Classification of potential landslides using the Shuttle Radar Topography Mission imagery in the Tulis Watershed, Indonesia

Tulis is one of the watersheds in the Mrica Reservoir Catchment Area in Indonesia. The Tulis Watershed has an area of 12,750 ha, which is dominated by hilly areas with areas below alluvial-colluvial. This study aimed to map the potential distribution of the landslides in the Tulis Watershed. As the Tulis Watershed has the potential for landslides, this study was conducted by using Shuttle Radar Topography Mission (SRTM) imagery year 2016. This study considered five aspects that affect landslides, namely: geological type, soil regolith depth, fault, slope, and soil texture. Areas in the Tulis Watershed were classified into five levels of landslide potential The following landslide classes and the area they cover were predicted after applying the formula: very low (0%), low (48%, 6,126 ha), moderate (51%, 6,548 ha), high (0.5%, 63 ha), and very high (0.1%, 13 ha). From the results of the level of potential landslides, several prevention and mitigation measures are recommended according to the level. For shallow landslide levels, it is recommended that relocation centers should be set up. In contrast, for those areas with very high landslide potential, it is necessary to mitigate and install Early Warning System (EWS) tools and prepare the community for adaptation

Systematic sample subdividing strategy for training landslide susceptibility models

© 2019 Elsevier B.V. Current practice in choosing training samples for landslide susceptibility modelling (LSM) is to randomly subdivide inventory information into training and testing samples. Where inventory data differ in distribution, the selection of training samples by a random process may cause inefficient training of machine learning (ML)/statistical models. A systematic technique may, however, produce efficient training samples that well represent the entire inventory data. This is particularly true when inventory information is scarce. This research proposed a systemic strategy to deal with this problem based on the fundamental distribution of probabilities (i.e. Hellinger) and a novel graphical representation of information contained in inventory data (i.e. inventory information curve, IIC). This graphical representation illustrates the relative increase in available information with the growth of the training sample size. Experiments on a selected dataset over the Cameron Highlands, Malaysia were conducted to validate the proposed methods. The dataset contained 104 landslide inventories and 7 landslide-conditioning factors (i.e. altitude, slope, aspect, land use, distance from the stream, distance from the road and distance from lineament) derived from a LiDAR-based digital elevation model and thematic maps acquired from government authorities. In addition, three ML/statistical models, namely, k-nearest neighbour (KNN), support vector machine (SVM) and decision tree (DT), were utilised to assess the proposed sampling strategy for LSM. The impacts of model's hyperparameters, noise and outliers on the performance of the models and the shape of IICs were also investigated and discussed. To evaluate the proposed method further, it was compared with other standard methods such as random sampling (RS), stratified RS (SRS) and cross-validation (CV). The evaluations were based on the area under the receiving characteristic curves. The results show that IICs are useful in explaining the information content in the training subset and their differences from the original inventory datasets. The quantitative evaluation with KNN, SVM and DT shows that the proposed method outperforms the RS and SRS in all the models and the CV method in KNN and DT models. The proposed sampling strategy enables new applications in landslide modelling, such as measuring inventory data content and complexity and selecting effective training samples to improve the predictive capability of landslide susceptibility models

The effective use of technology and digitalization in Disaster Management in Malaysia.

The study will review the literature about disaster management in the Scopus database in 10 years from 2009 to 2018. A review will be done systematically to find out the direction of published and highly cited literature in the policymaking and problem solving after and post-natural disaster in Malaysia. After the complete assessment and filtration of the sheet, only 52 studies are left for the further process. irrelevant literature is screen out and relevant research is analyzed for further process. flood management and landslides are widely discussed challenges and problems in Malaysia. But the flood hazard management in Malaysia is developing rapidly, although the country is a new industrial developed country. The growth and development of social pace, fast-growth political and economic changes and that also charging up the pace of physical and environmental changes. Landslides are also growing with the period in Malaysia and some parts of the country are victims near the residential areas during the monsoon period. It's alarming that the residential areas are in the potential risk of landslides and the highways in some parts of the mountain area of the country

Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling-Narayanghat road section in Nepal Himalaya

Landslide susceptibility maps are vital for disaster management and for planning development activities in the mountainous country like Nepal. In the present study, landslide susceptibility assessment of Mugling-Narayanghat road and its surrounding area is made using bivariate (certainty factor and index of entropy) and multivariate (logistic regression) models. At first, a landslide inventory map was prepared using earlier reports and aerial photographs as well as by carrying out field survey. As a result, 321 landslides were mapped and out of which 241 (75 %) were randomly selected for building landslide susceptibility models, while the remaining 80 (25 %) were used for validating the models. The effectiveness of landslide susceptibility assessment using GIS and statistics is based on appropriate selection of the factors which play a dominant role in slope stability. In this case study, the following landslide conditioning factors were evaluated: slope gradient; slope aspect; altitude; plan curvature; lithology; land use; distance from faults, rivers and roads; topographic wetness index; stream power index; and sediment transport index. These factors were prepared from topographic map, drainage map, road map, and the geological map. Finally, the validation of landslide susceptibility map was carried out using receiver operating characteristic (ROC) curves. The ROC plot estimation results showed that the susceptibility map using index of entropy model with AUC value of 0.9016 has highest prediction accuracy of 90.16 %. Similarly, the susceptibility maps produced using logistic regression model and certainty factor model showed 86.29 and 83.57 % of prediction accuracy, respectively. Furthermore, the ROC plot showed that the success rate of all the three models performed more than 80 % accuracy (i.e. 89.15 % for IOE model, 89.10 % for LR model and 87.21 % for CF model). Hence, it is concluded that all the models employed in this study showed reasonably good accuracy in predicting the landslide susceptibility of Mugling-Narayanghat road section. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation purpose.ArticleNATURAL HAZARDS. 65(1):135-165 (2013)journal articl

Integration of remotely sensed soil sealing data in landslide susceptibility mapping

Soil sealing is the destruction or covering of natural soils by totally or partially impermeable artificial material. ISPRA (Italian Institute for Environmental Protection Research) uses different remote sensing techniques to monitor this process and updates yearly a national-scale soil sealing map of Italy. In this work, for the first time, we tried to combine soil sealing indicators as additional parameters within a landslide susceptibility assessment. Four new parameters were derived from the raw soil sealing map: Soil sealing aggregation (percentage of sealed soil within each mapping unit), soil sealing (categorical variable expressing if a mapping unit is mainly natural or sealed), urbanization (categorical variable subdividing each unit into natural, semi-urbanized, or urbanized), and roads (expressing the road network disturbance). These parameters were integrated with a set of well-established explanatory variables in a random forest landslide susceptibility model and different configurations were tested: Without the proposed soil-sealing-derived variables, with all of them contemporarily, and with each of them separately. Results were compared in terms of AUC(area under receiver operating characteristics curve, expressing the overall effectiveness of each configuration) and out-of-bag-error (estimating the relative importance of each variable). We found that the parameter "soil sealing aggregation" significantly enhanced the model performances. The results highlight the potential relevance of using soil sealing maps on landslide hazard assessment procedures

GEOSPATIAL APPROACH FOR LANDSLIDE ACTIVITY ASSESSMENT AND MAPPING BASED ON VEGETATION ANOMALIES

Remote sensing has been widely used for landslide inventory mapping and monitoring. Landslide activity is one of the important parameters for landslide inventory and it can be strongly related to vegetation anomalies. Previous studies have shown that remotely sensed data can be used to obtain detailed vegetation characteristics at various scales and condition. However, only few studies of utilizing vegetation characteristics anomalies as a bio-indicator for landslide activity in tropical area. This study introduces a method that utilizes vegetation anomalies extracted using remote sensing data as a bio-indicator for landslide activity analysis and mapping. A high-density airborne LiDAR, aerial photo and satellite imagery were captured over the landslide prone area along Mesilau River in Kundasang, Sabah. Remote sensing data used in characterizing vegetation into several classes of height, density, types and structure in a tectonically active region along with vegetation indices. About 13 vegetation anomalies were derived from remotely sensed data. There were about 14 scenarios were modeled by focusing in 2 landslide depth, 3 main landslide types with 3 landslide activities by using statistical approach. All scenarios show that more than 65% of the landslides are captured within 70% of the probability model indicating high model efficiency. The predictive model rate curve also shows that more than 45% of the independent landslides can be predicted within 30% of the probability model. This study provides a better understanding of remote sensing data in extracting and characterizing vegetation anomalies induced by hillslope geomorphology processes in a tectonically active region in Malaysia

Gully erosion susceptibility mapping using four machine learning methods in Luzinzi watershed, eastern Democratic Republic of Congo

peer reviewedSoil erosion by gullying causes severe soil degradation, which in turn leads to severe socio-economic and environmental damages in tropical and subtropical regions. To mitigate these negative effects and guarantee sustainable management of natural resources, gullies must be prevented. Gully management strategies start by devising adequate assessment tools and identification of driving factors and control measures. To achieve this, machine learning methods are essential tools to assist in the identification of driving factors to implement site-specific control measures. This study aimed at assessing the effectiveness of four machine learning methods (Random Forest (RF), Maximum of Entropy (MaxEnt), Artificial Neural Network (ANN), and Boosted Regression Tree (BRT)) to identify gully's driving factors, and predict gully erosion susceptibility in the Luzinzi watershed, in eastern Democratic Republic of Congo (DRC)

Landslide susceptibility mapping using remote sensing data and geographic information system-based algorithms

Whether they occur due to natural triggers or human activities, landslides lead to loss of life and damages to properties which impact infrastructures, road networks and buildings. Landslide Susceptibility Map (LSM) provides the policy and decision makers with some valuable information. This study aims to detect landslide locations by using Sentinel-1 data, the only freely available online Radar imagery, and to map areas prone to landslide using a novel algorithm of AB-ADTree in Cameron Highlands, Pahang, Malaysia. A total of 152 landslide locations were detected by using integration of Interferometry Synthetic Aperture RADAR (InSAR) technique, Google Earth (GE) images and extensive field survey. However, 80% of the data were employed for training the machine learning algorithms and the remaining 20% for validation purposes. Seventeen triggering and conditioning factors, namely slope, aspect, elevation, distance to road, distance to river, proximity to fault, road density, river density, Normalized Difference Vegetation Index (NDVI), rainfall, land cover, lithology, soil types, curvature, profile curvature, Stream Power Index (SPI) and Topographic Wetness Index (TWI), were extracted from satellite imageries, digital elevation model (DEM), geological and soil maps. These factors were utilized to generate landslide susceptibility maps using Logistic Regression (LR) model, Logistic Model Tree (LMT), Random Forest (RF), Alternating Decision Tree (ADTree), Adaptive Boosting (AdaBoost) and a novel hybrid model from ADTree and AdaBoost models, namely AB-ADTree model. The validation was based on area under the ROC curve (AUC) and statistical measurements of Positive Predictive Value (PPV), Negative Predictive Value (NPV), sensitivity, specificity, accuracy and Root Mean Square Error (RMSE). The results showed that AUC was 90%, 92%, 88%, 59%, 96% and 94% for LR, LMT, RF, ADTree, AdaBoost and AB-ADTree algorithms, respectively. Non-parametric evaluations of the Friedman and Wilcoxon were also applied to assess the models’ performance: the findings revealed that ADTree is inferior to the other models used in this study. Using a handheld Global Positioning System (GPS), field study and validation were performed for almost 20% (30 locations) of the detected landslide locations and the results revealed that the landslide locations were correctly detected. In conclusion, this study can be applicable for hazard mitigation purposes and regional planning

Modelling landslide sediment and hazard cascades with limited data availability following large typhoon events in the Philippines

Despite landslides acting as a dominant source of sediment within river catchments, the impact of sediment transport and delivery following an extreme weather event on channel geomorphological change is not yet well understood. This research builds on existing studies by investigating the Abuan River catchment in the Philippines following Typhoon Lawin in 2016 and Typhoon Kammuri in 2019. Each event triggered thousands of landslides varying in magnitude, of which a large proportion following Typhoon Lawin produced landslide runouts connecting to the channel network. When considering the associated uncertainty and data availability, Typhoon Lawin was selected for further investigation of the impact of landslide sediment input on changes in channel geomorphology. Traditionally, geomorphic channel change is determined through calculated change in active channel width which is assessed alongside stream power across the catchment. Stream power alone failed to explain changes in lateral channel change observed from satellite imagery across the Abuan River catchment. This research builds on existing studies to incorporate the influence of landslide sediment fluxes through the use of r.avaflow, a computational multi-phase mass simulation model. With a digital elevation model only available prior to the landslide event, methods used in previous studies were unattainable and therefore an empirical formula was used to derive landslides depths to be input into the model. Additional key parameters were based on a previous study also conducted in the Philippines or calibrated to observed channel changes. Modelled results with the inclusion of landslide sediment, demonstrated high consistency with lateral channel changed observed with large depths of deposition and a dominance of erosion upstream. Sensitivity analysis alongside a test run without the inclusion of landslides validated the choice of parameter values used. Results highlighted the importance of a multi-phase model approach to simulate channel morphological change in mountainous and tropical catchments with high rates of landslide sediment delivery. This study improves the understanding of geomorphic hazard following a typhoon event by integrating the impact of landslide sediment influx into existing understanding of processes influencing of geomorphic channel change