Debris flow susceptibility zonation using statistical models in parts of Northwest Indian Himalayas—implementation, validation, and comparative evaluation

Dash, Rajesh Kumar; Falae, Philips Omowumi; Kanungo, Debi Prasanna

doi:10.1007/s11069-021-05128-3

Cited by 23 publications

(8 citation statements)

References 84 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is the main basis for risk response and risk control. Due to the accuracy and applicability of different types of combined forecasting models, researchers have carried out many effective practical design and exploration studies and have also achieved many effective results in theoretical methods and practical applications [ 13 ]. Figure 1 shows the flowchart of the occurrence of international trade risks.…”

Section: Methodsmentioning

confidence: 99%

Prediction Model of International Trade Risk Based on Stochastic Time-Series Neural Network

Gui-cai

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

With the extreme deterioration of domestic and foreign trade environment, international competition is becoming increasingly fierce. At the same time, many enterprises have loopholes in industrial structure and finance, resulting in many risks in their international trade. Therefore, we must take corresponding measures to effectively manage and avoid risks and realize the healthy and sustainable development of foreign trade enterprises and enterprise economy. This paper designs and proposes a risk prediction model combining ARIMA and BP neural network. The model can get good prediction in different time series and effectively avoid the risk. The model proposed in this paper optimizes the structure of the design model with the support of ARIMA algorithm and BP neural network algorithm and has good accuracy and error control for different time series. The purpose of establishing time series prediction model is to improve the prediction accuracy of the model, and it is also an effective way to enhance the practicability of the prediction model. Applying intersequence analysis method to financial risk prediction can greatly improve efficiency and save cost and has broad application prospects.

show abstract

Section: Methodsmentioning

confidence: 99%

Prediction Model of International Trade Risk Based on Stochastic Time-Series Neural Network

Gui-cai

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…The slope unit above can better reflect the natural conditions of debris-flow development and has a small area, but numerous small or unreasonable polygons may be generated in the flat area after completing the slope unit division, thereby leading to errors in the susceptibility modeling process (Huang et al 2020b). The grid unit therein has a strong advantage in statistical calculations, that facilitates the data processing by the debris-flow susceptibility model (Dash et al 2022), although its ability to reflect the physical relationship between the hazard factor and the debris-flow is low, by comparing with the above two. Moreover, the grid unit can generate the more authentic and credible assessment results, and realize the smooth transition of the assessment results of the whole study area, so it is widely used in the assessment of geological hazard susceptibility Angillieri et al 2020).…”

Section: Debris-flow Susceptibility Assessment Unitmentioning

confidence: 99%

Debris-Flow Susceptibility assessment Using Stacking Ensemble Learning Including Multiple Heterogeneous Learners with RFE for factor optimization

Zhao

Lin

2023

Preprint

View full text Add to dashboard Cite

An accurate assessment of debris-flow susceptibility is of great importance to the prevention and control of debris-flow disasters in mountainous areas. In this study, by applying the recursive feature elimination-random forest (RFE-RF) and the stacking ensemble learning including multiple heterogeneous learners, debris-flow the high accuracy of the debris-flow susceptibility is assessed. As indicated by the results, the very low and low susceptibility zones of debris-flow are mainly concentrated in the eastern and western parts in the study area. The very high and high susceptibility zones are mainly distributed on the two banks of Xiaojiang River Valley and the south bank of Jinsha River, where there is fragile geological environment and high risk, in the study area. The medium susceptibility zone is mainly distributed around the very high and high susceptibility zones. There are excellent accuracy and stability in the stacking ensemble learning model of debris-flow susceptibility in the mountainous areas, when combining with the RFE-RF model and the diversity measurement. As for the stacking ensemble learning therein, the area under curve (AUC) value of the receiver-operating characteristic (ROC), the accuracy (ACC) value, and F1 score are the maximum, reaching 95.6%, 88.6%, and 88.9%, respectively. Besides, the root mean square error (RMSE) value is the minimum, namely 0.287, which indicates that stacking ensemble learning including multiple heterogeneous learners is a high-performance model for debris-flow susceptibility assessment. The findings can provide a scientific basis for the disaster prevention and mitigation in the mountainous areas.

show abstract

“…(5) Draw landslide susceptibility mapping, and analyze and discuss the performance of each model and the main control disaster factors. 13 Geofluids meaningless workload, which will often have an adverse impact on the evaluation accuracy [58]. There is no fixed reference for the selection of the number of influencing factors.…”

Section: Modeling Process (1)mentioning

confidence: 99%

“…The use of quantitative attribution analysis model accounted for the main research model. Qualitative attribution analysis is highly correlated with researchers with knowledge reserves and subjectivity, resulting in a large difference in their effects [7], such as analytic hierachy process(AHP) and Entropy weight method [8]; other statistical models and the coupling models are also widely applied, such as frequency ratio model [9][10][11], information value model [12,13], and evidence weight model [14][15][16]. However, the results of these models are not ideal.…”

Section: Introductionmentioning

confidence: 99%

Mapping Landslide Sensitivity Based on Machine Learning: A Case Study in Ankang City, Shaanxi Province, China

Zhao

Zhu

et al. 2022

Geofluids

View full text Add to dashboard Cite

The main purpose of this research is to apply the logistic regression (LR) model, the support vector machine (SVM) model based on radial basis function, the random forest (RF) model, and the coupled model of the whale optimization algorithm (WOA) and genetic algorithm (GA) with RF, to make landslide susceptibility mapping for the Ankang City of Shaanxi Province, China. To this end, a landslide inventory map consisting of 4278 identified landslides is randomly divided into training and test landslides in a ratio of 7 : 3. The 15 landslide influencing factors are selected as follows: slope aspect, slope degree, elevation, terrain curvature, plane curvature, profile curvature, surface roughness, distance to faults, distance to roads, landform, lithology, distance to rivers, rainfall, stream power index (SPI), and normalized difference vegetation index (NDVI), and the potential multicollinearity problem among these factors is detected by Pearson correlation coefficient (PCC), variance inflation factor (VIF), and tolerance (TOL). We evaluate the performance of the model separately by statistical training and test dataset metrics, including sensitivity, specificity, accuracy, kappa, mean absolute error (MSE), root mean square error (RMSE), and area under the receiver operating characteristic curve. The training success rates of LR, SVM, RF, WOA-RF, and GA-RF models are 0.7546, 0.8317, 0.8561, 0.8804, and 0.8957; the testing success rates are 0.7551, 0.8375, 0.8395, 0.8348, and 0.85007. The results show that the GA significantly improves the predictive power of the RF model. This study provides a scientific reference for disaster prevention and control in this area and its surrounding areas.

show abstract

Debris flow susceptibility zonation using statistical models in parts of Northwest Indian Himalayas—implementation, validation, and comparative evaluation

Cited by 23 publications

References 84 publications

Prediction Model of International Trade Risk Based on Stochastic Time-Series Neural Network

Prediction Model of International Trade Risk Based on Stochastic Time-Series Neural Network

Debris-Flow Susceptibility assessment Using Stacking Ensemble Learning Including Multiple Heterogeneous Learners with RFE for factor optimization

Mapping Landslide Sensitivity Based on Machine Learning: A Case Study in Ankang City, Shaanxi Province, China

Contact Info

Product

Resources

About