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@article{187963,
        author = {THUSHARA THOMAS and Dr. MURARI LAL DHAKAR},
        title = {A HAVE A OBSERVE TO ASSESS THE LANDSLIDE RISK AND MANAGEMENT STRATEGIES IN KERALA},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {312-324},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=187963},
        abstract = {Terrain is highly susceptible to landslides triggered by frequent earthquakes and heavy rainfall. In the recent past, cloud burst events are on rising, causing massive loss of life and property, mainly attributed to climate change and extensive anthropogenic activities. Despite the growing scientific and technological advances, it has been difficult to reduce the impact of natural hazards as experienced in case of tragedy. Landslides are common hazards that affect all the mountainous areas of the world to some extent by posing a threat to human habitation, life, livelihoods, and infrastructure. The monsoon in June 2013 arrived two weeks earlier than usual in kerala cloud bursts and heavy rainfall hit several parts of the higher reaches of the kerala. This unprecedented rainfall resulted in an abrupt increase in water levels and gave rise to flash floods in the kerala and other river basins of causing extensive landslides at various locations. Continuous rains caused to rise. The weak moraine barrier in the lake breached, and a massive volume of water along with large boulders and sediment came down the channel to the east, devastating the towns of Landslides and toe erosion by the sediment-loaded rivers damaged roads/highways at many locations and washed away multiple bridges (steel girder, beam, and suspension/cable bridges). Traffic was disrupted over national highways and link roads in the region, along with the disruption of telecommunication lines, all adding to the impact of the disaster. Many hotels, rest houses, and shops around the temple in kerala were destroyed. Such massive devastation in the Himalayan region has not been seen in the last few decades. It considers a major devastation in Indian disaster history. Therefore, looking at these problems and considering the fragile Himalayan environment, this study has been conducted to examine upon the post-disaster situation so that risk in the future from such disasters can be minimized. The present study explores the systematic landslide disaster mitigation strategies for the kerala watershed affected by the disaster. The study involved preparing and using landslide inventory maps, identifying landslide hazard and risk, and possible landslide risk mitigation over the kerala watershed.
The number and quality of landslide hazard and risk studies have increased rapidly in the last decade. This is mainly due to the increasing knowledge of geo informatics tools such as GIS, GPS, and Remote Sensing, visualization tools, etc., allowing integration of data collected from various sources and methods and at different scales. The specific database required for landslide hazard and risk assessment has been explained in study. Preparing landslide inventory map is a preliminary step for zoning of landslide susceptibility, ascertaining landslide hazards, or determining landslide risk. In this study, landslide inventories specific to multi-temporal landslide maps were done. High-resolution Google Earth data of different periods (e.g., pre-disaster and post-disaster) and SRTM DEM data were used as the primary data product for generating the multi-temporal landslide inventory. In total, 2.27 Sq Km. of landslides was identified in 2012 in the pre-disaster landslide inventory, whereas the 5.30 Sq Km of landslides were identified in 2013 in the post-disaster landslide inventory. The experience gained in the mapping of multi-temporal landslide inventory maps in the kerala watershed has revealed that obtaining a reliable multi-temporal landslide inventory is a time-consuming and challenging operation. In preparing a multi-temporal landslide inventory, outmost care should be taken in the identifying the location of landslides of different time periods and in identifying areas where local morphological changed has occurred in response to landslide. Except for the landslide inventory, many other data types are needed for landslide hazard and risk assessment. There are valuable data sets available for spatial landslide hazard and risk assessment at different levels in India, but significant problems were found regarding data accessibility, format, and quality. In this study, based on extensive literature review, 18 thematic data layers road network thematic layers are generated using various geospatial tools and technique for landslide hazard assessment. The spatial data for landslide risk assessment eight vulnerable elements to landslide belong in four categories: i.e., physical vulnerability indicators (road section or transportation, building, electricity transmission line, and powerhouse substation), social vulnerability indicator (population density and health facilities availability) environmental vulnerability (land use land cover) and economic vulnerability (village income) were used for this study. Therefore the intermediate map of vulnerability is generated by combining the four vulnerabilities mentioned above. Identification and mapping of landslides and hazardous landslide areas and the vulnerability of the elements at risk are intrinsically tricky. It can be subjective and requires a great effort to minimize the inherent uncertainty. This difficulty has its leading cause that both process knowledge and the amount of reliable observations are limited in most parts of the world (except a few developed countries) due to the lack of good records. Statistical methods have become indispensable for landslide studies as their quantitative power allows dealing with spatial uncertainties. Commonly applied spatial statistical methods in landslide studies include lattice-based data models in pixels or raster modeling. Depending on the scope of the work, type of data available, and the ultimate goal to be achieved, the methods for the hazard analysis may also be different. This study aimed at demonstrating the implementation of spatial statistical modeling in a stochastic framework for landslide hazard, vulnerability, and risk assessment. The detailed methodology for landslide hazard assessment (LHZ) was discussed in chapter four. The primary objective of the hazard analysis over the kerala watershed is to quantify the changes of slope failures in terms of the disaster. This study used the Bayesian logistic regression model (BLR) for landslide susceptibility modeling. Results of the model showed that uncertainty analysis in parameter estimates could be comprehensively addressed using this method. This is because the Bayesian approach can be performed iteratively, resulting in a probability distribution of the posterior estimates of the parameters. Also, it has the advantage of prior information being included in the analysis. In the case of accuracy assessment through ROC, the accuracy of 88.2% and 91.4% in 2012 and 2013, respectively, was achieved using testing data for the BLR model.
The analysis of landslide hazard zonation (LHZ) shows a profound change in LHZ of kerala Watershed after the disaster. In the pre-disaster phase the exceptionally high LSZ changed from 1.01% to 2.54% during 2013. The major shift in very high LSZ comes from high LSZ near about 0.92% area shift, flowed by medium LSZ (0.67%). In High LSZ change from 7.32% to 19.57%, the largest share was gained from low LSZ (7.18%) and medium LSZ (5.96%). About 9.27% of land from low to very low LSZ goes to the High LSZ zone. LHZ was validated based on the ROC curve for the present study. The area under the curve measures the test's ability to correctly classify those pixels with and without landslide risk. The shape of the curve indicates the performance of the model. If the ROC plot is closer to the upper left corner, the test's overall accuracy is higher. The area under the ROC curve has a peak value of 1 for perfect prediction, whereas a value near 0.5 suggests the model's failure. The area under the curve was 0.882 in 2012 and 0.914 in 2013, which gives an accuracy of 88.2% and 91.4% in 2012 and 2013, respectively, for the model developed using binary logistic regression. The standard error (0.008 in 2012, 0.006 in 2013) and asymptotic signature value is found < 0.05 for both the year suggest the validity of the curve. Therefore, the landslide hazard map developed by this model is effective in predicting known and unknown landslides. 
After the hazard assessment a comprehensive landslide risk assessment was done. The objective of risk assessment was to provide a better tool for disaster risk management. The risk can be assessed either quantitatively (i.e., probabilistic) or qualitatively (i.e., heuristic) approaches. The detailed methodology for landslide risk assessment was discussed in study. This study applied a vicariate statistical approach known as the Bayes’ theorem of Weight of Evidence (WOE) model for landslide vulnerability mapping. Finally, the landslide risk is represented by a semi-quantitative risk index generated by multiplying the two composite index maps of hazard and vulnerability. Thus, the landslide risk map is produced using the following landslide risk equation developed by Varnes (1984). The proposed method starts with a comprehensive landslide inventory then analyzes the hazard and vulnerability indicators in detail before carrying out the risk assessment. The diversity and complexity of geomorphic processes in the watershed make the analysis much more complicated from a hazard point of view. Different spatial analysis techniques were applied in GIS to generate hazard maps for the landslide, and specific risk maps were made for particular combinations of hazards and elements at risk. The spatial distribution of landslide risk has been obtained by integrating a landslide hazard map and a landslide vulnerability map of the selected elements at a spatial level in a geographic information system (GIS) environment.
The result of the risk assessment revealed that the “very high” landslide risk zone occupies 12.15% of the total area, covering 40.72% of the total landslide quantity. A high-risk zone is demarcated along the NH- 109, which shows that the landslide risk is higher along the road section. On the other hand, high-risk zones pockets are sparsely distributed in all of the study areas. The total high-risk zone covers 17.52% of the total area and 24.95% of landslide, which is significantly higher. The combined high and very high-risk zones have a total area of about 29.67%. In contrast, moderate, low, and very low-risk zone covers 23.85%, 28.49%, and 17.99% of the total area, respectively, and only includes 17.60%, 16.51%, and 0.22% of the total number of landslides, respectively. The low and very low-risk zone is located over the vegetation cover and snow cover area of the watershed. The validation of the model has been done by the ROC curve. The area under the curve obtained is 0.910, which provides an accuracy of 91% for the model developed using WoE. Therefore, the landslide risk map generated by this model is effective. 
Observed to block the river course if further movement occurs, thereby threatening the safety of human habitations and infrastructure in downstream areas. Therefore, special attention is required to be paid to monitoring these and other major slide zones along the river banks. The pedestrian route has been relocated on the left bank of kerala. This slope is not a cause of immediate concern and would ultimately be stabilized by nature over time. The assessment of landslide hazards and risks is not the final goal. The aims of hazard and risk assessment are too used in the planning of landslide risk reduction measures and implementation of selected measures to ensure the protection of the community against an acceptable event. Therefore, the study aimed was to apply landslide Hazard and risk information in planning for long-term disaster Mitigation. Landslide risk has been reduced throughout the years by social and infrastructural investments. Despite the recognized success of disaster management in the country, economic losses continue to increase and affect development, as seen in the 2013 disaster. After reviewing the literature, national, state policy, and guidelines for landslide disaster mitigation in mountainous regions of the world, this study proposed the following four mitigation strategies for the study area. The first proposed landslide risk mitigation is Engineering Solution. The engineering solution is the costly and utmost direct strategy for reducing landslide risk. It is a suggestive measure as it will be applied by the local authority to control the initiation of landslides or control the landslide movement to reduce its impact on the elements at risk located down slope. Based on the practice engineering solution of the world, national policy, the character of the terrain and available material following engineering solution, e.g., Concrete retaining wall, Gabion wall, Grass turf, Cable, Mesh, Fencing, and Rock Curtains, Anchors and Bolts, Concrete and Unite are suggested for the area. All it needs is an injection of more science and technology-backed sound engineering to make construction economical, speedier, durable, safer, and more eco-friendly. Let’s come together and work together form multidisciplinary teams to achieve landslide risk reduction and resilience and minimize sufferings. The study also argues to ensure that the roads and their slopes in the kerala system are appropriately managed it is required to include roadway slopes as part of road infrastructure. Therefore, developing a digital roadway slope management system can be the best solution for safer and environmentally friendly roads. Before selecting any engineering solution for slope stability, a detailed investigation and suggestion from a geological/geotechnical expert should be considered.
The proposed second mitigation strategy was Land Use Zoning. It would also be considered as a suggestive measure. The local govt or PWD authority can be used the landslide hazard and landslide risk map for planning. Planning control is one of the efficient and cost-effective ways to reduce landslide losses. It can be accomplished either by (a) converting or removing existing development or (b) regulating new development in unstable areas. Land-use planning includes the prohibition of new constructions, and routine building maintenance works should be allowed based on the risk zonation map. The third mitigation strategy proposed in this study was early warning. The study considers it a suggestive measure as an early warning system is an instrument-based work beyond the scope of this research. The study suggests instrument-based monitoring for some major landslides located along and in high-density population areas. The proposed monitoring side is selected based on the population density risk zone falls in very high to high category, size of the slide. The objective of particular slope monitoring is to assess the existing conditions of the slope to determine whether the landslide is active or not, and if it is, then rate and direction at which slope is moving, to warn of imminent emergencies.
Finally, the study proposed using community-based disaster management as the fourth strategy for landslide risk reduction as it’s a cost-effective approach to reduce landslide risk in the study area. The study of risk perception surveys related to landslides and the disaster indicate that the risk from potential landslides can be reduced if the communities become aware of the potential threat. During conducting of CBDM, most people of the villages are aware of landslide disasters, and they accept that their area is prone to landslides. They are aware of the cause of landslides (i.e., very high rainfall), and they know that the period between July to September is the most problematic and the time of other hazards. Many people have lived in the study area throughout their lives and their ancestors for many generations. Although they have experienced or witnessed multiple disasters, they do not want to leave their native place. People tolerate the risk because of certain benefits such as working in nearby district headquarters well fertile land for cultivation, etc. The local people have no emergency preparedness plans and insurance coverage of properties for a disaster. They depend on the local government and local organizations such as the rescue operation unit, a nongovernmental organization, for post-disaster help and mitigation. The low economic status of most communities does not permit them to make another house in other safer places or carry out additional investment for reinforcement of their homes to protect them from landslide damages. The local people are well aware of the landslide problems over the region specially aftermath of the tragedy. The local people were very keen to mitigate the landslide problems in their particular region. With the experience gained in the field during the CBDM procedure, it was understood that CBDM should be conducted at least once in villages prone to landslide risk. The landslide management strategies suggested in this study would help to reduce the impact of any further events in the mountainous areas.},
        keywords = {},
        month = {November},
        }