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Research - (2022) Volume 11, Issue 11
Landslide Susceptibility Mapping Using Shannonâs Entropy Methods Using Hybrid Technique: A Case Study of Kinnaur District, Himachal Pradesh, India
Vicky Anand1*, Aastha Sharma2, Amit Kumar Sahni1 and Sunil Kumar Jangra32Department of Geography, Jamia Miliya Islamia University, New Delhi, India
3Department of Geographical Information System, Guru Jambheshwar University, Hisar, Haryana, India
Received: 26-Sep-2022, Manuscript No. JGRS-22-18161; Editor assigned: 29-Sep-2022, Pre QC No. JGRS-22-18161 (PQ); Reviewed: 13-Oct-2022, QC No. JGRS-22-18161; Revised: 19-Oct-2022, Manuscript No. JGRS-22-18161 (R); Published: 28-Oct-2022, DOI: 10.35248/2469-4134.22.11.261
Abstract
Global climate change has developed into one of the most complex problems facing mankind in the current scenario. This change has an irrefutable disturbance on the stability of natural and engineered slopes, including landslides. In mountainous terrain such as the Himalayas, landslides are among the most harmful hazards. Most landslides occur under the influence of earthquakes or rainfall, and they are often among the most devastating natural hazards. Increasing awareness of landslides’ socioeconomic impacts has attracted global attention to landslide studies. A landslide risk assessment in Himachal Pradesh’s Kinnaur region was conducted using remote sensing and Geographic Information Systems (GIS). For the Landslide Susceptibility Mapping, a hybrid approach is used. Using a hybrid approach, weighted overlay is combined with Shannon’s entropy, an eminent technique based on evidence. To prepare a susceptibility map, eight landslide-related criteria are used in conjunction with the inventory of landslides that contained recent and historical landslides. The validation results show that the models derived using the following approach have the highest prediction capability.
Keywords
Landslides; Susceptibility; Shannon entropy; Weighted overlay; Remote sensing
Abbrevations
RS: Remote Sensing; GIS: Geographical Information System; LSM: Landslide Susceptibility Mapping; IMO: Indian Meteorological Organization; ETM: Enhance Thematic Mapper; DEM: Digital Elevation Model; SRTM-Shuttle Radar Topography Mission; LULC: Land Use/Land Cover; ROC: Receiver Operating Characteristics.
INTRODUCTION
In geophysics and climate science, landslides are defined as an earth’s surface movement due to a geophysical phenomenon or a climatic change. Typically, it involves soil and rock mass displacement along one or more slip surfaces. Among Asian countries, South Asian nations suffer the greatest fatalities, and even among South Asian countries, India is one of the worst devastated by landslides [1]. Impacts from extreme-weather events hit the least developed countries most vigorously, as they are susceptible to the damaging repercussions of a hazard, have a lower coping capacity, and may need more time to rebuild and recover. The Indian Himalayan belt has been persistently threatened by landslides of varying intensities throughout history [2-4]. Human, material, and environmental losses have been caused by landslides when they occur in proximity to human habitation because of the high exposure to landslide risk. Unsustainable construction activities in vulnerable zones, deforestation for urban boundary expansion, and inadequate slope management will accentuate the number of people exposed to the risk of landslides. In view of escalating climate risk, it is imperative to adopt suitable adaptation strategies for landslides. In understanding and forecasting probable future landslides, Landslide Susceptibility Maps (LSMs) can be an effective tool [5-7]. Landslides frequently occur concurrently with other natural disasters such as wildfires and floods, rendering them a significant concern in hazard mitigation and comprehensive planning. LSMs depict areas prone to landslides event in the future through associating some of the major aspects that cause landslides by historical slope failure distributions, making it possible to predict where they will occur in the future [8-12]. For these reasons, the production of LSM early in the landslide assessment process plays a crucial role in the safe planning of economic activities, such as urbanization and building structures.
In contrast, no standard procedures for producing maps showing landslide susceptibility exist. Landslide risk management can thus be achieved through providing risk executives with timely and precise information about landslides [13-19]. Because of the complex nature of landslides, predicting their location in advance is challenging. In addition to providing information regarding landslide hazards, LSM also indicates whether landslides will occur within a particular period.
This study aims to determine a universal degree measure of inconclusiveness associated with fuzzy sets in the defined circumstances [20-26]. A new functional defined of the class of generalized characteristic functions (fuzzy sets) is introduced, called “entropy”, which does not use probabilistic concepts. This quantity has a very different meaning than classical entropy because it doesn’t require a probabilistic picture to be defined [27-34]. This measure gives a symbol of the “indefiniteness” of the situation. The definition of probability isn’t agreed upon, even though there is a mathematically defined theory of probability. Understanding susceptible regions would be possible with effective LSMs [35-40]. GIS-based tools are developed and used to create susceptibility maps to help planners in understanding of landslide hazards. There are three general types of approaches: subjective, objective, and Shannon entropy.
Study area
Kinnaur district is one of Himachal Pradesh’s 12 administrative districts that have been given the special status of a tribal district under Schedule 5 of the Indian constitution. Kinnaur can be found between 77°45’ and 79°00’35” East longitudes and between 31°05’50” and 32°05’15” north latitudes. This area is surrounded by Tibet and Uttaranchal in the east, Shimla district in the southwest, Kullu district in the northwest, and Lahaul-Spiti district in the northeast [41-47]. This district has three high mountain ranges, namely the Greater Himalayas, Zanskar, and Dhauladhar, which encompasses the valleys of Baspa, Sutlej, Spiti and their tributaries. Kinnaur’s topography ranges in altitude from 1600 meters to 6816 meters. Forests, orchards, fields, and picturesque hamlets are scattered along the mountainsides. Shivlinga is located at Kinner Kailash’s peak, an extremely religious place for Hinduism. 1989 was the year that this beautiful district was opened to outsiders [48-53]. A portion of the old Hindustan-Tibet Road runs along the banks of the Sutlej River through the Kinnaur valley until it arrives at the Shipki La Pass, which leads to Tibet.
Materials and Methods
The susceptibility map of the Kinnaur region was determined by utilizing the landslide-related criteria outlined in the table above. Input maps of the roads, rivers, and drainage systems were obtained from the study area’s topographic maps (1:50,000), while geology maps were obtained from geological survey of India. Additionally, the aspect and slope criteria derived using the Shuttle Radar Topography Mission (SRTM) 30 m Digital Elevation Model (DEM) using ArcGIS version 10.4. Images with 30 m spatial resolution of Landsat ETM+ satellites were used to create land use/cover maps [54-59]. A mean annual rainfall map was created in ArcGIS using kriging interpolation techniques and data from the Indian Meteorological Organization (IMO). Additionally, the inventory of landslides polygons obtained from the Geological Survey of India containing 1190 recent and historical landslide polygons (Table 1 and Figures 1-7).
| Criteria | Data source |
|---|---|
| Slope | 30 m, SRTM DEM |
| Aspect | 30 m, SRTM DEM |
| Rivers | 1:50,000 Topo-map |
| Altitude | SRTM |
| Rainfall | 30 years, IMO data |
| Transport network | 1:50,000, Topo-map |
| Geology | 1:100,000, Geo-map |
| Land use | 30 m, Landsat Images |
Table 1: Description of criteria and data source.
Figure 1: Altitude of the Kinnaur region.
Figure 2: Geology of the Kinnaur region. Note: (
) Palaeoproterozoic,(
) Proterozoic, (
) Tertiary, (
) Neoproterozoic, (
) Ordovician-Silurian, (
) Triassic-Jurassic, (
) Cambrian, (
) Palaeozoic, (
)Neoproterozoic-Cambrian, (
) Carboniferrous.
Figure 3: Aspect of the Kinnaur region. Note: (
) North (-1), (
) North
(-1), (
) Northeast (0-22.5), (
) East (67.5-112.5), (
) Southeast (112.5-
157.5), (
) South (157.5-202.5), (
) Southwest (202.5-247.5), (
) West
(247.5-292.5), (
) Northwest (292.5-337.5), (
) North (337.5-360).
Figure 4: Slope degree value of the Kinnaur region.
Figure 5: Distance from rivers. Note: (
) River, (
) Landslides.
Figure 6: Distance from roads. Note: (
) Roads, (
) Landslides.
(
)
Figure 7: Landuse/Landcover of the Kinnaur region. Note: (
) Water,
(
) Trees, (
) Grass, (
) Flooded vegetation, (
) Crops, (
) Scrub/shrub, (
) Built Area, (
) Bare ground, ( ) Snow.
Shannon’s entropy approach
A system’s entropy can be described as disorder, instability, imbalance or uncertainty. According to Boltzmann’s principle, entropy in a system is proportional to its condition [60-64]. As a thermodynamic measure, this correspondence describes the status of a system.
Using this method, the dissimilarity or diversity of each factor in the environment determines whether it has the potential to cause a landslide.
The entropy of landslides measures the extent to which different factors cause landslides; the higher the entropy index, the stronger the influence of the factor.
This approach indicates the extent of each factors causing the landslide; the greater the entropy index, the stronger the influence, and the lower the entropy index, the weaker the landslide effect.
The index system provides additional entropy through several important factors. Therefore, objectives weights can be calculated based on entropy.
A positive effect of mean annual rainfall and drainage density on the probability of landslide occurrence is predicted by equation (1).
There is a positive relationship between the mean annual rainfall and drainage density and the probability of landslides occurring and this relationship is predicted by the equation (1).
In other words, the higher the criterion value, the bigger the probability of a landslides. Alternatively, some other criteria, such as the proximity to roads, proximity to the rivers, etc. apply reverse conditions, and the equation 2 is best representation of it [65-69].
In the next step, the landslide shanon’s entropy matrix (R) is created by combining (m) landslide events sample with (n) geo-data layers:
The next step is to prepare the entropy matrix by combining the previously landslide samples with the n geo-data layers.
Therefore, Shannon entropy can be defined as follows:
In this equation, Ej is the entropy value of the jth landslide, pi,j is its value in the jth criteria, and k is a positive constant,
Units of measure are determined essentially by the following:
K = (In m)−1
Taking m as the number of landslides that have occurred, we can define a normalized decision matrix pi,j for each landslide criteria as follows:
In this index system, weights indicate the role the factors play in the synthesis assessment, and higher values indicate a greater importance for that factor.
Taking Wj as the weight of the jth geo-data layer and Vj as the value of the jth layer, Vj is defined as:
Hybrid Landslide Susceptibility Mapping Model
T In terms of landslide susceptibility mapping, the proposed hybrid model is as follows:
Assume that S is a measure of the susceptibility to landslides, Wi is the weight of each criterion, and Xi is the standardized criteria for landslides.
Results and Discussion
Landslide susceptibility mapping
To analyze the landslides susceptibility, Shannon entropy method was used. In the present study, the susceptibility of landslides was assessed by calculating the relationship between landslides occurrence and these eight conditioning factors using Shannon entropy method (Supplementary Table 1).
Supplementary Table 1, Shannon’s entropy has an aspect weight of 0.158148 that is calculated based on the equation no.6. Similarly, Aspect weight of Shannon’s entropy is 0.158148 which is higher than the slope angle indicates that, the direction of slope is one of the major factors for landslide susceptibility mapping in the Kinnaur district [70-73]. For Geology, Altitude, Proximity to river, Proximity to roads, Landuse/Land Cover and Rainfall weights are 0.153516855, 0.126592428, 0.149489064, 0.148656291, 0.127152285, and 0.122543344 respectively (Figure 8).
Figure 8: Landslide susceptibility of the Kinnaur region. Note: (
) Unlikely, (
) Moderate to low, (
) High, (
) Severe to very high.
Based on the weighted average, it can be concluded that factors such as geology, aspect, proximity to the river and proximity to the roads are the major factors in landslide susceptibility mapping. It is difficult for locals to access water because of the debris slipping down the slopes and blocking the river channel [74-76]. Subsequently, with the aim of prepare the final map of landslide susceptibility, On the basis of natural breaks, we combined weighted products of secondarily parametric maps.
Validation of landslide susceptibility models
In statistical theory, the Receiver operating characteristics curve is a classic method for evaluating performance to examine susceptibility maps. As the classifier threshold changes, the ROC curve, which has a value range of [0,1], describes the process of classifier performance. As seen in ROC curve, the model’s sensitivity is plotted against 1−specificity.
Based on these values, negative and positive observations in the validation sample are correctly differentiated by the model. Signal sensitivity is represented by every point on the curve [76-79]. This graph shows a horizontal axis indicating False Positive Rate (FPR) specifity compared to a vertical axis showing True Positive Rate (TPR) sensitivity. The prediction rate could be used to assess the prediction capability of the landslide models [8]. The ROC shows an accuracy of 88.23%. In analyzing the AUC results, we observed that the produced map exhibits a high level of accuracy (Figure 9).
Figure 9: Area under curve of frequency ratio.
Human life and property are at risk from landslides, so mapping landslides’ susceptibility is an important and preliminary step to preventing them. A landslide susceptibility map and an evaluation of contributing factors that trigger the landslides were produced using Shannon’s entropy model. In this study, Shannon entropy model was applied to develop a map of landslides susceptibility, and causeeffect relationships were analyzed in order to determine the cause of the landslides. In the modelling, eight landslide conditioning factors were taken into account in this modelling. Landslide conditioning factors are added with geographic information systems. A ROC curve was developed using the landslide inventory datasets acquired from geological survey of India to calculate Shannon’s entropy for the block entry. Based on the AUC curve, the result showed 88.23% accuracy. It has been divided into four classes: moderate to low, high, severe and very high. The area of each class falls under 252.20799 square kilometers, 1810.1757 square kilometers, 3826.0683 square kilometers, and 513.4724 square kilometers, respectively. The susceptibility map shows that Nichar tehsil falls within the unlikely and moderate landslide risk categories. Tehsil Sangla is most vulnerable to landslides and has a significant risk. Management of disasters through forecasting and monitoring may help in lessening the vulnerability of the communities.
Conclusion
There is an urgent exigency for landslide risk management at various spatio-temporal scales to map the landslide hazard to deal with its future occurrences. Human losses can be reduced by taking timely precautions and detecting early warning signs. It enables planners to make better decisions regarding further mitigation of landslide effects. A better understanding of the results of this study will provide engineers and planners with better methods for managing and mitigating landslides in the study area.
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Citation: Anand V, Sharma A, Sahni AK, Jangra SK (2022) Landslide Susceptibility Mapping Using Shannon’s Entropy Methods Using Hybrid Technique: A Case Study of Kinnaur District, Himachal Pradesh, India. J Remote Sens GIS. 11:261.
Copyright: © 2022 Anand V, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.