The Application of Remote Sensing Technology to the Interpretation of Land Use for Rainfall-Induced Landslides Based on Genetic Algorithms and Artificial Neural Networks

Yie Ruey Chen; Shun Chieh Hsieh; Po Ning Ni; Jing-Wen Chen

doi:10.1109/JSTARS.2009.2023802

The Application of Remote Sensing Technology to the Interpretation of Land Use for Rainfall-Induced Landslides Based on Genetic Algorithms and Artificial Neural Networks

Yie Ruey Chen, Shun Chieh Hsieh, Po Ning Ni, Jing-Wen Chen

Department of Civil Engineering

Research output: Contribution to journal › Article › peer-review

47 Citations (Scopus)

Abstract

In this paper, we explore the relationship between land use practices and landslides triggered by rainfall in eastern Taiwan. Before-and-after satellite images, combined with an artificial neural network method, enable the classification of land use and landslide zones. Genetic algorithms are used to evaluate the land use factors causing landslides. Using the geographic information system ArcGIS to support spatial reasoning, predictive maps are produced. The results suggest that the proposed method and procedures can be an effective tool for landslide monitoring and would be easily transferred to other similar applications.

Original language	English
Pages (from-to)	87-95
Number of pages	9
Journal	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Volume	2
Issue number	2
DOIs	https://doi.org/10.1109/JSTARS.2009.2023802
Publication status	Published - 2009 Jan 1

All Science Journal Classification (ASJC) codes

Computers in Earth Sciences
Atmospheric Science

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/JSTARS.2009.2023802

Cite this

@article{aa87d64a15844642a920b53788fefa5a,

title = "The Application of Remote Sensing Technology to the Interpretation of Land Use for Rainfall-Induced Landslides Based on Genetic Algorithms and Artificial Neural Networks",

abstract = "In this paper, we explore the relationship between land use practices and landslides triggered by rainfall in eastern Taiwan. Before-and-after satellite images, combined with an artificial neural network method, enable the classification of land use and landslide zones. Genetic algorithms are used to evaluate the land use factors causing landslides. Using the geographic information system ArcGIS to support spatial reasoning, predictive maps are produced. The results suggest that the proposed method and procedures can be an effective tool for landslide monitoring and would be easily transferred to other similar applications.",

author = "Chen, {Yie Ruey} and Hsieh, {Shun Chieh} and Ni, {Po Ning} and Jing-Wen Chen",

year = "2009",

month = jan,

day = "1",

doi = "10.1109/JSTARS.2009.2023802",

language = "English",

volume = "2",

pages = "87--95",

journal = "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing",

issn = "1939-1404",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

number = "2",

}

Chen, YR, Hsieh, SC, Ni, PN & Chen, J-W 2009, 'The Application of Remote Sensing Technology to the Interpretation of Land Use for Rainfall-Induced Landslides Based on Genetic Algorithms and Artificial Neural Networks', IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 2, no. 2, pp. 87-95. https://doi.org/10.1109/JSTARS.2009.2023802

The Application of Remote Sensing Technology to the Interpretation of Land Use for Rainfall-Induced Landslides Based on Genetic Algorithms and Artificial Neural Networks. / Chen, Yie Ruey; Hsieh, Shun Chieh; Ni, Po Ning et al.
In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 2, No. 2, 01.01.2009, p. 87-95.

Research output: Contribution to journal › Article › peer-review

TY - JOUR

T1 - The Application of Remote Sensing Technology to the Interpretation of Land Use for Rainfall-Induced Landslides Based on Genetic Algorithms and Artificial Neural Networks

AU - Chen, Yie Ruey

AU - Hsieh, Shun Chieh

AU - Ni, Po Ning

AU - Chen, Jing-Wen

PY - 2009/1/1

Y1 - 2009/1/1

N2 - In this paper, we explore the relationship between land use practices and landslides triggered by rainfall in eastern Taiwan. Before-and-after satellite images, combined with an artificial neural network method, enable the classification of land use and landslide zones. Genetic algorithms are used to evaluate the land use factors causing landslides. Using the geographic information system ArcGIS to support spatial reasoning, predictive maps are produced. The results suggest that the proposed method and procedures can be an effective tool for landslide monitoring and would be easily transferred to other similar applications.

AB - In this paper, we explore the relationship between land use practices and landslides triggered by rainfall in eastern Taiwan. Before-and-after satellite images, combined with an artificial neural network method, enable the classification of land use and landslide zones. Genetic algorithms are used to evaluate the land use factors causing landslides. Using the geographic information system ArcGIS to support spatial reasoning, predictive maps are produced. The results suggest that the proposed method and procedures can be an effective tool for landslide monitoring and would be easily transferred to other similar applications.

UR - http://www.scopus.com/inward/record.url?scp=70349254192&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=70349254192&partnerID=8YFLogxK

U2 - 10.1109/JSTARS.2009.2023802

DO - 10.1109/JSTARS.2009.2023802

M3 - Article

AN - SCOPUS:70349254192

SN - 1939-1404

VL - 2

SP - 87

EP - 95

JO - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

JF - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

IS - 2

ER -

The Application of Remote Sensing Technology to the Interpretation of Land Use for Rainfall-Induced Landslides Based on Genetic Algorithms and Artificial Neural Networks

Abstract

All Science Journal Classification (ASJC) codes

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this