Debris flow susceptibility mapping in mountainous area based on multi-source data fusion and CNN model – taking Nujiang Prefecture, China as an example

Xu, Fanshu; Wang, Baoyun

doi:10.1080/17538947.2022.2142304

Cited by 8 publications

(4 citation statements)

References 46 publications

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“…It is worth noting that compared with other broad categories, within deep learning, only convolutional neural network was utilized as a baseline predictor of debris flow occurrence, in only five studies. This underscores the need for further study on the applicability of complex network structures in deep learning for predicting debris flow occurrence [50,60].…”

Section: Categoriesmentioning

confidence: 99%

“…Among the eight studies using model coupling, the number of studies using each of the model coupling methods was as follows: coupling of ML model and traditional statistical model (three), coupling of ML and mechanism model (two), and coupling between ML models (two). In one paper using structure optimization, the author improved the structure of the convolutional neural network according to the characteristics of debris flow [50]. models (36) was significantly higher than that of those comparing ML models and non-ML models (10), as shown in Figure 8.…”

Section: Prediction Performance Improvement Strategiesmentioning

confidence: 99%

“…), numerical simulation models (FLO-2D [42], Flow-3D [43,44], Debris2D [45], etc. ), and ML models (LR [46,47], RF [24,48], convolutional neural networks [49,50], etc. ).…”

Section: Introductionmentioning

confidence: 99%

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Machine-Learning-Based Prediction Modeling for Debris Flow Occurrence: A Meta-Analysis

Yang,

Ge,

Chen

et al. 2024

Water

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Machine learning (ML) has become increasingly popular in the prediction of debris flow occurrence, but the various ML models utilized as baseline predictors reported in previous studies are typically limited to individual case bases. A comprehensive and systematic evaluation of existing empirical evidence on the utilization of ML as baseline predictors for debris flow occurrence is lacking. To address this gap, we conducted a meta-analysis of ML-based prediction modeling of debris flow occurrence by retrieving papers that were published between 2000 and 2023 from the Scopus and Web of Science databases. The general findings were as follows: (1) A total of 84 papers, distributed across 37 different journals in this time period, reflecting an overall upward trend. (2) Debris flow disasters occur throughout the world, and a total of 13 countries carried out research on the prediction of debris flow occurrence based on ML; China made significant contributions, but more research efforts in African countries should be considered. (3) A total of 36 categories of ML models were utilized as baseline predictors for debris flow occurrence, with logistic regression (LR) and random forest (RF) emerging as the most popular choices. (4) Feature engineering and model comparison were the most commonly utilized strategies in predicting debris flow occurrence based on ML (53 and 46 papers, respectively). (5) Interpretation methods were rarely utilized in predicting debris flow occurrence based on ML, with only 16 papers reporting their utilization. (6) In the prediction of debris flow occurrence based on ML, interpretation methods were rarely utilized, searching by data materials was the most important sample data source, the topographic factors were the most commonly utilized category of candidate variables, and the area under the ROC curve (AUROC) was the most frequently reported evaluation metric. (7) LR’s prediction performance for debris flow occurrence was inferior to that of RF, BPNN, and SVM; SVM was comparable to RF, and all superior to BPNN. (8) The application process for the prediction of debris flow occurrence based on ML consisted of three main steps: data preparation, model construction and evaluation, and prediction outcomes. The research gaps in predicting debris flow occurrence based on ML include utilizing new ML techniques and enhancing the interpretability of ML. Consequently, this study contributes both to academic ML research and to practical applications in the prediction of debris flow occurrence.

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Section: Categoriesmentioning

confidence: 99%

Section: Prediction Performance Improvement Strategiesmentioning

confidence: 99%

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Machine-Learning-Based Prediction Modeling for Debris Flow Occurrence: A Meta-Analysis

Yang,

Ge,

Chen

et al. 2024

Water

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“…China is a country prone to geological disasters, and various types of geological disasters have caused enormous losses to the lives and property security of its people (Derbyshire, 2001;Xu and Wang, 2022;Liu and Wang, 2024). Landslides, debris flows and collapses are major geological disasters characterized by strong concealment, significant hazards and high suddenness, and they are widely distributed in mountainous areas and canyons in China (Jiang et al, 2022;Guo et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Analysis of surface deformation prediction in high mountain canyon areas based on time-series InSAR technology and improved Elman neural network

Chen,

Xi,

Wang

2024

Preprint

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Abstract. To address the issues of over-reliance on deformation data and model singularity in existing surface deformation prediction methods in high mountain canyon areas, this study proposes the improvement of Elman neural network using cuckoo search algorithm and grey wolf optimization algorithm (CS-Elman and GWO-Elman) from the perspective of multi-temporal and multi-factor analysis. Firstly, surface deformation in the study area is monitored using SBAS-InSAR and PS-InSAR techniques. Then, the optimal evaluation factors are determined from 13 evaluation factors including digital elevation model (DEM) and slope using grey correlation analysis and correlation matrix analysis in SPSSAU software. These optimal factors, combined with surface deformation monitoring values obtained from InSAR technology, are used to construct CS-Elman and GWO-Elman prediction models from a multi-factor and multi-temporal perspective. Finally, the optimal prediction model is determined through comparative experiments and its prediction performance is validated. Results indicate: (1) SBAS-InSAR and PS-InSAR techniques exhibit a high correlation coefficient (R2=0.85) between monitored radar line of sight (LOS) deformation rates, demonstrating the feasibility of joint analysis of the two techniques. (2) The CS-Elman model has a smaller absolute error range compared to the GWO-Elman model. The optimal convergence iteration number, mean square error, mean absolute error (MAE) and mean absolute percentage error (MAPE) of the CS-Elman model are 3 iterations, 0.020 mm/a, 1.620 mm/a and 21.500 %, respectively, which are all superior to the GWO-Elman model. This indicates that the Elman network optimized by the CS algorithm exhibits better performance and higher accuracy in predicting surface deformation in high mountain canyon areas. (3) Comparative analysis with SVM, LSTM and PSO-BP models, as well as prediction of temporal deformation trends at deformation points, validate the advantages and effectiveness of the CS-Elman model in surface deformation prediction. This method can serve as an effective means for long-term deformation prediction in high mountain canyon areas.

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The segmentation of debris-flow fans based on local features and spatial attention mechanism

Song,

Wang

2024

J. Geogr. Sci.

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Debris flow susceptibility mapping in mountainous area based on multi-source data fusion and CNN model – taking Nujiang Prefecture, China as an example

Cited by 8 publications

References 46 publications

Machine-Learning-Based Prediction Modeling for Debris Flow Occurrence: A Meta-Analysis

Machine-Learning-Based Prediction Modeling for Debris Flow Occurrence: A Meta-Analysis

Analysis of surface deformation prediction in high mountain canyon areas based on time-series InSAR technology and improved Elman neural network

The segmentation of debris-flow fans based on local features and spatial attention mechanism

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