Preventing landslides in times of rainfall: case study and FEM analyses

Moayedi, Hossein; Huat, Bujang B. K.; Ali, Thamer Ahmad Mohammad; Asadi, Afshin; Moayedi, Fatemeh; Mokhberi, Mehdi

doi:10.1108/09653561111126067

Cited by 19 publications

(11 citation statements)

References 11 publications

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“…It is constructed from some extremely connected units which aim to discern the nonlinear relationship between external inputs [42]. The main reason for selecting the ANN as the basic model of this study was its high capability in various engineering simulations [43,44,45,46,47]. Figure 4 illustrates the structure of a commonly held type of neural networks named multilayer perceptron (MLP).…”

Section: Methodsmentioning

confidence: 99%

A Novel Swarm Intelligence—Harris Hawks Optimization for Spatial Assessment of Landslide Susceptibility

Bui

Moayedi

Kalantar

et al. 2019

Sensors

116

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In this research, the novel metaheuristic algorithm Harris hawks optimization (HHO) is applied to landslide susceptibility analysis in Western Iran. To this end, the HHO is synthesized with an artificial neural network (ANN) to optimize its performance. A spatial database comprising 208 historical landslides, as well as 14 landslide conditioning factors—elevation, slope aspect, plan curvature, profile curvature, soil type, lithology, distance to the river, distance to the road, distance to the fault, land cover, slope degree, stream power index (SPI), topographic wetness index (TWI), and rainfall—is prepared to develop the ANN and HHO–ANN predictive tools. Mean square error and mean absolute error criteria are defined to measure the performance error of the models, and area under the receiving operating characteristic curve (AUROC) is used to evaluate the accuracy of the generated susceptibility maps. The findings showed that the HHO algorithm effectively improved the performance of ANN in both recognizing (AUROCANN = 0.731 and AUROCHHO–ANN = 0.777) and predicting (AUROCANN = 0.720 and AUROCHHO–ANN = 0.773) the landslide pattern.

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Section: Methodsmentioning

confidence: 99%

A Novel Swarm Intelligence—Harris Hawks Optimization for Spatial Assessment of Landslide Susceptibility

Bui

Moayedi

Kalantar

et al. 2019

Sensors

116

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show abstract

“…%) in the concentrated solutions of PVA were very small. It is known that the zeta potential of many polyelectrolytes can be significantly diminished in the presence of PVA, especially in alkaline range [51]. We can therefore assume that slightly smaller compatibility of PVA with GuAr in the alkaline range of pH is the result of the decrease in the zeta potential of GuAr at pH 10.…”

Section: Properties Of Pvacgs Formed From the Feeds With Alkaline Ph mentioning

confidence: 95%

Wide-Pore Cryogels Prepared Using the Combination of Liquid-Liquid Phase Separation and Cryotropic Gel-Formation Processes

Lozinskiy¹,

Antonov²,

Damshkaln³

et al. 2015

Vestn. Volgogr. gos. univ., Ser. 10. Innov. deiat.

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“…The risk of LS and its vulnerability in the European mountainous region were deliberated using various models used in different researcher's work [9,10]. Some notable LS modeling works have been done using the hybrid methods [22], explicit deep learning neural network model [23], artificial neural network model [24,25], adaptive neuro-fuzzy inference system (ANFIS) multiple meta-heuristic model [27].…”

Section: Introductionmentioning

confidence: 99%

Geo-spatial approach-based landslide susceptibility mapping using analytical hierarchical process, frequency ratio, logistic regression and their ensemble methods

2020

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Kurseong municipality and its surrounding hill slope are famous high altitudinal residential areas in the Darjeeling Himalayan region of India. In Darjeeling Himalayas, huge landslides occur in the rainy season every year. This paper was aimed to delineate landslide susceptibility zone (LSZ) in Kurseong and its surrounding hill slope in Darjeeling Himalayas. Nine landslide inducing parameters (i.e., slope, altitude, rainfall, geological structure, distance from river channels, distance from lineament, soil type or characteristics, land-use/land-cover and aspect) were used for mapping LSZ applying analytic hierarchy process (AHP), frequency ratio (FR), binary logistic regression (BLR) models and their ensemble (AHP-FR; AHP-BLR; and FR-BLR) with the aid of ArcGIS, SPSS and ERDAS software. The landslide susceptible maps produced in GIS environment by the AHP, FR, BLR and their ensemble models were classified into five susceptibility classes such as very low, low, moderate, high and very high. The area under curve (AUC) of receiver operating characteristics (ROC) and kappa statistics were used to analyze the accuracy of the LSZ maps. The AUC values of prepared maps were 78.86% (AHP), 80.22% (FR), 80.67% (BLR), 83.44% (AHP-FR), 84.39% (AHP-BLR) and 84.73% (FR-BLR). Overall kappa statistics were 0.789 (AHP), 0.812 (FR), 0.799 (BLR), 0.837 (AHP-FR), 0856 (AHP-BLR) and 0.868 (FR-BLR), indicating the good accuracy of the models. The mapping of LSZ will be useful for land-use planning and future landslide hazard mitigation strategies.

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Preventing landslides in times of rainfall: case study and FEM analyses

Cited by 19 publications

References 11 publications

A Novel Swarm Intelligence—Harris Hawks Optimization for Spatial Assessment of Landslide Susceptibility

A Novel Swarm Intelligence—Harris Hawks Optimization for Spatial Assessment of Landslide Susceptibility

Wide-Pore Cryogels Prepared Using the Combination of Liquid-Liquid Phase Separation and Cryotropic Gel-Formation Processes

Geo-spatial approach-based landslide susceptibility mapping using analytical hierarchical process, frequency ratio, logistic regression and their ensemble methods

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