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This paper investigates the climate change influence on landslide susceptibility mapping (LSM) using a case study conducted on Penang Island in Malaysia, a region prone to landslides. This study was carried out due to limited research assessing the climate change effect on LSM, considering rainfall and temperature. The study employs climate factors, including temperature and rainfall, alongside 12 causative factors in a Support Vector Machine (SVM) model to develop LSM. The Statistical Downscaling Model (SDSM) is used to derive climate change projections under two Representative Concentration Pathways (RCP) scenarios (RCP4.5 and RCP8.5). Data preparation and normalization are performed using ArcGIS 10.7. Based on the results, future annual rainfall and daily temperatures are expected to rise under both scenarios, with RCP8.5 exhibiting more significant climatic changes. The LSM zonation is impacted more significantly under RCP8.5 due to the severity of climate change. LSM under an observation period achieves the best results (area under the curve (AUC) = 85.75, average statistical index (SI) = 94.48%, kappa = 0.885), followed by LSM under RCP4.5 (AUC = 84.38, average SI = 93.54%, kappa = 0.865) and LSM under RCP8.5 (AUC = 84.13, average SI = 93.34%, kappa = 0.860), demonstrating their reliability and adequate performance. These LSMs can assist local authorities in designating critical areas for monitoring and implementing an early-warning system to respond more effectively to landslide risks caused by climate change. However, the study's limitation is considering only two climate scenarios (RCP4.5 and RCP8.5). Future research should encompass a broader range of climate scenarios to develop the most reliable LSM, given the high uncertainty associated with climate change. • Climate change was projected using the SDSM model. • Climate change has more changes under RCP8.5. • Climate change, directly and indirectly, affect slope stability. • LHM was developed using the SVM model under the climate change effect. • Climate change has a significant impact on landslide distribution zoning in LHM. [ABSTRACT FROM AUTHOR]

Tropical alluvial soil has distinctive physical characteristics when compared to common sedimentary soils. Ignoring such distinctive characteristics might lead to geotechnical problems that require a thorough re-investigation. Foundation failures have been identified along some river banks of tropical alluvial soil deposits in Sarawak, Malaysia, which were speculated to be due to cyclic loading effects. This paper presents preliminary results on tropical alluvial soil engineering behaviors from simple shear cyclic loading tests. Specimens measuring 50 mm in diameter and 16 mm in height were obtained from three undisturbed samples. They were sheared using an Electro-Mechanical Dynamic Cyclic Simple Shear (EMDCSS) for thirty cycles at three different target displacements. The results showed hysteresis loops, where the shear stress decreases as the cycle increases. Excess pore water pressures develop during shearing, which correspond to the target displacement levels. The damping ratio increases as the shear strain increases but decreases as the cycle increases. The normalized shear modulus decreases as both the shear strain and loading cycle increase. A comparison with previous literature on cohesive soils shows that the tropical alluvial soil has a lower damping ratio and normalized shear modulus, which range from 3.3% to 9% and 0.0004 to 0.01, respectively. The findings indicate that the tropical alluvial soil has a potential reduction in strength when subjected to continuous cyclic loading. The paper provides a better understanding for academics and engineering professionals before conducting design or remedial work in the tropical alluvial soil environment. • Cyclic simple shear tests on tropical alluvial soil showed strength reduction. • Tropical alluvial soil has low normalized shear modulus and damping ratios. • Tropical alluvial soil has distinctive behavior than common sedimentary soils. [ABSTRACT FROM AUTHOR]

Tropical country such as Malaysia experienced high intensity and long period of rainfall, which is the main factor of slope failure. The increasing of seepage subjected by rainfall not only distress suction distribution, but also affects hydraulic properties of soil lead to the decreasing of soil strength. This also leads to soil slope failure. The main hydraulic properties for soils are Soil-water characteristic curve (SWCC) and hydraulic conductivity, k. The relationship of these parameters is highly influenced by tropical soil, thus contributes to soil instability phenomenon. The main objective of this paper is to study the relationship between soils' hydraulic properties and particle size distribution (PSD). Four types of soils investigated in this research include one sample taken from Universiti Kebangsaan Malaysia (UKM)'s reserved forest, and three samples were commercially obtained. Other than soil basic properties, pressure plate extractor and permeability tests were conducted for SWCC and permeability, respectively. Tests show that different types of soils demonstrate different characteristic of SWCC and hydraulic conductivity. Result also shows that PSD has the influence and affect the result of hydraulic properties of soils. This study is expected to enhance the understanding of complex phenomenon of residual soil instability. • Infiltration of rainwater into soil is a main cause of slope instability. • Tackle this issue by studying relationship of hydraulic properties of soils. • Result shows that PSD has the influence and effect to hydraulic properties of soils. [ABSTRACT FROM AUTHOR]

Debris flow caused high number of deaths among many of geohazard events in Malaysia due to the characteristics of sudden outburst and high energy density. This paper reviewed the initial mechanism and characteristics of existing debris flow in Malaysia. Almost all debris flows occurrence were initiated on natural terrains as opposed to other forms of landslides. Most of the events were induced by the rainfall. This is due to the geographic locations of Malaysia that lies on the equator with tropical climates and received abundant rainfall of 2400 mm annually. Intense and heavy rainfall increases the water content and pore water pressures within the residual soil that could trigger landslides. Thus, an intense and prolonged rainfall has become the major triggering factor for debris flow. This review article focus on clarifies the characterization of historical debris flow events that had an impact the infrastructure in Malaysia. In summary, this review attempts to systematically summarize the existing research on the mechanism of debris flow in Malaysia to provide further reference on debris flow prevention. Comprehensive understanding of the mechanisms and the behaviour of the debris flow will assist to the fundamental hazard and provide proper planning for structural measures. • The initial mechanism and characteristics of existing debris flow in Malaysia. • The effects are more severe due to the climate change and human activities. • Formation of natural dam also can produce a disastrous debris flow. • Reality ofthe occurrence in Malaysia known as cascading geological process. [ABSTRACT FROM AUTHOR]

This paper utilized the Auto Regressive Integrated Moving Average (ARIMA) technique to forecast the near future rainfall using the long-term satellite rainfall data. At present, many of the future rainfall and climate forecast was conducted for a long-term period (>10 years) at the macroscale (∼1000 km and above) level. Those forecasts are inappropriate to meet the demand for the short-term action (1–3 years). In addition, the macroscale information is unsuitable and too general for the local administrative council and water resources stakeholders to make the adaptation plan, mitigation action, and preventive measures at smaller scale (10–50 km). Many developing countries were facing hydrological data conflict (scarcity, sparse, etc.) and utilizing the publicly accessed satellite data could be useful alternatives. Therefore, sound evidence is required to validate the hypothesis where the use of ARIMA and the long-term satellite rainfall data could provide near future rainfall forecast. Prior to that, the southern part of Johor in Malaysia is selected as the experimental site for this study. To perform the task, 204 of monthly gridded of satellite rainfall from 1998 to 2014 is utilized. In implementing the ARIMA, the standard seasonal lag based on local monsoon period is chosen. The auto-regression co-efficient is computed for each individual grid based on the time series of monthly rainfall, as well as the forecasted rainfall. To determine the ideal duration of historical rainfall for the best near-future forecasted rainfall, the rainfall datasets are divided into decadal and semi-decadal (epoch is every 5 years). The results showed that the satellite-based forecasted rainfall based on ARIMA (SAFORA) using half-decadal datasets able to forecast the 2015 rainfall at average temporal correlation of 0.703. Measurement biases range from 5 to 70%, depending on monsoon season. The SAFORA tends to overestimate low rainfall months. The output of this experiment proved that SAFORA able to forecast the monthly-scale rainfall at reliable accuracy and could be useful for hydrological data conflict situation catchment or location in the humid tropics. • The satellite forecasted rainfall using ARIMA was able to forecast the monthly rainfall with correlation of 0.703. • The best datasets to forecast near future of monthly rainfall for the study area is half-decade datasets. • Measurement bias range from 5 to 70% depending on monsoon season. • The satellite forecasted rainfall using ARIMA (SAFORA) tends to overestimate ground rainfall during low rainfall months. [ABSTRACT FROM AUTHOR]

This paper utilized the Auto Regressive Integrated Moving Average (ARIMA) technique to forecast the near future rainfall using the long-term satellite rainfall data. At present, many of the future rainfall and climate forecast was conducted for a long-term period (>10 years) at the macroscale (∼1000 km and above) level. Those forecasts are inappropriate to meet the demand for the short-term action (1–3 years). In addition, the macroscale information is unsuitable and too general for the local administrative council and water resources stakeholders to make the adaptation plan, mitigation action, and preventive measures at smaller scale (10–50 km). Many developing countries were facing hydrological data conflict (scarcity, sparse, etc.) and utilizing the publicly accessed satellite data could be useful alternatives. Therefore, sound evidence is required to validate the hypothesis where the use of ARIMA and the long-term satellite rainfall data could provide near future rainfall forecast. Prior to that, the southern part of Johor in Malaysia is selected as the experimental site for this study. To perform the task, 204 of monthly gridded of satellite rainfall from 1998 to 2014 is utilized. In implementing the ARIMA, the standard seasonal lag based on local monsoon period is chosen. The auto-regression co-efficient is computed for each individual grid based on the time series of monthly rainfall, as well as the forecasted rainfall. To determine the ideal duration of historical rainfall for the best near-future forecasted rainfall, the rainfall datasets are divided into decadal and semi-decadal (epoch is every 5 years). The results showed that the satellite-based forecasted rainfall based on ARIMA (SAFORA) using half-decadal datasets able to forecast the 2015 rainfall at average temporal correlation of 0.703. Measurement biases range from 5 to 70%, depending on monsoon season. The SAFORA tends to overestimate low rainfall months. The output of this experiment proved that SAFORA able to forecast the monthly-scale rainfall at reliable accuracy and could be useful for hydrological data conflict situation catchment or location in the humid tropics. • The satellite forecasted rainfall using ARIMA was able to forecast the monthly rainfall with correlation of 0.703. • The best datasets to forecast near future of monthly rainfall for the study area is half-decade datasets. • Measurement bias range from 5 to 70% depending on monsoon season. • The satellite forecasted rainfall using ARIMA (SAFORA) tends to overestimate ground rainfall during low rainfall months. [ABSTRACT FROM AUTHOR]

This paper discusses the applications of unmanned aerial vehicle (UAV) for slope mapping which located at Pahang Matriculation College, Malaysia. Mapping is very important to obtain the terrain profiles for study area. By using UAV and commercial software, important information of slopes including perimeter, area and volume will be determined. With the development of modern technology, the utilization of UAV to gather data for mapping becoming easier as it is quick, reliable, precise, cost-effective and also easily to operate. High imagery quality is essential for the effectiveness and nature of normal mapping output such as Digital Surface Model (DSM) and Digital Orthophoto. With the help of established software, the dimensions of selected study area can be determined easily which can be considered as the main interest in this study. In addition to that, the cross-section of selected area can be obtained which help to determine the highest and lowest point of each area together with the terrain profiles. These slope profiles are determined according to their respective axis (vertical and horizontal) since both axis give different terrain profiles for each slope. Other than that, this research was further extended by determining the potential slope hazard based on their respective angles. These determinations are very important as early precautionary steps for engineers and researchers to prevent from any danger, injuries and accidents that for worse may cause fatalities. • Mapping using (UAV) are quick, reliable, precise and cost-effective. • Huge amount of data can be obtained using unmanned aerial vehicle (UAV). • Map presented in Digital Orthophoto and Digital Surface Model (DSM). • UAV helps to determine slope dimension which are perimeter, area and volume of slope. • UAV helps to determine potential slope hazard based on critical angle of slope. [ABSTRACT FROM AUTHOR]