Landslide Susceptibility Model Using Artificial Neural Network (ANN) Approach in Langat River Basin, Selangor, Malaysia

Selamat, Siti Norsakinah; Majid, Nuriah Abd; Taha, Mohd Raihan; Osman, Ashraf S.

doi:10.3390/land11060833

Cited by 33 publications

(20 citation statements)

References 115 publications

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“…The perfect agreement in Kappa scores and higher AUC, sensitivity, and accuracy values confirmed F-AHP as the better model than the AHP model. The validation matrices such as sensitivity, accuracy, and the Kappa index have been employed by many researchers [135,136] for assessing the performance of maps.…”

Section: Discussionmentioning

confidence: 99%

Wildfire Risk Zone Mapping in Contrasting Climatic Conditions: An Approach Employing AHP and F-AHP Models

Sinha

Nikhil

Ajin

et al. 2023

Fire

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Wildfires are one of the gravest and most momentous hazards affecting rich forest biomes worldwide; India is one of the hotspots due to its diverse forest types and human-induced reasons. This research aims to identify wildfire risk zones in two contrasting climate zones, the Wayanad Wildlife Sanctuary in the Western Ghats and the Kedarnath Wildlife Sanctuary in the Himalayas, using geospatial tools, analytical hierarchy process (AHP), and fuzzy-AHP models to assess the impacts of various conditioning factors and compare the efficacy of the two models. Both of the wildlife sanctuaries were severely battered by fires in the past, with more than 100 fire incidences considered for this modeling. This analysis found that both natural and anthropogenic factors are responsible for the fire occurrences in both of the two sanctuaries. The validation of the risk maps, utilizing the receiver operating characteristic (ROC) method, proved that both models have outstanding prediction accuracy for the training and validation datasets, with the F-AHP model having a slight edge over the other model. The results of other statistical validation matrices such as sensitivity, accuracy, and Kappa index also confirmed that F-AHP is better than the AHP model. According to the F-AHP model, about 22.49% of Kedarnath and 17.12% of Wayanad fall within the very-high risk zones. The created models will serve as a tool for implementing effective policies intended to reduce the impact of fires, even in other protected areas with similar forest types, terrain, and climatic conditions.

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Section: Discussionmentioning

confidence: 99%

Wildfire Risk Zone Mapping in Contrasting Climatic Conditions: An Approach Employing AHP and F-AHP Models

Sinha

Nikhil

Ajin

et al. 2023

Fire

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“…The rock contact, the rock content greatly contribute to the stability coefficient of soil-rock nixture slopes (Wang et al, 2022a;Wang et al, 2022b;Wang et al, 2022c). If there are multicollinearities among the input parameters in machine learning, the accuracy of the prediction model can be affected (Hitouri et al, 2022;Selamat et al, 2022;Xia et al, 2022). Therefore, this study uses rock content as an input parameter instead of weight, cohesion, and internal friction angle.…”

Section: Sample Analysismentioning

confidence: 99%

Stability prediction for soil-rock mixture slopes based on a novel ensemble learning model

Zhang²,

Wang³

et al. 2023

Front. Earth Sci.

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Soil-rock mixtures are geological materials with complex physical and mechanical properties. Therefore, the stability prediction of soil-rock mixture slopes using machine learning methods is an important topic in the field of geological engineering. This study uses the soil-rock mixture slopes investigated in detail as the dataset. An intelligent optimization algorithm-weighted mean of vectors algorithm (INFO) is coupled with a machine learning algorithm. One of the new ensemble learning models, which named IN-Voting, is coupled with INFO and voting model. Twelve single machine learning models and sixteen novel IN-Voting ensemble learning models are built to predict the stability of soil-rock mixture slopes. Then, the prediction accuracies of the above models are compared and evaluated using three evaluation metrics: coefficient of determination (R2), mean square error (MSE), and mean absolute error (MAE). Finally, an IN-Voting ensemble learning model based on five weak learners is used as the final model for predicting the stability of soil-rock mixture slopes. This model is also used to analyze the importance of the input parameters. The results show that: 1) Among 12 single machine learning models for the stability prediction of soil-rock mixture slopes, MLP (Multilayer Perceptron) has the highest prediction accuracy. 2) The IN-Voting model has higher prediction accuracy than single machine learning models, with an accuracy of up to 0.9846) The structural factors affecting the stability of soil-rock mixture slopes in decreasing order are the rock content, bedrock inclination, slope height, and slope angle.

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“…In order to determine the weight of the characteristic, another 100 points were generated from ArcMap. This generation is taken into consideration to represent non-landslide events to avoid overfitting issues (Selamat et al, 2022). Hence, a total of 200 points were used in this analysis, randomly separated into 70% training set and 30% for testing.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Influencing Physical Characteristics of Landslides in Kuala Lumpur, Malaysia

Zulkafli¹,

Majid²,

Zakaria³

et al. 2023

JST

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Landslide is one of the natural disasters that commonly occurs in terrestrial environments with slopes throughout the world. Located among countries with tropical climates, the hot and humid conditions expose Kuala Lumpur, Malaysia, to the risk of landslides. This paper aims to delineate the influencing physical characteristics of landslide occurrences in Kuala Lumpur. In this study, a 100 landslides historical data set and eight landslide factors were obtained from proper field validation and maps provided by those concerned in the government, such as distance to roads, distance to streams, elevation, slope angle, curvature, slope aspect, land use, and lithology. These factors were processed using GIS as geospatial analysis provides a useful tool for planning, disaster management, and hazard mitigation. By using ArcMap 10.8.2, a GIS software, different spatial analyses in which maps for each physical factor were layered with landslide events distribution. The weights for each factor were determined using the ANN approach resulting in the slope angle having the highest relative importance with a 100.0% value. In comparison, 8.3% represents the slope aspect as the most insignificant factor out of the eight selected characteristics for this study area. Therefore, a proper perspective and a thorough understanding of the certain slope condition have to be established for future mitigation action to support the agenda of SDG 15.

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Landslide Susceptibility Model Using Artificial Neural Network (ANN) Approach in Langat River Basin, Selangor, Malaysia

Cited by 33 publications

References 115 publications

Wildfire Risk Zone Mapping in Contrasting Climatic Conditions: An Approach Employing AHP and F-AHP Models

Wildfire Risk Zone Mapping in Contrasting Climatic Conditions: An Approach Employing AHP and F-AHP Models

Stability prediction for soil-rock mixture slopes based on a novel ensemble learning model

Influencing Physical Characteristics of Landslides in Kuala Lumpur, Malaysia

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