusion of LSI effectively improved the accuracy of the landslide hazard assessment: Compared with only considering the rainfall process factors, the accuracy of the landslide prediction model both considering the rainfall process and landslide susceptibility is improved by 3%. These results indicate that an integration of susceptibility index and rainfall process is essential in improving the timeliness and accuracy of regional landslide early warning.

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“…The LCA tries to find the best-fitted solution to the problem by implementing an artificial league including schedule programming and determining the winner/looser teams. More information about the mentioned algorithms (e.g., mathematical relationships) are details in previous studies (for the ALO [119,120], BBO [121][122][123], DA [124,125], ES [126,127], IWO [128,129], and LCA [130][131][132]).…”

Section: Swarm-based Metaheuristic Ideasmentioning

confidence: 99%

Synthesizing Multi-Layer Perceptron Network with Ant Lion, Biogeography-Based, Dragonfly Algorithm, Evolutionary Strategy, Invasive Weed, and League Champion Optimization Hybrid Algorithms in Predicting Heating Load in Residential Buildings

Moayedi¹,

Mosavi²

2021

Preprint

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: The significance of heating load (HL) accurate approximation is the primary motivation of this research to distinguish the most efficient predictive model among several neural-metaheuristic models. The proposed models are through synthesizing multi-layer perceptron network (MLP) with ant lion optimization (ALO), biogeography-based optimization (BBO), dragonfly algorithm (DA), evolutionary strategy (ES), invasive weed optimization (IWO), and league champion optimization (LCA) hybrid algorithms. Each ensemble is optimized in terms of the operating population. Accordingly, the ALO-MLP, BBO-MLP, DA-MLP, ES-MLP, IWO-MLP, and LCA-MLP presented their best performance for population sizes of 350, 400, 200, 500, 50, and 300, respectively. The comparison was carried out by implementing a ranking system. Based on the obtained overall scores (OSs), the BBO (OS = 36) featured as the most capable optimization technique, followed by ALO (OS = 27) and ES (OS = 20). Due to the efficient performance of these algorithms, the corresponding MLPs can be promising substitutes for traditional methods used for HL analysis.

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Spatial Landslide Susceptibility Assessment Based on Novel Neural-Metaheuristic Geographic Information System Based Ensembles

Cited by 34 publications

References 74 publications

A New Modeling Approach for Spatial Prediction of Flash Flood with Biogeography Optimized CHAID Tree Ensemble and Remote Sensing Data

A New Modeling Approach for Spatial Prediction of Flash Flood with Biogeography Optimized CHAID Tree Ensemble and Remote Sensing Data

Regional landslide hazard assessment through integrating susceptibility index and rainfall process

Synthesizing Multi-Layer Perceptron Network with Ant Lion, Biogeography-Based, Dragonfly Algorithm, Evolutionary Strategy, Invasive Weed, and League Champion Optimization Hybrid Algorithms in Predicting Heating Load in Residential Buildings

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