Landslide Disaster Management Research Articles

Soil Vulnerability Analysis Using the Microtremor Method for Mapping the Landslide Susceptibility Area in Batu City, Indonesia

Based on Regional Agency for Disaster Management (BPBD) Batu City data, Indonesia, 36 landslides have been recorded in the last three years around the Payung area, where this area connects several regions around Batu City. So, it is necessary to map areas with potential ground movement as an initial step for the local government in formulating a landslide disaster management strategy. This research aims to map landslide susceptibility areas around Payung, Batu City, identified based on the soil vulnerability index value range in the peak ground acceleration (PGA) mapping results. The data was measured using the microtremor method. There were 22 measurement points in 3 zones with different heights, and the microtremor signal was recorded for 35-40 minutes at each point. The microtremor signal from seismic recording is composed of three components (NS, EW, and Up-Down), which are used to develop the Horizontal to Vertical Spectral Ratio (HVSR) technique, where the ratio value is obtained by comparing horizontal (H) components (HNS and HEW) to the vertical component (Up-Down (V)). The research results show that landslide susceptibility areas are located at points M10, M13, and M16, with a soil vulnerability index range of 21-40.1. When a landslide occurs in that area with high vulnerability, it will impact points along Payung Street at points M3–M9. The points with high soil vulnerability index values need special treatment to reduce the impact of landslides.

Detecting Coseismic Landslides in GEE Using Machine Learning Algorithms on Combined Optical and Radar Imagery

Landslides, resulting from disturbances in slope equilibrium, pose a significant threat to landscapes, infrastructure, and human life. Triggered by factors such as intense precipitation, seismic activities, or volcanic eruptions, these events can cause extensive damage and endanger nearby communities. A comprehensive understanding of landslide characteristics, including spatio-temporal patterns, dimensions, and morphology, is vital for effective landslide disaster management. Existing remote sensing approaches mostly use either optical or synthetic aperture radar sensors. Integrating information from both these types of sensors promises greater accuracy for identifying and locating landslides. This study proposes a novel approach, the ML-LaDeCORsat (Machine Learning-based coseismic Landslide Detection using Combined Optical and Radar Satellite Imagery), that integrates freely available Sentinel-1, Palsar-2, and Sentinel-2 imagery data in Google Earth Engine (GEE). The approach also integrates relevant spectral indices and suitable bands used in a machine learning-based classification of coseismic landslides. The approach includes a robust and reproducible training and validation strategy and allows one to choose between five classifiers (CART, Random Forest, GTB, SVM, and Naive Bayes). Using landslides from four different earthquake case studies, we demonstrate the superiority of our approach over existing solutions in coseismic landslide identification and localization, providing a GTB-based detection accuracy of 87–92%. ML-LaDeCORsat can be adapted to other landslide events (GEE script is provided). Transfer learning experiments proved that our model can be applied to other coseismic landslide events without the need for additional training data. Our novel approach therefore facilitates quick and reliable identification of coseismic landslides, highlighting its potential to contribute towards more effective disaster management.

Mitigation of Landslide Disaster Management in Senggigi Tourism Area in West Lombok Regency

Landslides are a frequent occurrence in the wet tropics, particularly in the Senggigi area of West Lombok Regency. Following landslide incidents on February 6, 2021, near the Sheraton Hotel, Alberto Restaurant, and a road in Senggigi on November 12, 2021, mitigation efforts have become imperative. This research aims to identify the causal and influencing factors, assess their impacts, and propose mitigation strategies. Data is collected through questionnaires and interviews, followed by validity and reliability testing. Results indicate that soil type (20.945%) and high rainfall (23.776%) are significant factors contributing to landslides. Consequently, specialized attention from relevant agencies is needed to address soil issues in Senggigi effectively.

Assessment of Rainfall-Induced Shallow Landslides in Kavre District, Nepal

Steep slopes and fragile geology make Nepal a landslide-prone country. Kavre district experiences a large number of shallow landslides in the monsoon season. This research aims to establish rainfall intensity relation with rainfall duration along with rainfall threshold for the initiation of shallow landslides in the Kavre district, Nepal. The six rain gauge stations' rainfall data in the Kavre district from 1987-2016 and landslide data from 1987-2013 were used for establishing rainfall thresholds. The rainfall that risks the landslide event was sorted and the regression analysis of the rainfall and landslide yielded a relationship I =86.738 *D-1.066 having a coefficient of determination R2 = 0.1257. The analysis also incorporates the drawing of the lower boundary which is the 48-h rainfall depth that indicates the rainfall threshold for the initiation of landslide in the Kavre district. The 48-hour rainfall intensity and rainfall depth at which the risk of landslide initiation is high in the Kavre district is 1.4 mm/h. and 67.18mm. Analysis of the antecedent rainfall and the landslide occurrence event indicated that the daily rainfall at failure conditions is better correlated with 3 days of cumulative rainfall at failure than with 5, 7, 10, 15, and 30 days. The study is significant for rainfall-induced landslide disaster management.

Rainfall-Induced Landslide Susceptibility Assessment and the Establishment of Early Warning Techniques at Regional Scale

This study builds upon deterministic evaluations of the extensive cumulative rainfall thresholds associated with shallow landslides in the Gaoping River Basin, with a specific focus on the necessary response times during typhoon and intense rainfall events. Following the significant impact of Typhoon Morakot on the Liugui area, our investigation enhances previous research by employing a downscaled approach. We aim to establish early warning models for village-level, intermediate-scale landslide cumulative rainfall thresholds and to create action thresholds for small-scale, key landslide-prone slopes. Our inquiry not only combines various analytical models but also validates their reliability through comprehensive case studies. Comparative analysis with the empirical values set by the Soil and Water Conservation Bureau (SWCB) and the National Center for Disaster Reduction (NCDR) provides a median response time of 6 h, confirming that our findings are consistent with the response time frameworks established by these institutions, thus validating their effectiveness for typhoon-related landslide alerts. The results not only highlight the reference value of applying downscaled cumulative rainfall thresholds at the village level but also emphasize the significance of the evaluated warning thresholds as viable benchmarks for early warnings in landslide disaster management during Taiwan’s flood and typhoon seasons. This research offers a refined methodological approach to landslide early warning systems and provides scientific support for decision making by local governments and disaster response teams.

Introduction pages, Nova Geodesia 3(4), 2023

Nova Geodesia (https://novageodesia.ro), Issue 4, Volume 3, 2023: The papers published in this issue represent interesting novelties in different topics of geodetic science or related fields. Nova Geodesia publishes significant papers in geodesy and close topics related to cartography, engineering projects and construction, planetology, hydrography, geography, sociodemographic factors, urban administration, planning and environment, landscape, biodiversity, and ecology. Among the exciting articles, we invite readers to find news about: Exploring distinctive morphological traits in Beilschmiedia mannii georeferenced by GPS: Implication for conservation; Effect of abiotic factors and seasonal influence on spider diversity in rice agroecosystem of Bargarh District, Odisha, India; The complementary role of indigenous knowledge systems in landslide disaster management in Kanungu District, Uganda; Uncodified system of animal health care in Eastern Himalaya and Indo-Burma hotspots of India: Plant use and their distribution; Responses of silver fir (Abies alba Mill.) seedlings from different geographical locations to low temperature stress; Locational variation in rhizospheric mycobiota associated with Elaeagnus latifolia, an endemic plant species of N.E. India.

GIS-based landslide susceptibility mapping using logistic regression, random forest and decision and regression tree models in Chattogram District, Bangladesh

The frequency of landslides and related economic and environmental damage has increased in recent decades across the hilly areas of the world, no exception is Bangladesh. Considering the first step in landslide disaster management, different methods have been applied but no methods found as best one. As a result, landslide assessment using different methods in different geographical regions has significant importance. The research aims to prepare and evaluate landslide susceptibility maps (LSMs) of the Chattogram district using three machine learning algorithms of Logistic Regression (LR), Random forest (RF) and Decision and Regression Tree (DRT). Sixteen landslide conditioning factors were determined considering topographic, hydro-climatic, geologic and anthropogenic influence. The landslide inventory database (255 locations) was randomly divided into training (80 %) and testing (20 %) sets. The LSMs showed that almost 9–12 % of areas of the Chattogram district are highly susceptible to landslides. The highly susceptible zones cover the Chattogram district's hill ranges where active morphological processes (erosion and denudation) are dominant. The ROC values for training data were 0.943, 0.917 and 0.947 and testing data were 0.963, 0.934 and 0.905 for LR, RF and DRT models, respectively. The accuracy is higher than the previous research in comparison to the extent of the study area and the size of the inventory. Among the models, LR showed the highest prediction rate and DRT showed the highest success rate. According to susceptibility zones, DRT is the more realistic model followed by LR. The maps can be applied at the local scale for landslide hazard management.

Landslide Inventory and Disaster Management Based on Community Participation for Sustainability Environment in the Volcanic Area

Landslides are still an essential problem for areas with rough morphology. There are several ways to map landslide vulnerability that can be used to reduce disaster risk. To produce maps with high accuracy is one of the challenges. This study proposes a community-based landslide inventory mapping approach. Communities are important in providing information about disaster events and are essential to disaster management. The aims of this study are 1) to conduct field investigations for the community for landslide inventory; 2) to identify and analyze the role of the community in disaster risk reduction for a sustainable environment. The method used in this research is using participatory mapping (P-GIS) and focus group discussion. The community is participatively asked to reconstruct past landslides. Community-based landslide data inventory using geospatial technology in the form of remote sensing imagery as support. The results of community participation showed that there were 138 landslides with translational and rotational landslide typologies. There is a community role in reducing the risk of landslides through the Taji Village Disaster Preparedness Community (SIBAT). The management model for land management carried out by the community is based on the principles of environmental sustainability in the form of easy application, local-based materials, local-based culture, and local-based knowledge, and provides sufficient economic income.

The complementary role of indigenous knowledge systems in landslide disaster management in Kanungu District, Uganda

This study aimed to investigate the use and effectiveness of Indigenous Knowledge Systems (IKS) in managing landslides in Kanungu District, Uganda. The study used the Likert scale and the multivariate probit (MVP) model and found that marginalization was a significant challenge facing local interventions in landslide disaster management. Factors such as farm size, credit availability, social group membership, access to extension services, farming experience, accessibility of weather and climatic information, and perception of climatic changes influenced the adoption of IKS, both positively and negatively. The study concludes that education programs should focus on farmer capacity building to mitigate landslide risks and emphasizes the application of IKS in landslide hazard management.

Landslide susceptibility assessment in Western Henan Province based on a comparison of conventional and ensemble machine learning

Landslide is a serious natural disaster next only to earthquake and flood, which will cause a great threat to people’s lives and property safety. The traditional research of landslide disaster based on experience driving or statistical model and its assessment results are subjective , difficult to quantify, and no pertinence. As a new research method for landslide susceptibility assessment, machine learning can greatly improve the landslide susceptibility model’s accuracy by constructing statistical models. Taking Western Henan for example, the study selected 16 landslide influencing factors such as topography, geological environment, hydrological conditions, and human activities. And 11 landslide factors with the most significant influence on the landslide were selected by the recursive feature elimination (RFE) method. There are five machine learning methods (Support Vector Machines (SVM), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Linear Discriminant Analysis (LDA)) were used to construct the spatial distribution model of landslide susceptibility. The models were evaluated by the receiver operating characteristic curve and statistical index. After analysis and comparison, the XGBoost model (AUC 0.8759) performed the best and was suitable for dealing with regression problems. The model had a high adaptability to landslide data. According to the landslide susceptibility map of the five models, the overall distribution can be observed. The extremely high and high susceptibility areas are distributed in the Funiu Mountain range in the southwest, the Xiaoshan Mountain range in the west, and the Yellow River basin in the north. These areas have large terrain fluctuations, complicated geological structural environments and frequent human engineering activities. The extremely high and highly prone areas were 12043.3 km² and 3087.45 km², accounting for 47.61% and 12.20% of the total area of the study area, respectively. Our study reflects the distribution of landslide susceptibility in western Henan Province, which provides a scientific basis for regional disaster warning, prediction, and resource protection. The study has important practical significance for subsequent landslide disaster management.

An Adaptive Identification Method for Potential Landslide Hazards Based on Multisource Data

The effectiveness of landslide disaster prevention depends largely on the quality of early identification of potential hazards, and how to comprehensively, deeply, and accurately identify such hazards has become a major difficulty in landslide disaster management. Existing deep learning methods for potential landslide hazard identification often use fixed-size window modeling and ignore the different window sizes required by landslides of different scales. To address this problem, we propose an adaptive identification method for potential landslide hazards based on multisource data. Taking Yongping County, China, as the study area, we create a multisource factor dataset based on the landslide disaster background in terms of topography, geology, human activities, hydrology, and vegetation as the sample for the identification model after processing. Moreover, we combine differential interferometric synthetic aperture radar (D-InSAR) and multitemporal InSAR (MT-InSAR) to process the surface deformation of the study area, and we measure the deformation richness based on the average of the pixel deformation difference within the current window of a pixel point in the image. Therefore, convolutional neural networks (CNNs) with different window sizes are adaptively selected. The results show that the precision of adaptive identification of potential landslide hazards in the study area is 85.30%, the recall is 83.03%, and the F1 score is 84.15%. The recognition rate for potential hazards reaches 80%, which is better than the fixed-window modeling result and proves the effectiveness of the proposed method. This method can help to improve intelligent identification systems for potential landslide hazards, and also contribute to the identification of other potential geological hazards, such as mudslides and collapses.

Sosoalisasi Pemanfaatan Rumput Vetiver (Chrysopogon zizanioides) Dalam Mitigasi Bencana Longsor di Desa Gelangsar, Kabupaten Lombok Barat

Gelangsar Village, West Lombok is one of the villages located in a hilly area which resulted in several points in the village of Gelangsar experiencing landslides. The topography which is hilly and steep, triggers landslides in the village of Gelangsar. In addition, the relatively high rainfall in the last few months has made the soil conditions more flexible. Areas that have the potential to cause landslides can be overcome by planting plants that can bind the soil tightly so that it can overcome the potential for landslides. Therefore, this service aims to socialize the use of vetiver grass as a bioengineering method in managing landslides in Gelangsar Village, Gunungsari, West Lombok. This Community Service activity uses the counseling method and the creation of a pilot project. Counseling was conducted to introduce vetiver grass plants and their cultivation techniques. Making a pilot project (pilot project) have been done in the homes of residents who are partners. The Pilot Project was created as a media for socialization and as a strategy for efficiency and effectiveness for target activities. The community of partners are willing to implement and develop vetiver grass as a biotechnology agent to prevent erosion and also as an economic opportunity. This programme has been able to provide knowledge in mitigation landslide disaster management activities independently. Planting vetiver grass is expected to be able to reduce the risk of erosion at landslide-prone points in the village of Gelangsar, improve the physical condition of the land and increase land productivity.

Effect of Disaster Preparedness Training on Knowledge Regarding Flood and Landslides Disaster Management among Family with Elderly

Elderly population is among vulnerable populations during disasters. This study aimed to assess the effect of disaster preparedness training on knowledge on flood and landslide disaster preparedness and management in families with elderly. This was a quasi-experimental pre-post study on 30 respondents participating in natural disaster preparedness training. A significant change in general knowledge on disaster, flood, and landslide (12.9, 20, and 12.4 points, respectively). Disaster preparedness practice was good as reflected in actions performed before, during, and after disaster. Before flood occurs, families prepare a disaster preparedness bag for important documents as well as logistics (food) for emergencies and equipment for evacuation while also store valuable goods in a safe place. During flood, families prioritize elderly evacuation while seeking information on the flood through neighbors, handy talky, handphone, television, and radio and ensure the availability of sufficient logistics. After disaster, families clean their properties and house; provide clean water and toilet facilities for the family; check the health of family members that may be impacted by the flood; and make sure that all electrical panels safe. Disaster training affects the knowledge of flood and landslide disaster management in families with elderly.

A SPATIOTEMPORAL FUSION NETWORK TO MULTI SOURCE HETEROGENEOUS DATA FOR LANDSLIDE RECOGNITION

Abstract. In recent years, the frequency of landslide disasters has been increasing year by year due to the extension of human activities to the natural environment. Fast and detailed landslide surveys are important for landslide disaster prediction and management. There are many driving factors for landslide formation, and most of the current deep learning-based landslide identification methods use optical remote sensing images in a short period or a few types of fused data for prediction. Therefore the upper limit of accuracy they can achieve is low. This paper proposes a landslide identification network model based on the spatio-temporal fusion of heterogeneous data from multiple sources. The model takes observations such as time-series optical remote sensing images, DEM, geological formations, and meteorological data as inputs. To address the problems of non-uniform data forms and redundancy caused by time-series data, we design the temporal phase fusion module of coupled CNN-LSTM to fuse the temporal features of multi-source data based on the extraction of their spatial features. Subsequently, we design the spatial feature fusion module based on DCNN-DBN to realize the deep expression of temporal phase and spatial features of landslides and improve the recognition efficiency and accuracy of the network. Through experimental verification, the AUC value of our proposed model is 0.8976, the F1 score is 0.8352, and the MIoU is 0.8624. The evaluation results reflect that the model can provide support for large-scale landslide disaster investigation.

A novel landslide susceptibility optimization framework to assess landslide occurrence probability at the regional scale for environmental management

Landslide is a hazard that has drastic repercussions on population and the environment worldwide. Landslide susceptibility mapping (LSM) is vital for landslide disaster management and formulating mitigation strategies. In this study, with the support of geographic information system and remote sensing, a new LSM hybrid framework is developed based on random forest (RF) and cusp catastrophe model (CCM). Under the framework, 15 conditioning factors and 2082 historical landslides are selected to test and compare its performance in a landslide-prone area in Liangshan, Southwest China. The results depicted a better performance of the new LSM hybrid framework (RF-CCM) than those of RF or traditional application mode of catastrophe model (Catastrophe fuzzy membership functions, CFMFs) only. The RF-CCM achieved the highest accuracy (0.901), the narrowest confidence interval (0.895–0.907), and the smallest standard error (0.004) among all the models. Notably, RF-CCM successfully decreased the uncertainty of CFMFs in determining the relative importance of conditioning factors, overcame the dependence of the CFMFs on independence among the conditioning factors, and had a higher stability level than RF. Moreover, distance to human engineering activities and slope had the greatest impact on LSM in the modeling process. The study result can provide insights for developing reliable predictive models for other landslide-prone areas with similar geo-environmental conditions.

KOLABORASI TRIPLE HELIX DALAM PENANGGULANGAN BENCANA TANAH LONGSOR DI KABUPATEN BUTON

Landslide disaster management policies and actions are then transformed into communities that experience vulnerability, so that the relationship between universities, companies, and the government in the field of disaster is very much needed in disaster mitigation efforts, especially landslides. The purpose of this study was to determine the triple helix collaboration in landslide disaster management in Buton Regency. This type of research is descriptive research with a qualitative approach. Data collection techniques were carried out through observation, in-depth interviews, and documentation studies. The results of this study indicate that the minimal level of coordination and innovation that exists in the triple helix element so that no integration is created which makes mitigation efforts seem slow; between the elements of this triple helix has a different ideology, and this ideology is sometimes highlighted by each element of the helix, so that the triple helix relation cannot be built as desired; and conditions of bureaucratic procedures that are too difficult to make elements of universities and companies reluctant to collaborate with government elements in efforts to mitigate landslides in Buton Regency.

Disaster Risk Management Of Landslides In Peninsular Malaysia

Landslide is one of the major disasters that occur in Malaysia. Despite various initiatives undertaken by the government to reduce their occurrences, landslide incidents continue to occur, resulting in loss of life and property. The development process coupled with climate change increases the risk of landslides. This manuscript briefly looks at the causal factors of landslides and identifies requirements for landslide risk management to identify options to strengthen landslide risk management in Peninsular Malaysia. Risk management encompassing identification of hazards, vulnerabilities and exposure of elements can prevent landslides from becoming a disaster. The study conducted, noted that the present approach in landslide disaster management has not fully incorporated the landslide disaster risk management approach. Key aspects such as legislation, institutions, zoning of disaster risk areas, risk transfer mechanisms and financial allocations require serious consideration. The determination of the legal basis to incorporate risk management measures, which will set out the needed mandates, jurisdiction and responsibility is one of the important steps. It will help ensure that decision making processes are risk-based, and strengthen the processes in preparing disaster risk maps, comprehensive databases using landslide inventory and register. It would also help in the preparation of early warning and emergency response systems as well as bolster community involvement, awareness, and public education. These will require a holistic approach in landslide disaster risk management. It will support the effort undertaken by government and stakeholders to incorporate the four priority areas of the Sendai Framework for Disaster Risk Reduction 2015-2030 which can effectively help reduce the risk of landslides.

COPING MECHANISM IN THE FACE OF LANDSLIDE HAZARD IN EAST JAVA INDONESIA

Increasing the number of landslide disasters and their effecting population in Indonesia is crucial to concern and need a solution. A coping mechanism is the strategies of people exposed to disasters and a key to anticipate and prepare and reduce landslide disaster risk. During twenty years, Malang Regency in East Java Province was hit by several landslide disasters, which radically affected both the physical and social environment. This paper aims to investigate coping strategies by communities facing landslide disaster. The methodology used in this research was divided into three sections: (1) pre-field; (2) fieldwork; and (3) post-field. The survey was initially conducted by mapping physical condition and interview with the key of informants. The research result was analyzed utilizing recorded-transcribed interviews and interpreted according to recurrent themes in the answers. Generally, people have performed different strategies to cope with landslide disasters within the study area. The coping mechanisms have been well developing into two categories, such as individual and structural coping mechanisms. This case study indicates that a coping strategy should be taken to reduce landslide disaster risks, such as fostering agriculture protection, properties-housing protection, and reinforcing risk governance to improve landslide disaster management and programs fostering adaptation and resilience.

A Knowledge Discovery Method for Landslide Monitoring Based on K-Core Decomposition and the Louvain Algorithm

Landslide monitoring plays an important role in predicting, forecasting and preventing landslides. Quantitative explorations at the subject level and fine-scale knowledge in landslide monitoring research can be used to provide information and references for landslide monitoring status analysis and disaster management. In the context of the large amount of network information, it is difficult to clearly determine and display the domain topic hierarchy and knowledge structure. This paper proposes a landslide monitoring knowledge discovery method that combines K-core decomposition and Louvain algorithms. In this method, author keywords are used as nodes to construct a weighted co-occurrence network, and a pruning standard value is defined as K. The K-core approach is used to decompose the network into subgraphs. Combined with the unsupervised Louvain algorithm, subgraphs are divided into different topic communities by setting a modularity change threshold, which is used to establish a topic hierarchy and identify fine-scale knowledge related to landslide monitoring. Based on the Web of Science, a comparative experiment involving the above method and a high-frequency keyword subgraph method for landslide monitoring knowledge discovery is performed. The results show that the run time of the proposed method is significantly less than that of the traditional method.

GEOSPATIAL METAMODEL FOR LANDSLIDE DISASTER MANAGEMENT IN MALAYSIA: CURRENT PRACTICES

Most of time, landslide in Malaysia was triggered by heavy rainfall during monsoon season. Landslides disaster in Malaysia are managed by Public Work Department (JKR) via Slope Engineering Branch (CKC), Department of Mineral and Geosciences (JMG), and Malaysian Space Agency (Agensi Angkasa Malaysia). JKR was critically engaged in slope remediation activities and the establishment of slope management. JMG and MYSA contribution was informing the government areas prone to landslides via landslide mapping. National Institution, expert practices, and researcher in landslide management in Malaysia is actively engaged in disaster management (DM) activities through the implementation National Slope Master Plan 2009 – 2023 (NSMP 2009 – 2023). With due respect, some issues such as no evasive approach where experts might use for decision-making process according to site suitability, and appropriate mitigation measure, lack of study in landslide disaster management practices in Malaysia, and the availability landslide historical data. Hence, this paper systematically reviews the current practices of geospatial metamodel for landslide disaster management in Malaysia. Findings show that, only few researchers deeply explore the potential of geospatial metamodel in facilitating landslide management and monitoring activities in Malaysia. Geospatial metamodel gives a benefit in determined the completeness of any landslide DM activities either before, during, or after the incident.