Evaluating the relation between land use changes and the 2018 landslide disaster in Kerala, India

Hao, Lina; Westen, C.J. van; Rajaneesh, A.; Sajinkumar, K.S.; Martha, Tapas R.; Jaiswal, P.

doi:10.1016/j.catena.2022.106363

Cited by 28 publications

(21 citation statements)

References 63 publications

(112 reference statements)

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“…In contrast to this, a recent study by Lan et al (2020) suggests that a densely vegetated slope decreases its stability. This study has been concurred with by the recent findings of Hao et al (2022) wherein most of the landslides that occurred in Kerala during 2018 are spatially associated with forest land. However, a closer look at Fig.…”

Section: Kavali Landslidesupporting

confidence: 90%

The tale of three landslides in the Western Ghats, India: lessons to be learnt

Ajin

Nandakumar

Rajaneesh

et al. 2022

Geoenviron Disasters

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In recent years, landslides have become a typical monsoon calamity in the Western Ghats region of Kerala, India. In addition to property damage, heavy rainfall (36% above normal) and multiple landslides (4728) killed 48 people in 2018. This tendency continued throughout the monsoon seasons of 2019, 2020, and 2021, resulting in the deaths of over 100 people. Anomalous precipitation is ascribed to the frequent development of low-pressure in the surrounding oceans. Using ground real data and satellite imagery, we evaluated the features of three large landslides in the state of Kerala, which occurred during the monsoon season of 2021. Our investigation found that the Kokkayar landslide was triggered by anthropogenic-related agricultural activities, the Plappally landslide by geomorphic and tectonic processes as well as human involvement, and the Kavali landslide by forest fragmentation with dense vegetation on thin soil. The triggering mechanism for all three of these landslides, however, is the intense rainfall of 266 mm in less than 24 h. Thus, an accurate and precise forecast of rainfall can be used to define a threshold for an early warning, which will be vital for saving lives.

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Section: Kavali Landslidesupporting

confidence: 90%

The tale of three landslides in the Western Ghats, India: lessons to be learnt

Ajin

Nandakumar

Rajaneesh

et al. 2022

Geoenviron Disasters

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“…(1) represents the frequency ratio which can be utilised to calculate the probability density of each class in Eq. (2). It is required to calculate the entropy value which shows the amount of information present in the every individual parameter.…”

Section: Shannon's Entropy Methodsmentioning

confidence: 99%

“…Landslides can occur as a result of natural characteristics that influence slope stability, as well as subsequent and triggering factors that can be caused by either natural or anthropogenic factors [1]. There appear to be significant regional and global impacts, such as decreased agricultural productivity, increased soil eroding, habitat destruction and death tolls, and infrastructure damage due to landslides [2]. An avalanche is the result of a slope spreading due to gravity; however, the development can be caused by a variety of factors such as precipitation, quakes, and disintegration.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Framework for Landslide Severity Prediction and Susceptibility Mapping

Bhargavi¹,

Arunnehru²

2023

Intelligent Automation &Amp; Soft Computing

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Landslides are a natural hazard that is unpredictable, but we can prevent them. The Landslide Susceptibility Index reduces the uncertainty of living with landslides significantly. Planning and managing landslide-prone areas is critical. Using the most optimistic deep neural network techniques, the proposed work classifies and analyses the severity of the landslide. The selected experimental study area is Kerala's Idukki district. A total of 3363 points were considered for this experiment using historic landslide points, field surveys, and literature searches. The primary triggering factors slope degree, slope aspect, elevation (altitude), normalized difference vegetation index (NDVI), and distance from road, lithology, and rainfall are considered. A landslide susceptibility map was generated using the Arc geographic information system (GIS) tool for all the triggering factors using frequency ratio method, Shannon entropy method, Relative effect method, and fuzzy logic method. A new Deep Neural Network (DNN) framework has been developed for the multiclass classification and prediction of landslide hazard zones as low, moderate, high, and very high. Existing works are only uses statistical methods, but the proposed work has used DNN to predict landslide severity at four different level even for semi data with over accurately. The training data for deep learning model are generated using the Sentinel Satellite images and field survey. The label for training the data are generated from the Landslide Susceptibility Index which are generated from statistical method. Among all the statistical method generated data the Shannon Entropy data is the most accurate of the four statistical methods achieves 99.16%, accuracy. The frequency ratio method based data achieves 97.08% accuracy, the relative effect method based data achieves 92.72% accuracy, and the Fuzzy logic method based data achieves 86.60% accuracy.

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“…The degradation of land use changes causes landslides to increase [2]. Anthropogenic activities can modify vegetation structure and change mechanical root reinforcement, soil cohesion, and hydrological processes [3]. Landslide is one type of soil or rock mass movement or a mixture of both due to soil or rock stability disruption.…”

Section: Introductionmentioning

confidence: 99%

The risk of landslides in The Jlantah upstream watershed in Karanganyar Regency in 2022

Nugraha

Suryanto

Utomowati

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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The Jlantah watershed (DAS) is a tributary of the Bengawan Solo River with an area of ±2,247 hectares, located on the western slope of Mount Lawu, including Tawangmangu District and Jatiyoso District, Karanganyar Regency. Regional developments in the eastern part of Karanganyar Regency, supported by improved accessibility of the road linking Karanganyar Regency with Magetan Regency, will create new growth centres to diversify use. The development of agribusiness-oriented dryland agriculture, natural tourism destinations, and the establishment of residential buildings provide more employment and business opportunities to improve the government’s and surrounding communities’ economic conditions. In other parts of the land characteristics in the Jlantah watershed, there are 68.78% very steep land slopes (>40%), 62.74% of the type of soil is greyish brown andosol and lytosol complex, and the rock structure has minor faults. The study aimed to determine the vulnerability, vulnerability, and risk of landslides in areas with other regional developments. This research was conducted in the Jlantah Upstream watershed, which includes: Tawangmangu Subdistrict (Blumbang Village, Gondosuli Village) with high environmental pressure and Jatiyoso District (Beruk Village) with low ecological pressure. The research method is quantitative descriptive with the determination of hazard, vulnerability, and risk of landslides using scoring and weighting from documentation data and observation results. The results of the study are: Blumbang Village, Gondosuli Village, and Beruk Village have a high category of hazard and vulnerability to landslides, while the risk of landslides has a medium class.

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Evaluating the relation between land use changes and the 2018 landslide disaster in Kerala, India

Cited by 28 publications

References 63 publications

The tale of three landslides in the Western Ghats, India: lessons to be learnt

The tale of three landslides in the Western Ghats, India: lessons to be learnt

Deep Learning Framework for Landslide Severity Prediction and Susceptibility Mapping

The risk of landslides in The Jlantah upstream watershed in Karanganyar Regency in 2022

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