Optimization of Random Forest Model for Assessing and Predicting Geological Hazards Susceptibility in Lingyun County

The random forest (RF) model is improved by the optimization of unbalanced geological hazards dataset, differentiation of continuous geological hazards evaluation factors, sample similarity calculation, and iterative method for finding optimal random characteristics by calculating out-of-bagger errors. The geological hazards susceptibility evaluation model based on optimized RF (OPRF) was established and used to assess the susceptibility for Lingyun County. Then, ROC curve and field investigation were performed to verify the efficiency for different geological hazards susceptibility assessment models. The AUC values for five models were estimated as 0.766, 0.814, 0.842, 0.846 and 0.934, respectively, which indicated that the prediction accuracy of the OPRF model can be as high as 93.4%. This result demonstrated that the geological hazards susceptibility assessment model based on OPRF has the highest prediction accuracy. Furthermore, the OPRF model could be extended to other regions with similar geological environment backgrounds for geological hazards susceptibility assessment and prediction.

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Optimization of random forest model for assessing and predicting geological hazards susceptibility in Lingyun County

10.21203/rs.3.rs-231323/v1 ◽

2021 ◽

Author(s):

Chunfang Kong ◽

Junzuo Wang ◽

Xiaogang Ma ◽

Yiping Tian ◽

Zhiting Zhang ◽

...

Keyword(s):

Land Use ◽

Random Forest ◽

Vegetation Index ◽

Evaluation Model ◽

Field Investigation ◽

Slope Aspect ◽

Geological Hazards ◽

Operating Characteristics ◽

Model Based ◽

Prediction And Prevention

Abstract The frequent occurrence of geological hazards will not only cause peoples' property loss and deterioration of living environments, but will also endanger peoples' lives. Therefore, rapid and accurate evaluation of geological hazards susceptibility can provide an important scientific basis for emergency rescue and disaster reduction and prevention. In this paper, ten effective variables including slope, aspect, curvature, normalized differential vegetation index, annual precipitation, strata lithology, tectonic complexity, residential density, road network density, and land use/land cover were selected as evaluation indexes. Meanwhile, random forest (RF) model is improved by the optimization of unbalanced geological hazards dataset, differentiation of continuous geological hazards evaluation factors, sample similarity calculation, and iterative method for finding optimal random characteristics by calculating out-of-bagger errors. The geological hazards susceptibility evaluation model based on optimized RF (OPRF) was established and used to assess the susceptibility level of geological hazards for Lingyun County. Then, receiver operating characteristics (ROC) curves and field investigation were performed to verify the efficiency for five models. Analysis and comparison of the results denoted that the model based on OPRF has the highest prediction accuracy of 93.4%, which is far better than the other four models. Furthermore, the evaluation results can provide reference for geological hazards prediction and prevention, and can also provide decision support for land use development and rational utilization of resources and environment in Lingyun County. Based on these results, the OPRF model could be extended to other regions with similar geological environment backgrounds for geological hazards susceptibility assessment and prediction.

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Prediction of Road Congestion Level Based on Bayes Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.599-601.1593 ◽

2014 ◽

Vol 599-601 ◽

pp. 1593-1596

Author(s):

Shou Bai Xiao

Keyword(s):

Traffic Congestion ◽

Road Traffic ◽

Evaluation Model ◽

The State ◽

Assessment Model ◽

Bayesian Algorithm ◽

Processing Efficiency ◽

Model Based ◽

Bayes Algorithm ◽

Road Traffic Congestion

Traffic jams increasingly threaten the normal city traffic, so our paper analyzes the state of the existing road traffic congestion, road traffic congestion found in the state is a relatively vague and random dynamic data model. Based on these two characteristics, we propose a road traffic congestion degree assessment model based on Bayesian algorithm. Based on the theoretical analysis of Bayesian algorithms to improve the processing efficiency of the algorithm to construct the road traffic congestion degree evaluation model based on Bayesian algorithm set, and the simulation experiments.

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Study on Evaluation Model of Emergency Rescue Capability of Chemical Accidents Based on PCA-BP

Computational Intelligence and Neuroscience ◽

10.1155/2021/8869608 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Jianghong Liu ◽

Junfeng Wu ◽

Weisi Liu

Keyword(s):

Neural Network ◽

Emergency Management ◽

Bp Neural Network ◽

Index System ◽

Evaluation Model ◽

Principal Component ◽

Assessment Model ◽

Chemical Accidents ◽

Emergency Rescue ◽

Model Based

The emergency management of chemical accidents plays an important role in preventing the expansion of chemical accidents. In recent years, the evaluation and research of emergency management of chemical accidents has attracted the attention of many scholars. However, as an important part of emergency management, the professional rescue team of chemicals has few evaluation models for their capabilities. In this study, an emergency rescue capability assessment model based on the PCA-BP neural network is proposed. Firstly, the construction status of 11 emergency rescue teams for chemical accidents in Shanghai is analyzed, and an index system for evaluating the capabilities of emergency rescue teams for chemicals is established. Secondly, the principal component analysis (PCA) is used to perform dimension reduction and indicators’ weight acquisition on the original index system to achieve an effective evaluation of the capabilities of 11 rescue teams. Finally, the indicators after dimensionality reduction are used as the input neurons of the backpropagation (BP) neural network, the characteristic data of eight rescue teams are used as the training set, and the comprehensive scores of three rescue teams are used for verifying the generalization ability of the evaluation model. The result shows that the proposed evaluation model based on the PCA-BP neural network can effectively evaluate the rescue capability of the emergency rescue teams for chemical accidents and provide a new idea for emergency rescue capability assessment.

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Flood hazard risk assessment model based on random forest

Journal of Hydrology ◽

10.1016/j.jhydrol.2015.06.008 ◽

2015 ◽

Vol 527 ◽

pp. 1130-1141 ◽

Cited By ~ 188

Author(s):

Zhaoli Wang ◽

Chengguang Lai ◽

Xiaohong Chen ◽

Bing Yang ◽

Shiwei Zhao ◽

...

Keyword(s):

Risk Assessment ◽

Random Forest ◽

Flood Hazard ◽

Assessment Model ◽

Risk Assessment Model ◽

Model Based ◽

Hazard Risk ◽

Hazard Risk Assessment

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Rainfall-induced landslide susceptibility assessment using random forest weight at basin scale

Hydrology Research ◽

10.2166/nh.2017.044 ◽

2017 ◽

Vol 49 (5) ◽

pp. 1363-1378 ◽

Cited By ~ 4

Author(s):

Chengguang Lai ◽

Xiaohong Chen ◽

Zhaoli Wang ◽

Chong-Yu Xu ◽

Bing Yang

Keyword(s):

Random Forest ◽

Landslide Susceptibility ◽

Slope Angle ◽

Fuzzy Comprehensive Evaluation ◽

Assessment Model ◽

Assessment Procedure ◽

Susceptibility Assessment ◽

Entropy Weight ◽

Landslide Susceptibility Assessment ◽

Basin Scale

Abstract Rainfall-induced landslide susceptibility assessment is currently considered an effective tool for landslide hazard assessment as well as for appropriate warning and forecasting. As part of the assessment procedure, a credible index weight matrix can strongly increase the rationality of the assessment result. This study proposed a novel weight-determining method by using random forests (RFs) to find a suitable weight. Random forest weights (RFWs) and eight indexes were used to construct an assessment model of the Dongjiang River basin based on fuzzy comprehensive evaluation. The results show that RF identified the elevation (EL) and slope angle (SL) as the two most important indexes, and soil erodibility factor (SEF) and shear resistance capacity (SRC) as the two least important indexes. The assessment accuracy of RFW can be as high as 79.71%, which is higher than the entropy weight (EW) of 63.77%. Two experiments were conducted by respectively removing the most dominant and the weakest indexes to examine the rationality and feasibility of RFW; both precision validation and contrastive analysis indicated the assessment results of RFW to be reasonable and satisfactory. The initial application of RF for weight determination shows significant potential and the use of RFW is therefore recommended.

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GIS-Based Random Forest Weight for Rainfall-Induced Landslide Susceptibility Assessment at a Humid Region in Southern China

Water ◽

10.3390/w10081019 ◽

2018 ◽

Vol 10 (8) ◽

pp. 1019 ◽

Cited By ~ 5

Author(s):

Peng Wang ◽

Xiaoyan Bai ◽

Xiaoqing Wu ◽

Haijun Yu ◽

Yanru Hao ◽

...

Keyword(s):

Random Forest ◽

Landslide Susceptibility ◽

Vegetation Index ◽

Southern China ◽

Error Rates ◽

Assessment Model ◽

Average Error ◽

Assessment Procedure ◽

Susceptibility Assessment ◽

Landslide Susceptibility Assessment

Landslide susceptibility assessment is presently considered an effective tool for landslide warning and forecasting. Under the assessment procedure, a credible index weight can greatly increase the rationality of the assessment result. Using the Beijiang River Basin, China, as a case study, this paper proposes a new weight-determining method based on random forest (RF) and used the weighted linear combination (WLC) to evaluate the landslide susceptibility. The RF weight and eight indices were used to construct the assessment model. As a comparison, the entropy weight (EW) and weight determined by analytic hierarchy process (AHP) were also used, respectively, to demonstrate the rationality of the proposed weight-determining method. The results show that: (1) the average error rates of training and testing based on RF are 18.12% and 15.83%, respectively, suggesting that the RF model can be considered rational and credible; (2) RF ranks the indices elevation (EL), slope (SL), maximum one-day precipitation (M1DP) and distance to fault (DF) as the Top 4 most important of the eight indices, occupying 73.24% of the total, while the indices runoff coefficient (RC), normalized difference vegetation index (NDVI), shear resistance capacity (SRC) and available water capacity (AWC) are less consequential, with an index importance degree of only 26.76% of the total; and (3) the verification of landslide susceptibility indicates that the accuracy rate based on the RF weight reaches 75.41% but are only 59.02% and 72.13% for the other two weights (EW and AHP), respectively. This paper shows the potential to provide a new weight-determining method for landslide susceptibility assessment. Evaluation results are expected to provide a reference for landslide management, prevention and reduction in the studied basin.

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Eutrophication Evaluation of Lake Based on Cloud Model

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.955-959.1653 ◽

2014 ◽

Vol 955-959 ◽

pp. 1653-1656 ◽

Cited By ~ 1

Author(s):

Fan Xiu Li ◽

Mian Lu ◽

Shao Jin Yi

Keyword(s):

Cloud Model ◽

Evaluation Model ◽

Quantitative Model ◽

Assessment Model ◽

Lake Eutrophication ◽

New Model ◽

Model Based ◽

Cloud Theory ◽

Stochastic Feature

Considering the fuzzy and stochastic feature in assessment, a new assessment model based on cloud theory is proposed. The new model can realize greatly the conversion from qualitative conception to quantitative model. Taking the new model into evaluating water quality of lake eutrophication, the evaluation model based on cloud theory can not only show the fuzzy feature of grade, but also indicate random attribute of membership. The method is developed to evaluate lake eutrophication status, and the results show that the new evaluation model is feasible and can effectively evaluate eutrophication level.

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