Landslide occurrences increase alongside the urbanization rate in developing countries. Hence, the need tovisualize the distribution pattern of increases is essential for the management of landslide cases, especiallyin Malaysia. Thus, a landslide monitoring system is proposed for landslide risk areas using computer generatedmodeling to perform spatial distribution patterns, which is important for management andcontrol. The purpose of this study is to evaluate the pattern of distribution and determine whether it isclustered or dispersed. A total of 145 landslide incidents were distributed in Kuala Lumpur. This paper will be focusing on one of the spatial pattern analyses, which is the spatial mean centre of the landslideincidents. It is found that the distribution pattern for landslide events is clustered. Meanwhile, the z-scoreis -4.091522 and there is a less than 1% likelihood. The nearest neighbor ratio is 0.82. Further studies toidentify factors that contribute to landslide incidents in the urban Kuala Lumpur are required for landslidesmitigation in the future.
A landslide is one of the most notorious natural disasters, resulting in massive losses and significant damages. Thus, this paper aims to analyze the spatial heterogeneity of the influencing factors which later inspect the relationship between the factors and landslide occurrences. A total of 988 landslides historical data and eight landslide factors were obtained from proper field validation and maps provided by those concerned in the government, including distance to roads, distance to rivers, distance to faults, slope angle, curvature, slope aspect, land use, and lithology. Geographically Weighted Logistic Regression (GWLR) is introduced in this paper to carry out the local analysis, resulting in the slope angle and the slope aspect playing the most significant role in influencing landslides. The Akaike’s information criterion (AICc) of GWLR is 824.51 which has a lower value than the global regression represented as 906.09 revealing that GWLR is the best model. Other evaluation criteria such as deviance, local percent deviance explained (pdev), and Bayesian information criterion (BIC) also validate the significance of the GWLR model. The GWLR results show the degree of spatial variation in the relationship between landslides and the influencing factors in the study area as the coefficient values of every factor are inconsistent, providing a reference for managers to formulate targeted decision-making measures. In the meantime, urgent action to sustain this natural disaster as suggested in the SDG 13 has to be taken earnestly to avoid bigger impacts on both society and the environment.
Landslides are one of the common natural disasters involving mostly movement of soil surfaces associated with gravitational attraction. Their adverse losses and significant damage, which always result in at least 17% of casualties and billions of dollars of financial losses worldwide, have made landslides the third most notorious phenomenon devastating many parts of the world. Malaysia has had multiple landslide occurrences, particularly in highly urbanized areas, such as Penang Island, owing to the declining vegetation cover in hilly terrains. Thus, this study aims to delineate the spatial relationship variances between landslide occurrences and the influencing factors in the area of interest. Ten influencing factors considered, including distance to roads, distance to rivers, distance to faults, slope angle, slope aspect, curvature, rainfall annual average, lithology, soil series, and land use. In this study, we use a software (GWR 4.0) as a medium for the analysis processing, coupled with GIS. A local statistical technique, Geographically Weighted Logistic Regression (GWLR), is primacy in capturing the geographical variation of the model coefficients that considers non-stationary variables and models their relationships, as well as processes regression coefficients over space. Goodness-of-fit criteria were used to evaluate the GWLR model, namely AICc that decrease from 872.202167 to 800.856998. Bayesian Information Criterion (BIC) shows a decrease in value from 925.784185 to 945.196942. Likewise, deviance decreased from 849.931675 to 739.175630, while pdev increased from 0.379457 to 0.460321. These goodness-of-fit criteria values express GWLR as the best model for local measure. The variances in both local parameter estimates and the t-values (negative and positive values) show the level of significance for each landslide factor in influencing landslide occurrences across the study area. The results of the local parameter estimates and the t-values also show that the slope angle and the slope aspect spatially affect landslide occurrences across the study area. Therefore, a proper perspective and a thorough understanding of the certain slope condition must be established for future mitigation actions to support the agenda of SDG 15, which promotes resilience and disaster risk reduction.
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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