Spatial case revision in case-based reasoning for risk assessment of geological disasters

Deng, Shuiguang; Li, Wenshu

doi:10.1080/19475705.2020.1774427

Cited by 11 publications

(4 citation statements)

References 28 publications

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“…Recall rate (Rec): is taken to measure the coverage of positive samples, that is, the proportion of positive samples that are correctly classified to the total positive samples, as shown in equation (5). Pre and Recall sometimes have conflicts, so they need to be considered comprehensively.…”

Section: Tp Tn Acc Tp Tn Fp Fnmentioning

confidence: 99%

“…It not only affects the safety of life and property, but also causes irreversible damage to the human living environment [4]. Common geologic hazards include collapse, landslide, mudslide, ground subsidence, ground fissure, and ground subsidence, which all pose a serious threat to life and property [5]. Geologic hazard will not only bring immeasurable losses to a country or individual's economy, it will also greatly destroy people's living conditions and stability.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of Radar Technology Identification Model for Potential Geologic Hazard based on Convolutional Neural Network and Big Data

et al. 2021

Preprint

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To ensure the proper adoption of new technologies in identifying the potential geologic hazard on tourist routes, convolutional neural network (CNN) technology is applied in the radar image geologic hazard information extraction. A scientific and practical geologic hazard radar identification model is built, which is based on CNN’s image identification and big data algorithm calculation, and it can effectively improve the geologic hazard identification accuracy. By designing experiments, the geologic hazard radar image data are verified, and the practicality of radar image intelligent Identification under CNN and big data technology is also verified. The results show that the images of different resolution sizes all play a significant role in identification of geologic hazard performed by CNN. However, there are differences in the performance of different CNN models. With the continuous increase of training samples, the identification accuracy of various network models is also improved. By means of radar image test, the identification capability of CNN model is the best, the highest precision is 93.61%, and the geologic hazard recall rate is 98.27%. Apriori algorithm is introduced into data processing, and the running speed and efficiency of identification models are improved, with favorable identification effect in variable data sets. This research can provide theoretical ideas and practical value for the development of potential geologic hazard identification on tourist routes.

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Section: Tp Tn Acc Tp Tn Fp Fnmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis of Radar Technology Identification Model for Potential Geologic Hazard based on Convolutional Neural Network and Big Data

et al. 2021

Preprint

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“…As a mature branch of artificial intelligence, case-based reasoning (CBR) has been widely applied in other fields [23]. CBR has greater classification performance compared with traditional data mining methods [24] and it has also shown excellent performance in fields like fault diagnosis [25][26][27], risk assessment [28,29], and forest fire prediction [30][31][32]. It should be noted that the weights of case characteristic attributes in CBR have a significant impact on the prediction performance of the model.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Coal Spontaneous Combustion Hazard Grades Based on Fuzzy Clustered Case-Based Reasoning

Pei,

Jia,

Liu

et al. 2024

Fire

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Accurate prediction of the coal spontaneous combustion hazard grades is of great significance to ensure the safe production of coal mines. However, traditional coal temperature prediction models have low accuracy and do not predict the coal spontaneous combustion hazard grades. In order to accurately predict coal spontaneous combustion hazard grades, a prediction model of coal spontaneous combustion based on principal component analysis (PCA), case-based reasoning (CBR), fuzzy clustering (FM), and the snake optimization (SO) algorithm was proposed in this manuscript. Firstly, based on the change rule of the concentration of signature gases in the process of coal warming, a new method of classifying the risk of spontaneous combustion of coal was established. Secondly, MeanRadius-SMOTE was adopted to balance the data structure. The weights of the prediction indicators were calculated through PCA to enhance the prediction precision of the CBR model. Then, by employing FM in the case base, the computational cost of CBR was reduced and its computational efficiency was improved. The SO algorithm was used to determine the hyperparameters in the PCA-FM-CBR model. In addition, multiple comparative experiments were conducted to verify the superiority of the model proposed in this manuscript. The results indicated that SO-PCA-FM-CBR possesses good prediction performance and also improves computational efficiency. Finally, the authors of this manuscript adopted the Random Balance Designs—Fourier Amplitude Sensitivity Test (RBD-FAST) to explain the output of the model and analyzed the global importance of input variables. The results demonstrated that CO is the most important variable affecting the coal spontaneous combustion hazard grades.

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“…There are many domestic studies on the mechanism of single geological disasters such as landslides, but the research progress on the development and distribution of regional geological disasters is relatively slow [ 4 ]. Exploring and forming a set of scientific, complete, and practical research methods for the development and distribution of regional geological disasters, and evaluating the risk of regional geological disasters, is of great practical significance for the prevention and control of regional geological disasters [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Research on the Application of GIS Technology Combined with RBFNN‐GA Algorithm in the Delineation of Geological Hazard Prone Areas

Lei¹,

Yao²,

Zhang

et al. 2021

Computational Intelligence and Neuroscience

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With the rapid development of the economy and society, geological disasters such as landslides, collapses, and mudslides have shown an intensifying trend, seriously endangering the safety of people’s lives and property, and affecting the sustainable development of the economy and society. Aiming at the problems of merging different data layers and determining the weighting of data stacking in the statistical analysis model based on GIS technology in the evaluation of the risk of geological disasters, this study proposes a logistic regression model combined with the RBFNN-GA algorithm, that is, the determination of the occurrence of geological disasters. The fusion coefficient (CF value) with the RBFNN-GA algorithm model, and with the help of SPSS statistical analysis software, solves the problem of factor selection, heterogeneous data merging, and weighting of each data layer in the risk assessment. In the experimental stage, this study adopts the method of geological hazard certainty coefficients to carry out the sensitivity analysis of the geological hazards in the study area. Using homogeneous grid division, the spatial quantitative evaluation of the risk of geological disasters is realized, and at the same time, the results of the spatial quantitative evaluation of the risk of geological disasters are tested according to the latest landslide points in the region. The existing classification mainly depends on the acquisition of land use/cover information or the processing method of the acquired information, but the existing information acquisition will be limited by time, space, and spectral resolution. The results show that the number of landslide points per unit area in the extremely unstable zone and the unstable zone is 0.0395 points/km2 and 0.0251 points/km2, respectively, which is much higher than 0.0038 points/km2 in the stable zone, indicating the evaluation results and actual landslide conditions.

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Spatial case revision in case-based reasoning for risk assessment of geological disasters

Cited by 11 publications

References 28 publications

Analysis of Radar Technology Identification Model for Potential Geologic Hazard based on Convolutional Neural Network and Big Data

Analysis of Radar Technology Identification Model for Potential Geologic Hazard based on Convolutional Neural Network and Big Data

Prediction of Coal Spontaneous Combustion Hazard Grades Based on Fuzzy Clustered Case-Based Reasoning

Research on the Application of GIS Technology Combined with RBFNN‐GA Algorithm in the Delineation of Geological Hazard Prone Areas

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