Development of community-based landslide early warning system in the earthquake-affected areas of Nepal Himalaya

Thapa, Prakash Singh; Adhikari, Basanta Raj

doi:10.1007/s11629-019-5586-5

Cited by 24 publications

(14 citation statements)

References 56 publications

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“…In some cases, more than one type of participant was engaged in a single initiative. For example, an at-risk community may be involved in risk knowledge activities, while a volunteer or community organisation is tasked with communication and dissemination responsibilities (Gautam and Phaiju, 2013; Thapa and Adhikari, 2019). The most common types were volunteers, at-risk community and community organisation.…”

Section: Resultsmentioning

confidence: 99%

Towards an improved understanding of participation in natural hazard early warning systems

Clegg

Haigh

Amaratunga

2021

IJDRBE

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Purpose The purpose of this paper is to improve the conceptual understanding of the process of participation in early warning systems (EWS) through a review of participatory EWS examples in the academic literature. Specifically, this paper asks: who is involved, what responsibilities do participants hold, what activities are they involved in, and what are the associated successes, issues and outcomes? Design/methodology/approach A total of 30 cases of participation in EWS documented in the academic literature were identified through online searches. Existing concepts in participation (power and responsibility, communication) and people-centred early warning (risk knowledge, monitoring and warning, communication and dissemination and response capability) were used to examine each paper. Findings Participation was found to take place through a range of activities across all elements of the EWS. Participation also varied in breadth of inclusion, ranging from the general public to selected volunteers. The majority of cases received support and facilitation from other actors, such as government and NGOs, but the extent of power and responsibility held by participants varied greatly within this. Common successes and issues associated with participatory EWS and the potential outcomes are presented, and the opportunities, challenges and gaps in knowledge are discussed. Originality/value This paper links participation and EWS literature to form a clearer conceptualisation of participation in EWS in support of future research in the field. It provides unique insights into who participates, their roles and relations with other actors and the outcomes of participation.

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Section: Resultsmentioning

confidence: 99%

Towards an improved understanding of participation in natural hazard early warning systems

Clegg

Haigh

Amaratunga

2021

IJDRBE

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“…In this way, static early warning becomes dynamic, which is of great significance to resource development, environmental protection and risk prediction. This research introduces SVR, GA and ACO algorithms to implement the early warning model of SD of resource-based industrial clusters (Thapa and Adhikari, 2019). However, the proposed early warning system is offline and non-real-time, which cannot deal with the demand for online timing early warning.…”

Section: Construction Of An Early-warning Model For Sd-oriented Resou...mentioning

confidence: 99%

Sustainable development early warning and financing risk management of resource-based industrial clusters using optimization algorithms

Wang

Xue

2022

JEIM

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PurposeThe purpose is to analyze and discuss the sustainable development (SD) and financing risk assessment (FRA) of resource-based industrial clusters under the Internet of Things (IoT) economy and promote the application of Machine Learning methods and intelligent optimization algorithms in FRA.Design/methodology/approachThis study used the Support Vector Machine (SVM) algorithm that is analyzed together with the Genetic Algorithm (GA) and Ant Colony Optimization (ACO) algorithm. First, Yulin City in Shaanxi Province is selected for case analysis. Then, resource-based industrial clusters are studied, and an SD early-warning model is implemented. Then, the financing Risk Assessment Index System is established from the perspective of construction-operation-transfer. Finally, the risk assessment results of Support Vector Regression (SVR) and ACO-based SVR (ACO-SVR) are analyzed.FindingsThe results show that the overall sustainability of resource-based industrial clusters and IoT industrial clusters is good in the Yulin City of Shaanxi Province, and the early warning model of GA-based SVR (GA-SVR) has been achieved good results. Yulin City shows an excellent SD momentum in the resource-based industrial cluster, but there are still some risks. Therefore, it is necessary to promote the industrial structure of SD and improve the stability of the resource-based industrial cluster for Yulin City.Originality/valueThe results can provide a direction for the research on the early warning and evaluation of the SD-oriented resource-based industrial clusters and the IoT industrial clusters, promoting the application of SVM technology in the engineering field.

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“…The disadvantages of using extensometers are that they are sensitive to temperature variations, the need of expert technical staff for the installation of borehole extensometers, and the necessity to protect surface extensometers. Extensometers are widely used in early warning applications [138], [139], [140].…”

Section: Geotechnical Instrumentsmentioning

confidence: 99%

Review of Landslide Monitoring Techniques With IoT Integration Opportunities

Hemalatha

Uhlemann

Reghunadh

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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With the advancements of technology in the era of big data and artificial intelligence, IoT (Internet of Things) has a major role for the purpose of monitoring natural disasters like landslides. Landslides are a catastrophic disaster worldwide that alter from terrain to terrain. In the pursuit of saving communities that are endangered by landslides, many monitoring techniques are practiced. This paper is a survey of the major landslide monitoring techniques adapted in different parts of the world to monitor unstable slopes. It provides a glance into the challenges and opportunities of integrating IoT into the major landslide monitoring techniques, which are explained briefly with emphasis on real-world case studies. Each technique is presented regarding the kind of monitoring parameters, the type of landslides that it can monitor, landslide investigating phases, advantages, disadvantages, and the possibility of IoT to integrate with each techniques. This paper also aims to provide an overview of landslide monitoring in general to non-specialist in the field. The major monitoring techniques are classified based on the type (fall, topple, slide, spread, flow, slope deformation), velocity (slow, moderate, rapid), monitoring parameters (meteorological, geological, hydro-geological, physical, geophysical), monitoring phases (spatial, temporal) and early-warning systems (spatial, temporal) of landslides. This classification will serve as a guideline (but not a replacement for expert advice) for selecting appropriate landslide monitoring technique and the classifications are expressed through visual representations.

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Development of community-based landslide early warning system in the earthquake-affected areas of Nepal Himalaya

Cited by 24 publications

References 56 publications

Towards an improved understanding of participation in natural hazard early warning systems

Towards an improved understanding of participation in natural hazard early warning systems

Sustainable development early warning and financing risk management of resource-based industrial clusters using optimization algorithms

Review of Landslide Monitoring Techniques With IoT Integration Opportunities

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