Soft computing machine learning applications for assessing regional-scale landslide susceptibility in the Nepal Himalaya

IF 1.5 4区 工程技术 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Bikesh Manandhar, Thanh-Canh Huynh, Pawan Kumar Bhattarai, Suchita Shrestha, Ananta Man Singh Pradhan
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引用次数: 0

Abstract

Purpose

This research is aimed at preparing landslide susceptibility using spatial analysis and soft computing machine learning techniques based on convolutional neural networks (CNNs), artificial neural networks (ANNs) and logistic regression (LR) models.

Design/methodology/approach

Using the Geographical Information System (GIS), a spatial database including topographic, hydrologic, geological and landuse data is created for the study area. The data are randomly divided between a training set (70%), a validation (10%) and a test set (20%).

Findings

The validation findings demonstrate that the CNN model (has an 89% success rate and an 84% prediction rate). The ANN model (with an 84% success rate and an 81% prediction rate) predicts landslides better than the LR model (with a success rate of 82% and a prediction rate of 79%). In comparison, the CNN proves to be more accurate than the logistic regression and is utilized for final susceptibility.

Research limitations/implications

Land cover data and geological data are limited in largescale, making it challenging to develop accurate and comprehensive susceptibility maps.

Practical implications

It helps to identify areas with a higher likelihood of experiencing landslides. This information is crucial for assessing the risk posed to human lives, infrastructure and properties in these areas. It allows authorities and stakeholders to prioritize risk management efforts and allocate resources more effectively.

Social implications

The social implications of a landslide susceptibility map are profound, as it provides vital information for disaster preparedness, risk mitigation and landuse planning. Communities can utilize these maps to identify vulnerable areas, implement zoning regulations and develop evacuation plans, ultimately safeguarding lives and property. Additionally, access to such information promotes public awareness and education about landslide risks, fostering a proactive approach to disaster management. However, reliance solely on these maps may also create a false sense of security, necessitating continuous updates and integration with other risk assessment measures to ensure effective disaster resilience strategies are in place.

Originality/value

Landslide susceptibility mapping provides a proactive approach to identifying areas at higher risk of landslides before any significant events occur. Researchers continually explore new data sources, modeling techniques and validation approaches, leading to a better understanding of landslide dynamics and susceptibility factors.

应用软计算机器学习评估尼泊尔喜马拉雅地区的区域尺度滑坡易发性
目的本研究旨在利用空间分析和基于卷积神经网络 (CNN)、人工神经网络 (ANN) 和逻辑回归 (LR) 模型的软计算机器学习技术,为滑坡易发性做准备。结果验证结果表明,CNN 模型的成功率为 89%,预测率为 84%。ANN 模型(成功率为 84%,预测率为 81%)比 LR 模型(成功率为 82%,预测率为 79%)更能预测滑坡。相比之下,CNN 被证明比逻辑回归更准确,并被用于最终的易损性预测。研究局限性/意义土地覆盖数据和地质数据在大范围内是有限的,这使得绘制准确而全面的易损性地图具有挑战性。这些信息对于评估这些地区的人类生命、基础设施和财产所面临的风险至关重要。社会影响滑坡易发区地图具有深远的社会影响,因为它为备灾、降低风险和土地使用规划提供了重要信息。社区可以利用这些地图来确定易受影响的地区,实施分区法规并制定疏散计划,最终保障生命和财产安全。此外,获取这些信息还能提高公众对滑坡风险的认识和教育,促进积极主动的灾害管理方法。然而,仅仅依靠这些地图也可能会产生虚假的安全感,因此需要不断更新并与其他风险评估措施相结合,以确保制定有效的抗灾战略。研究人员不断探索新的数据来源、建模技术和验证方法,从而更好地了解滑坡动态和易发因素。
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来源期刊
Engineering Computations
Engineering Computations 工程技术-工程:综合
CiteScore
3.40
自引率
6.20%
发文量
61
审稿时长
5 months
期刊介绍: The journal presents its readers with broad coverage across all branches of engineering and science of the latest development and application of new solution algorithms, innovative numerical methods and/or solution techniques directed at the utilization of computational methods in engineering analysis, engineering design and practice. For more information visit: http://www.emeraldgrouppublishing.com/ec.htm
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