Comparison of Tree-Structured Parzen Estimator Optimization in Three Typical Neural Network Models for Landslide Susceptibility Assessment

Landslides pose a constant threat to the lives and property of mountain people and may also cause geomorphological destruction such as soil and water loss, vegetation destruction, and land cover change. Landslide susceptibility assessment (LSA) is a key component of landslide risk evaluation. There...

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Autores principales:	Guangzhi Rong, Kaiwei Li, Yulin Su, Zhijun Tong, Xingpeng Liu, Jiquan Zhang, Yichen Zhang, Tiantao Li
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	landslide susceptibility assessment deep neural network recurrent neural network convolutional neural network hyperparameter optimization tree-structured Parzen estimator algorithm Science Q
Acceso en línea:	https://doaj.org/article/993690e3392a42b99f8f5c8431e63e1d
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spelling	oai:doaj.org-article:993690e3392a42b99f8f5c8431e63e1d2021-11-25T18:55:28ZComparison of Tree-Structured Parzen Estimator Optimization in Three Typical Neural Network Models for Landslide Susceptibility Assessment10.3390/rs132246942072-4292https://doaj.org/article/993690e3392a42b99f8f5c8431e63e1d2021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/22/4694https://doaj.org/toc/2072-4292Landslides pose a constant threat to the lives and property of mountain people and may also cause geomorphological destruction such as soil and water loss, vegetation destruction, and land cover change. Landslide susceptibility assessment (LSA) is a key component of landslide risk evaluation. There are many related studies, but few analyses and comparisons of models for optimization. This paper aims to introduce the Tree-structured Parzen Estimator (TPE) algorithm for hyperparameter optimization of three typical neural network models for LSA in Shuicheng County, China, as an example, and to compare the differences of predictive ability among the models in order to achieve higher application performance. First, 17 influencing factors of landslide multiple data sources were selected for spatial prediction, hybrid ensemble oversampling and undersampling techniques were used to address the imbalanced sample and small sample size problem, and the samples were randomly divided into a training set and validation set. Second, deep neural network (DNN), recurrent neural network (RNN), and convolutional neural network (CNN) models were adopted to predict the regional landslides susceptibility, and the TPE algorithm was used to optimize the hyperparameters respectively to improve the assessment capacity. Finally, to compare the differences and optimization effects of these models, several objective measures were applied for validation. The results show that the high-susceptibility regions mostly distributed in bands along fault zones, where the lithology is mostly claystone, sandstone, and basalt. The DNN, RNN, and CNN models all perform well in LSA, especially the RNN model. The TPE optimization significantly improves the accuracy of the DNN and CNN (3.92% and 1.52%, respectively), but does not improve the performance of the RNN. In summary, our proposed RNN model and TPE-optimized DNN and CNN model have robust predictive capability for landslide susceptibility in the study area and can also be applied to other areas containing similar geological conditions.Guangzhi RongKaiwei LiYulin SuZhijun TongXingpeng LiuJiquan ZhangYichen ZhangTiantao LiMDPI AGarticlelandslide susceptibility assessmentdeep neural networkrecurrent neural networkconvolutional neural networkhyperparameter optimizationtree-structured Parzen estimator algorithmScienceQENRemote Sensing, Vol 13, Iss 4694, p 4694 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	landslide susceptibility assessment deep neural network recurrent neural network convolutional neural network hyperparameter optimization tree-structured Parzen estimator algorithm Science Q
spellingShingle	landslide susceptibility assessment deep neural network recurrent neural network convolutional neural network hyperparameter optimization tree-structured Parzen estimator algorithm Science Q Guangzhi Rong Kaiwei Li Yulin Su Zhijun Tong Xingpeng Liu Jiquan Zhang Yichen Zhang Tiantao Li Comparison of Tree-Structured Parzen Estimator Optimization in Three Typical Neural Network Models for Landslide Susceptibility Assessment
description	Landslides pose a constant threat to the lives and property of mountain people and may also cause geomorphological destruction such as soil and water loss, vegetation destruction, and land cover change. Landslide susceptibility assessment (LSA) is a key component of landslide risk evaluation. There are many related studies, but few analyses and comparisons of models for optimization. This paper aims to introduce the Tree-structured Parzen Estimator (TPE) algorithm for hyperparameter optimization of three typical neural network models for LSA in Shuicheng County, China, as an example, and to compare the differences of predictive ability among the models in order to achieve higher application performance. First, 17 influencing factors of landslide multiple data sources were selected for spatial prediction, hybrid ensemble oversampling and undersampling techniques were used to address the imbalanced sample and small sample size problem, and the samples were randomly divided into a training set and validation set. Second, deep neural network (DNN), recurrent neural network (RNN), and convolutional neural network (CNN) models were adopted to predict the regional landslides susceptibility, and the TPE algorithm was used to optimize the hyperparameters respectively to improve the assessment capacity. Finally, to compare the differences and optimization effects of these models, several objective measures were applied for validation. The results show that the high-susceptibility regions mostly distributed in bands along fault zones, where the lithology is mostly claystone, sandstone, and basalt. The DNN, RNN, and CNN models all perform well in LSA, especially the RNN model. The TPE optimization significantly improves the accuracy of the DNN and CNN (3.92% and 1.52%, respectively), but does not improve the performance of the RNN. In summary, our proposed RNN model and TPE-optimized DNN and CNN model have robust predictive capability for landslide susceptibility in the study area and can also be applied to other areas containing similar geological conditions.
format	article
author	Guangzhi Rong Kaiwei Li Yulin Su Zhijun Tong Xingpeng Liu Jiquan Zhang Yichen Zhang Tiantao Li
author_facet	Guangzhi Rong Kaiwei Li Yulin Su Zhijun Tong Xingpeng Liu Jiquan Zhang Yichen Zhang Tiantao Li
author_sort	Guangzhi Rong
title	Comparison of Tree-Structured Parzen Estimator Optimization in Three Typical Neural Network Models for Landslide Susceptibility Assessment
title_short	Comparison of Tree-Structured Parzen Estimator Optimization in Three Typical Neural Network Models for Landslide Susceptibility Assessment
title_full	Comparison of Tree-Structured Parzen Estimator Optimization in Three Typical Neural Network Models for Landslide Susceptibility Assessment
title_fullStr	Comparison of Tree-Structured Parzen Estimator Optimization in Three Typical Neural Network Models for Landslide Susceptibility Assessment
title_full_unstemmed	Comparison of Tree-Structured Parzen Estimator Optimization in Three Typical Neural Network Models for Landslide Susceptibility Assessment
title_sort	comparison of tree-structured parzen estimator optimization in three typical neural network models for landslide susceptibility assessment
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/993690e3392a42b99f8f5c8431e63e1d
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Comparison of Tree-Structured Parzen Estimator Optimization in Three Typical Neural Network Models for Landslide Susceptibility Assessment

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