Causative factors of landslides 2019: case study in Malappuram and Wayanad districts of Kerala, India

Wadhawan, S. K.; Singh, Balmukund; Ramesh, Maneesha Vinodini

doi:10.1007/s10346-020-01520-5

Cited by 46 publications

(12 citation statements)

References 6 publications

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“…6 In India, the heavy rainfall due to the southwest monsoon is continuously causing floods and landslides in its various mountainous regions. A case study by Wadhawan et al 7 has highlighted various causative factors for landslides in the Kerala State of India in year 2019. This study helps in understanding the real-time analysis of the reasons due to which landslides occur across the globe.…”

Section: Overview and Backgroundmentioning

confidence: 99%

Internet of Things-Based Optimized Routing and Big Data Gathering System for Landslide Detection

Menon¹,

Verma

Kaur

et al. 2021

Big Data

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An exponential progression in the miniaturization of communicating devices has proliferated the generation of a large volume of data termed as ''big data.'' The technological advancements in the micro-electro/mechanical system has made it possible to design the low-cost, low-power consuming artificial intelligence (AI)-based wireless sensor nodes to gather the big data belonging to various attributes from their surroundings. These nodes help in the early detection and prediction for the occurrence of landslides, which are among the catastrophic hazards. A profusion of research has focused on exploiting the potential of sensors for continuous monitoring and detecting the landslides at the earliest. However, the limited energy resources of sensor nodes give rise to the huge challenge for the network longevity pertaining to landslide detection. To address this concern, in this article, we propose an optimized routing and big data gathering system for landslide detection using (AI)-based wireless sensor network (WSN) (ORLAW). Since we propose a distributed routing mechanism, AI has a major role to play in the intelligent detection of landslides that too without the intervention of an external entity. We use the Dynamic Salp Swarm Algorithm for the cluster head selection in ORLAW. Two data collecting sinks are deployed on the opposite sides of the network, which is assumed to be a mountainous area. It is discerned from the simulation examination that ORLAW elongates the reliability period by 23.9% compared with the recently proposed clusterbased intelligent routing protocol, and also outperforms many others in the perspective of energy efficient management of big data.

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Section: Overview and Backgroundmentioning

confidence: 99%

Internet of Things-Based Optimized Routing and Big Data Gathering System for Landslide Detection

Menon¹,

Verma

Kaur

et al. 2021

Big Data

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“…It is also a general practice in Kerala to create small pits (makeshift reservoirs) by altering hillslopes to store water for irrigation by obstructing natural drainage. These reservoirs lead to increased infiltration and super saturation of soil along hillslopes (Wadhawan et al 2020).…”

Section: Rainfallmentioning

confidence: 99%

Long-Term Climate Variability and Drought Characteristics in Tropical Region of India

Vijay

Sivan²,

Mudbhatkal

et al. 2021

J. Hydrol. Eng.

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This work reports climate change signals and long-term trend analysis of climate variables, meteorological drought, and extreme climate indices over the tropical state of Kerala in India. The trend analysis reveals statistically significant decrease of annual and southwest monsoon rainfall (as much as 63 mm and 55 mm per decade respectively). A decrease in number of annual rainy days (up to 2.8 days/decade) is also reported. Temperature trend analysis indicates an increasing 2 trend with as high as 1.3°C/decade. The spatio-temporal variation of extreme climate indices across Kerala shows a decreasing trend of extreme precipitation indices and an increasing trend of extreme temperature indices. R95 and R95p decreased in northern and southern Kerala whereas R5 index increased in central and southern regions. Warm days have significantly increased whereas cold days exhibit a decreasing trend across the state. The increase in warmer nights is statistically significant whereas colder nights are decreasing in central and southern regions. Meteorological drought using Standardized Precipitation Index (SPI) reveals increasing occurrence of droughts in Kerala with higher frequencies over southern and central Kerala.

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“…Landslides have been a concern in the Western Ghats of Kerala for the past two decades (Kuriakose, Sankar & Muraleedharan 2009, Kuriakose, Van Beek & Van Westen 2009, Wadhawan et al 2020. However, until 2018, shallow landslides and rockfalls were more in number than the debris flows (Abraham et al 2019(Abraham et al , 2020.…”

Section: An Unprecedented Increase In Debris Flowsmentioning

confidence: 99%

Mechanisms of Initiation, Runout, and Rainfall Thresholds of Extreme-Precipitation-Induced Debris Flows

Subramanian

Yunus

Jasin

et al. 2021

Preprint

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The frequency of unprecedented extreme precipitation events is increasing, and consequently, catastrophic debris flows occur in regions worldwide. Rapid velocity and long-runout distances of debris flow induce massive loss of life and damage to infrastructure. Despite extensive research, understanding the initiation mechanisms and defining early warning thresholds for extreme-precipitation-induced debris flows remain a challenge. Due to the nonavailability of extreme events in the past, statistical models cannot determine thresholds from historical datasets. Here, we develop a numerical model to analyze the initiation and runout of extreme-precipitation-induced runoff-generated debris flows and derive the Intensity-Duration (ID) rainfall threshold. We choose the catastrophic debris flow on 6 August 2020 in Pettimudi, Kerala, India, for our analysis. Our model satisfactorily predicts the accumulation thickness (7 m to 8 m) and occurrence time of debris flow compared to the benchmark. Results reveal that the debris flow was rapid, traveling with a maximum velocity of 9 m/s for more than 9 minutes. The ID rainfall threshold defined for the event suggests earlier thresholds are not valid for debris flow triggered by extreme precipitation. The methodology we develop in this study is helpful to derive ID rainfall thresholds for debris flows without historical data.

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Causative factors of landslides 2019: case study in Malappuram and Wayanad districts of Kerala, India

Cited by 46 publications

References 6 publications

Internet of Things-Based Optimized Routing and Big Data Gathering System for Landslide Detection

Internet of Things-Based Optimized Routing and Big Data Gathering System for Landslide Detection

Long-Term Climate Variability and Drought Characteristics in Tropical Region of India

Mechanisms of Initiation, Runout, and Rainfall Thresholds of Extreme-Precipitation-Induced Debris Flows

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