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; Ni, Po-Ning; Chen, Jingwen; Hsieh, Shun-Chieh

doi:10.1109/jstars.2009.2023802

Cited by 41 publications

(21 citation statements)

References 19 publications

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“…This is done by seeking to minimize each class' average deviation from the class mean, while maximizing each class' deviation from the means of other groups. In other words, the method seeks to reduce the variance within classes and maximize the variance between classes [34]. Details of the three models are provided in the following subsections.…”

Section: Methodsmentioning

confidence: 99%

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An Improved Information Value Model Based on Gray Clustering for Landslide Susceptibility Mapping

Chen

Deng³

et al. 2017

IJGI

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Landslides, as geological hazards, cause significant casualties and economic losses. Therefore, it is necessary to identify areas prone to landslides for prevention work. This paper proposes an improved information value model based on gray clustering (IVM-GC) for landslide susceptibility mapping. This method uses the information value derived from an information value model to achieve susceptibility classification and weight determination of landslide predisposing factors and, hence, obtain the landslide susceptibility of each study unit based on the clustering analysis. Using a landslide inventory of Chongqing, China, which contains 8435 landslides, three landslide susceptibility maps were generated based on the common information value model (IVM), an information value model improved by an analytic hierarchy process (IVM-AHP) and our new improved model. Approximately 70% (5905) of the inventory landslides were used to generate the susceptibility maps, while the remaining 30% (2530) were used to validate the results. The training accuracies of the IVM, IVM-AHP and IVM-GC were 81.8%, 78.7% and 85.2%, respectively, and the prediction accuracies were 82.0%, 78.7% and 85.4%, respectively. The results demonstrate that all three methods perform well in evaluating landslide susceptibility. Among them, IVM-GC has the best performance.

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Section: Methodsmentioning

confidence: 99%

“…NDVI is an important index denoting a region's vegetation cover, and it is an important factor for landslide occurrence and movement [33]. Plant roots can hold the soil to mitigate the effect of rainfall [34]. Theoretically, the possibility of landslide occurrence gradually decreases with increasing NDVI value [35].…”

Section: Landslide Predisposing Factorsmentioning

confidence: 99%

An Improved Information Value Model Based on Gray Clustering for Landslide Susceptibility Mapping

Chen

Deng³

et al. 2017

IJGI

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“…2) Overview and Construction of ANN Model: ANN makes use of nonlinear and complex learning and prediction algorithms to extract the complex relationships among the various factors controlling debris-flow occurrences [67], [72]. In this study, a pattern-recognition neural-network module of MAT-LAB R2013a was utilized to uncover debris-flow patterns in the study area and conduct susceptibility analyses.…”

Section: ) Input Preprocessing For Ann and Lr Modelsmentioning

confidence: 99%

A Remote Sensing-Based Approach for Debris-Flow Susceptibility Assessment Using Artificial Neural Networks and Logistic Regression Modeling

Elkadiri

Sultan

Youssef

et al. 2014

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

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Efforts to map the distribution of debris flows, to assess the factors controlling their development, and to identify the areas susceptible to their occurrences are often hampered by the paucity of monitoring systems and historical databases in many parts of the world. In this paper, we develop and successfully apply methodologies that rely heavily on readily available remote-sensing datasets over the Jazan province in the Red Sea hills of Saudi Arabia. A fivefold exercise was conducted: 1) a geographical information system (GIS) with a Web interface was generated to host and analyze relevant coregistered remote-sensing data and derived products; 2) an inventory was compiled for debris flows identified from satellite datasets (e.g., GeoEye, Orbview), a subset of which was field verified; 3) spatial analyses were conducted in a GIS environment and 10 predisposing factors were identified; 4) an artificial neural network (ANN) model and a logistic regression (LR) model were constructed, optimized, and validated; and 5) the generated models were used to produce debris-flow susceptibility maps. Findings include: 1) excellent prediction performance for both models (ANN: 96.1%; LR: 96.3%); 2) the high correspondence between model outputs (91.5% of the predictions were common) reinforces the validity of the debris-flow susceptibility results; 3) the variables with the highest predictive power were topographic position index (TPI), slope, distance to drainage line (DTDL), and normalized difference vegetation index (NDVI); and 4) the adopted methodologies are reliable, cost-effective, and could potentially be applied over many of the world's data-scarce mountainous lands, particularly along the Red Sea Hills.Index Terms-Artificial neural networks (ANN), data mining, data-scarce field regions, debris flows, geographical information system (GIS), logistic regression (LR), remote sensing.

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“…Penelitian ini tidak mengkaji secara mendalam pengaruh parameter JST dalam menghasilkan akurasi hasil prediksi, sehingga parameter yang digunakan hanya berdasarkan kombinasi dengan akurasi terbaik yang dilakukan oleh peneliti terdahulu (Arif dan Danoedoro, 2013;Chen et al, 2013;Song et al 2013). Proses simulasi prediksi erosi dilakukan pada software IDRISI Selva.…”

Section: Gambar 2 Peta Faktor K Das Serangunclassified

Pemodelan Spasial Erosi Kualitatif Berbasis Raster Studi Kasus di DAS Serang, Kabupaten Kulonprogo

2018

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ABSTRAKErosi merupakan salah satu fenomena alam yang banyak dikaji karena melibatkan banyak faktor yaitu vegetasi, tanah, iklim, topografi dan manusia. Kompleksitas faktor-faktor yang mempengaruhi erosi disederhanakan melalui pemodelan untuk memprediksi tingkat erosi pada suatu wilayah dengan memanfaatkan data penginderaan jauh dan sistem informasi geografis. Faktor yang digunakan dalam menyusun model hanya melibatkan tiga faktor yaitu vegetasi, tanah dan lereng. Penelitian ini dilakukan di DAS Serang karena termasuk salah satu DAS yang berada dalam kondisi kritis yang dapat memicu terjadinya degradasi lahan, erosi dan longsor. Tujuan penelitian ini adalah mengetahui distribusi spasial tingkat erosi kualitatif di DAS Serang. Pendekatan yang digunakan adalah integrasi peginderaan jauh dan sistem informasi geografis berbasis raster. Validasi model dilakukan dengan melihat faktor topografi dan indikator erosi kualitatif di lapangan yaitu armour layer, singkapan akar, pedestal, erosi alur dan gully. Hasil penelitian menunjukan model yang dihasilkan sangat efektif sebagai solusi cepat prediksi erosi. Berdasarkan hasil analisis tingkat erosi sangat berat mendominasi di wilayah kajian yaitu sebagian besar di kecamatan Kokap, Girimulyo dan sebagian Pengasih. ABSTRACTErosion is one of the natural phenomena that's studied by many because it involves many factors, namely vegetation, soil, climate, topography and humans. The complexity of the factors affecting erosion is simplified through modeling to predict of erosion rates in a region by utilizing remote sensing data and geographic information systems. The erosion control factor used in this research fewer parameters, namely vegetation, soil and topography only. This research was conducted in Serang watershed because it is one of the watersheds which are in critical conditions which can trigger land degradation, erosion and landslides. The purpose of this research was to know the spatial distribution of erosion susceptibility levels in Serang watershed. The approach used was the integration of remote sensing and raster-based geographic information system. Model validation was undertaken based on topograhy factor and observation of qualitative erosion indicators in the field. The indicators used were pedestals, armor layers, root exposure, or other erosion featuress such as rill and gullies. The results show that the resulting model is more effective as a quick solution to the prediction of erosion. Based on the results of the analysis, the spatial distribution of erosion rates is very dominant in the study area, mostly in Kokap, Girimulyo and some of the subdistricts.

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

Cited by 41 publications

References 19 publications

An Improved Information Value Model Based on Gray Clustering for Landslide Susceptibility Mapping

An Improved Information Value Model Based on Gray Clustering for Landslide Susceptibility Mapping

A Remote Sensing-Based Approach for Debris-Flow Susceptibility Assessment Using Artificial Neural Networks and Logistic Regression Modeling

Pemodelan Spasial Erosi Kualitatif Berbasis Raster Studi Kasus di DAS Serang, Kabupaten Kulonprogo

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