Application of artificial neural network model based on GIS in geological hazard zoning

Tan, Qulin; Huang, Yong; Hu, Jun; Zhou, Pinggen; Hu, Jiping

doi:10.1007/s00521-020-04987-4

Cited by 32 publications

(13 citation statements)

References 25 publications

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“…These learning algorithms may provide the probability of the spatial occurrence of a landslide and identify the importance of different geo-environmental causal factors that play a potential role in these landslide events [4]. Several machine learning approaches have been utilized for landslide assessment in the past decade, such as Support Vector Machines (SVMs) [5,6], Artificial Neural Networks (ANNs) [7,8], Deep Learning Neural Networks (DLNNs) [9,10], Convolutional Neural Networks (CNNs) [11], Boosted Regression Trees (BRTs) [12,13], and Random Forests (RFs) [13]. A number of case studies show that these algorithms have a good prediction performance [14,15]; in particular, the RF has gained a high reputation for its outperformance in both classification and prediction compared to other approaches.…”

Section: Introductionmentioning

confidence: 99%

Mapping Landslide Hazard Risk Using Random Forest Algorithm in Guixi, Jiangxi, China

Zhang

Qin

et al. 2020

IJGI

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Landslide hazards affect the security of human life and property. Mapping the spatial distribution of landslide hazard risk is critical for decision-makers to implement disaster prevention measures. This study aimed to predict and zone landslide hazard risk, using Guixi County in eastern Jiangxi, China, as an example. An integrated dataset composed of 21 geo-information layers, including lithology, rainfall, altitude, slope, distances to faults, roads and rivers, and thickness of the weathering crust, was used to achieve the aim. Non-digital layers were digitized and assigned weights based on their landslide propensity. Landslide locations and non-risk zones (flat areas) were both vectorized as polygons and randomly divided into two groups to create a training set (70%) and a validation set (30%). Using this training set, the Random Forests (RF) algorithm, which is known for its accurate prediction, was applied to the integrated dataset for risk modeling. The results were assessed against the validation set. Overall accuracy of 91.23% and Kappa Coefficient of 0.82 were obtained. The calculated probability for each pixel was consequently graded into different zones for risk mapping. Hence, we conclude that landslide risk zoning using the RF algorithm can serve as a pertinent reference for local government in their disaster prevention and early warning measures.

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Section: Introductionmentioning

confidence: 99%

Mapping Landslide Hazard Risk Using Random Forest Algorithm in Guixi, Jiangxi, China

Zhang

Qin

et al. 2020

IJGI

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“…e future development trend will be the mapping and combination of fuzzy logic to neural network structure; that is, through the replacement of fuzzy membership function and network transfer function, the connection weight of the network can be dynamically adjusted by fuzzy rules. e fuzzy neural network with this structure and performance will have greater adaptability to many uncertain problems in the prediction of geologically complicated fault structure [10].…”

Section: Fuzzy Logic Analysis Algorithmmentioning

confidence: 99%

Prediction Model for Geologically Complicated Fault Structure Based on Artificial Neural Network and Fuzzy Logic

Liu

Yang³

et al. 2022

Scientific Programming

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The development and distribution of geologically complicated fault structure have the characteristics of uncertainty, randomness, ambiguity, and variability. Therefore, the prediction of complicated fault structures is a typical nonlinear problem. Neither fuzzy logic method nor artificial neural network alone can solve this problem well because the fuzzy method is generally not easy to realize adaptive learning function, and the neural network method is not suitable for describing sedimentary microfacies or geophysical facies. Therefore, taking the marginal subsags in the Jiyang Depression, Eastern China, as a study case, this paper uses the method of combining artificial neural network and fuzzy logic to study geologically complicated fault structure prediction model. This paper expounds on the research status and significance of geologically complicated fault structure prediction model, elaborates the development background, current status, and future challenges of artificial neural networks and fuzzy logic, introduces the method and principle of fuzzy neural network structure and fuzzy logic analysis algorithm, conducts prediction model design and implementation based on fuzzy neural network, proposes the learning algorithm of fuzzy neural network, analyzes the programming realization of fuzzy neural network, constructs complicated fault structure prediction model based on the artificial neural network and fuzzy logic, performs the fuzzy logic system selection of complicated fault structure prediction model, carries out the artificial neural network structure design of complicated fault structure prediction model, compares the prediction effects of the geologically complicated fault structure model based on artificial neural networks and fuzzy logic, and finally discusses the system design and optimization of the prediction model for geologically complicated fault structures. The study results show that the fuzzy neural network fully integrates the advantages of artificial neural network and fuzzy logic system; based on the clear physical background of fuzzy logic system, it effectively integrates powerful knowledge expression ability and fuzzy reasoning ability into the network knowledge structure of neural network, which greatly improves the prediction accuracy of geologically complicated fault structure.

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“…However, this process is susceptible to human experience, inefficient, and costly. Recently, with the rapid development of artificial intelligence, a considerable number of methods have been proposed to solve geological problems in a smarter and more convenient way by using an artificial neural network (ANN) [1][2][3][4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Mineral Photos Recognition Based on Feature Fusion and Online Hard Sample Mining

et al. 2021

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Mineral recognition is of importance in geological research. Traditional mineral recognition methods need professional knowledge or special equipment, are susceptible to human experience, and are inconvenient to carry in some conditions such as in the wild. The development of computer vision provides a possibility for convenient, fast, and intelligent mineral recognition. Recently, several mineral recognition methods based on images using a neural network have been proposed for this aim. However, these methods do not exploit features extracted from the backbone network or available information of the samples in the mineral dataset sufficiently, resulting in low recognition accuracy. In this paper, a method based on feature fusion and online hard sample mining is proposed to improve recognition accuracy by using only mineral photo images. This method first fuses multi-resolution features extracted from ResNet-50 to obtain comprehensive information of mineral photos, and then proposes the weighted top-k loss to emphasize the learning of hard samples. Based on a dataset consisting of 14,986 images of 22 common minerals, the proposed method with 10-fold cross-validation achieves a Top1 accuracy of 88.01% on the validation image set, surpassing those of Inception-v3 and EfficientNet-B0 by a margin of 1.88% and 1.29%, respectively, which demonstrates the good prospect of the proposed method for convenient and reliable mineral recognition using mineral photos only.

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Application of artificial neural network model based on GIS in geological hazard zoning

Cited by 32 publications

References 25 publications

Mapping Landslide Hazard Risk Using Random Forest Algorithm in Guixi, Jiangxi, China

Mapping Landslide Hazard Risk Using Random Forest Algorithm in Guixi, Jiangxi, China

Prediction Model for Geologically Complicated Fault Structure Based on Artificial Neural Network and Fuzzy Logic

Mineral Photos Recognition Based on Feature Fusion and Online Hard Sample Mining

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