2019
DOI: 10.5194/nhess-19-629-2019
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Application of the Levenburg–Marquardt back propagation neural network approach for landslide risk assessments

Abstract: Abstract. Landslide disasters are one of the main risks involved with the operation of long-distance oil and gas pipelines. Because previously established disaster risk models are too subjective, this paper presents a quantitative model for regional risk assessment through an analysis of the patterns of historical landslide disasters along oil and gas pipelines. Using the Guangyuan section of the Lanzhou–Chengdu–Chongqing (LCC) long-distance multiproduct oil pipeline (82 km) in China as a case study, we succes… Show more

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Cited by 38 publications
(15 citation statements)
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References 30 publications
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“…(2004) classification, GIS-based hydrological analysis and modeling tools are implemented to divide the watershed into slope units afterward. In order to make the division accuracy of risk assessment results consistent, Xiong et al (2019) used the slope units to assess the impact of landslide risk on the operation of oil and gas long-distance pipelines. Based on the previous experience, this study completes the processing chart of delimited slope units, as illustrated in Fig.…”
Section: Delimited Slope Unitsmentioning
confidence: 99%
“…(2004) classification, GIS-based hydrological analysis and modeling tools are implemented to divide the watershed into slope units afterward. In order to make the division accuracy of risk assessment results consistent, Xiong et al (2019) used the slope units to assess the impact of landslide risk on the operation of oil and gas long-distance pipelines. Based on the previous experience, this study completes the processing chart of delimited slope units, as illustrated in Fig.…”
Section: Delimited Slope Unitsmentioning
confidence: 99%
“…Algoritma feed-forward, back-propagation, dan fungsi aktivasi digunakan dalam struktur model ANN (Dou et al, 2015;Rumelhart et al, 1986;Sarkar & Sharma, 2011). Algoritma levenverg-marquadt memiliki kemampuan lebih cepat untuk meminimalisir nilai kesalahan dalam jaringan syaraf tiruan, tetapi membutuhkan daya komputasi yang besar (Sarkar & Sharma, 2011;Xiong et al, 2019). Normalisasi data faktor pengontrol juga dilakukan agar komputasi lebih cepat, kesalahan minimal, dan tidak terjadi over tting model.…”
Section: Model Arti Cial Neural Networkunclassified
“…Each index was normalized using the extremum difference method (Jin et al, 2015). A weighting for each parameter was determined using the entropy weight method (Sun et al, 2019). And the weight of each index is shown in Table 2.…”
Section: Comprehensive Assessmentmentioning
confidence: 99%