Citation: | Hao Zheng, Mingtao Ding, Tao Huang, Zemin Gao. Benefit Evaluation of Geotechnical Projects for Debris Flow Prevention and Control Based on Projection Pursuit in Wenchuan County, SW China. Journal of Earth Science, 2025, 36(2): 700-716. doi: 10.1007/s12583-022-1730-1 |
Benefit evaluation of debris flow prevention and control projects was one of the essential contents of debris flow prevention and mitigation work. In order to scientifically and quantitatively evaluate the comprehensive benefit of debris flow prevention and control projects, this study identified nine factors as evaluation indicators from economic, social, and ecological aspects. The projection pursuit (PP) model based on the improved particle swarm optimization (IPSO) algorithm was used to construct a mathematical model to evaluate the benefit of debris flow prevention and control projects. The interpolation method was applied to divide the benefit grades. The debris flow prevention and control projects in Qipan, Taoguan, Chutou, Anjia, and Mozi gullies in Wenchuan County were chosen as typical cases for empirical analysis. The case study revealed that, among the criteria layer indicators, investment per unit of the protected area, investment per unit of the protected population, the amount of water and soil conservation, and reduction rate of accumulation fan had the most significant weights. The social and ecological benefits were found to be the more important in the target layer. The comprehensive benefit of Qipan, Taoguan, Chutou, Anjia, and Mozi gullies was found to be 4.44, 4.83, 1.95, 3, and 2, respectively. The benefit ranking of the five gullies was consistent with their effectiveness in disaster prevention ranking in the flood season of 2019. Therefore, it could prove that the newly-built benefit evaluation model was practical and feasible, and the evaluation results of the sample could be reasonably interpreted, which verified the effectiveness of the methods.
Cheon, E., Lee, S. R., Lee, D. H., 2020. Hazard Assessment Based on the Combination of DAN3D and Machine Learning Method for Planning Closed-Type Barriers Against Debris-Flow. Water, 12(1): 170. https://doi.org/10.3390/w12010170 |
Villacorta, S. P., Evans, K. G., Nakatani, K., et al., 2020. Large Debris Flows in Chosica, Lima, Peru: The Application of Hydraulic Infrastructure for Erosion Control and Disaster Prevention. Australian Journal of Earth Sciences, 67(3): 425–436. https://doi.org/10.1080/08120099.2020.1690577 |
Agrawal, R., Gehrke, J., Gunopulos, D., et al., 2005. Automatic Subspace Clustering of High Dimensional Data. Data Mining and Knowledge Discovery, 11(1): 5–33. https://doi.org/10.1007/s10618-005-1396-1 |
Bachmann, C. M., Musman, S. A., Luong, D., et al., 1994. Unsupervised BCM Projection Pursuit Algorithms for Classification of Simulated Radar Presentations. Neural Networks, 7(4): 709–728. https://doi.org/10.1016/0893-6080(94)90047-7 |
Banihabib, M. E., Forghani, A., 2017. An Assessment Framework for the Mitigation Effects of Check Dams on Debris Flow. CATENA, 152: 277–284. https://doi.org/10.1016/j.catena.2017.01.018 |
Barbieri da Silva Cruz, E., Baqueta, M. R., Neto, R. M., et al., 2020. Kurtosis-Based Projection Pursuit Analysis to Extract Information from Sensory Attributes of Cachaça. Chemometrics and Intelligent Laboratory Systems, 203: 104075. https://doi.org/10.1016/j.chemolab.2020.104075 |
Bendaoud, R., Amiry, H., Benhmida, M., et al., 2019. New Method for Extracting Physical Parameters of PV Generators Combining an Implemented Genetic Algorithm and the Simulated Annealing Algorithm. Solar Energy, 194: 239–247. https://doi.org/10.1016/j.solener.2019.10.040 |
Calvo, B., Savi, F., 2009. A Real-World Application of Monte Carlo Procedure for Debris Flow Risk Assessment. Computers & Geosciences, 35(5): 967–977. https://doi.org/10.1016/j.cageo.2008.04.002 |
Chen, G., Xu, X., Wang, J., et al., 2010. Classification of Surrounding Rock Stabilities Based on Projection Pursuit Interpolation Model. Rock and Soil Mechanics, 31: 1897–1901 (in Chinese with English Abstract) doi: 10.3969/j.issn.1000-7598.2010.06.037 |
Chen, H. X., Zhang, S., Peng, M., et al., 2016. A Physically-Based Multi-Hazard Risk Assessment Platform for Regional Rainfall-Induced Slope Failures and Debris Flows. Engineering Geology, 203: 15–29. https://doi.org/10.1016/j.enggeo.2015.12.009 |
Chen, M. L., Hu, G. S., Chen, N. S., et al., 2016. Valuation of Debris Flow Mitigation Measures in Tourist Towns: A Case Study on Hongchun Gully in Southwest China. Journal of Mountain Science, 13(10): 1867–1879. https://doi.org/10.1007/s11629-015-3759-4 |
Chen, N. S., Zhou, H. B., Lu, Y., et al., 2013. Analysis of Benefits of Debris Flow Control Projects in Southwest Mountain Areas of China. Journal of Chengdu University of Technology (Science & Technology Edition), 40: 50–58 (in Chinese with English Abstract) doi: 10.3969/j.issn.1671-9727.2013.01.008 |
Chen, X., Cui, P., You, Y., et al., 2013. Layout Methods of Control Works Preventing Large Scale Debris Flows in Wenchuan Earthquake Area. Journal of Hydraulic Engineering, 44: 586–593 doi: 10.3969/j.issn.0559-9350.2013.05.014 |
Chiou, I. J., Chen, C. H., Liu, W. L., et al., 2015. Methodology of Disaster Risk Assessment for Debris Flows in a River Basin. Stochastic Environmental Research and Risk Assessment, 29(3): 775–792. https://doi.org/10.1007/s00477-014-0932-1 |
Ciurean, R. L., Hussin, H., van Westen, C. J., et al., 2017. Multi-Scale Debris Flow Vulnerability Assessment and Direct Loss Estimation of Buildings in the Eastern Italian Alps. Natural Hazards, 85(2): 929–957. https://doi.org/10.1007/s11069-016-2612-6 |
Cong, K., Li, R., Bi, Y., 2019. Benefit Evaluation of Debris Flow Control Engineering Based on the FLO-2D Model. Northwestern Geology, 52: 209–216 (in Chinese with English Abstract) |
Ding, M. T., Heiser, M., Hübl, J., et al., 2016. Regional Vulnerability Assessment for Debris Flows in China—A CWS Approach. Landslides, 13(3): 537–550. https://doi.org/10.1007/s10346-015-0578-1 |
Ding, M. T., Tang, C., Huang, T., et al., 2020b. Dynamic Vulnerability Analysis of Mountain Settlements Exposed to Geological Hazards: A Case Study of the Upper Min River, China. Advances in Civil Engineering, 2020(1): 1–13. https://doi.org/10.1155/2020/8887487 |
Ding, M. T., Tang, C., Miao, C., 2020a. Response Analysis of Valley Settlements to the Evolution of Debris Flow Fans under Different Topographic Conditions: A Case Study of the Upper Reaches of Min River, China. Bulletin of Engineering Geology and the Environment, 79(3): 1639–1650. https://doi.org/10.1007/s10064-019-01641-9 |
Ding, M. T., Huang, T., Zheng, H., et al., 2020c. Respective Influence of Vertical Mountain Differentiation on Debris Flow Occurrence in the Upper Min River, China. Scientific Reports, 10(1): 11689. https://doi.org/10.1038/s41598-020-68590-2 |
Dong, J., Li, Y. J., Wang, M., 2019. Fast Multi-Objective Antenna Optimization Based on RBF Neural Network Surrogate Model Optimized by Improved PSO Algorithm. Applied Sciences, 9(13): 2589. https://doi.org/10.3390/app9132589 |
Dormishi, A. R., Ataei, M., Kakaie, R. K., 2019. Performance Evaluation of Gang Saw Using Hybrid ANFIS-DE and Hybrid ANFIS-PSO Algorithms. Journal of Mining and Environment, 10: 543–557 |
Friedman, J. H., Tukey, J. W., 1974. A Projection Pursuit Algorithm for Exploratory Data Analysis. IEEE Transactions on Computers, C-23(9): 881–890. https://doi.org/10.1109/t-c.1974.224051 |
Gong, X. L., Chen, K. T., Chen, X. Q., et al., 2020. Characteristics of a Debris Flow Disaster and Its Mitigation Countermeasures in Zechawa Gully, Jiuzhaigou Valley, China. Water, 12(5): 1256. https://doi.org/10.3390/w12051256 |
Hall, P., Li, K. C., 1993. On almost Linearity of Low Dimensional Projections from High Dimensional Data. The Annals of Statistics, 21(2): 867–889. https://doi.org/10.1214/aos/1176349155 |
Han, M., Hu, X., Liang, J., 2015. Risk Evaluation of Debris Flow along Duwen Highway Using Optimal Combination of Empowerment. Mountain Research, 33: 597–602 (in Chinese with English Abstract) |
Han, Y. S., Dong, S. K., Chen, Z. C., et al., 2014. Assessment of Secondary Mountain Hazards along a Section of the Dujiangyan-Wenchuan Highway. Journal of Mountain Science, 11(1): 51–65. https://doi.org/10.1007/s11629-012-2516-1 |
Horiguchi, T., Komatsu, Y., 2019. Method to Evaluate the Effect of Inclination Angle of Steel Open-Type Check Dam on Debris Flow Impact Load. International Journal of Protective Structures, 10(1): 95–115. https://doi.org/10.1177/2041419618789702 |
Huang, H. H., Zhang, T., 2020. Robust Discriminant Analysis Using Multi-Directional Projection Pursuit. Pattern Recognition Letters, 138: 651–656. https://doi.org/10.1016/j.patrec.2020.09.013 |
Kennedy, J., Eberhart, R., 1995. Particle Swarm Optimization. International Conference on Neural Networks Proceedings, Vols 1–6, New York. |
Kennedy, J., Eberhart, R. C., 1997. A Discrete Binary Version of the Particle Swarm Algorithm. In: Smc '97 Conference Proceedings -1997 Ieee International Conference on Systems, Man, and Cybernetics, Vols 1–5: Conference Theme: Computational Cybernetics and Simulation. Ieee International Conference on Systems, Man, and Cybernatics, Conference Proceedings. IEEE, New York, |
Kim, M. I., Kwak, J. H., 2020. Assessment of Building Vulnerability with Varying Distances from Outlet Considering Impact Force of Debris Flow and Building Resistance. Water, 12(7): 2021. https://doi.org/10.3390/w12072021 |
Kukker, A., Sharma, R., 2020. Genetic Algorithm-Optimized Fuzzy Lyapunov Reinforcement Learning for Nonlinear Systems. Arabian Journal for Science and Engineering, 45(3): 1629–1638. https://doi.org/10.1007/s13369-019-04126-9 |
Lan, Z. G., Huang, M., 2018. Safety Assessment for Seawall Based on Constrained Maximum Entropy Projection Pursuit Model. Natural Hazards, 91(3): 1165–1178. https://doi.org/10.1007/s11069-018-3172-8 |
Li, R. H., Tang, X. C., 1995. Study the Model for Evaluating Social Benefit of Walley Debris Flow Control. Journal of Soil Erosion and Soil and Water Conservation, 9: 33–37 (in Chinese with English Abstract) |
Liu, C. K., Yin, Y., 2018. Particle Swarm Optimised Analysis of Investment Decision. Cognitive Systems Research, 52: 685–690. https://doi.org/10.1016/j.cogsys.2018.07.032 |
Liu, D., Liu, C. L., Fu, Q., et al., 2018. Projection Pursuit Evaluation Model of Regional Surface Water Environment Based on Improved Chicken Swarm Optimization Algorithm. Water Resources Management, 32(4): 1325–1342. https://doi.org/10.1007/s11269-017-1872-6 |
Liu, D., Zhang, G. D., Li, H., et al., 2019. Projection Pursuit Evaluation Model of a Regional Surface Water Environment Based on an Ameliorative Moth-Flame Optimization Algorithm. Ecological Indicators, 107: 105674. https://doi.org/10.1016/j.ecolind.2019.105674 |
Liu, W., Yan, S. X., He, S. M., 2020. A Simple Method to Evaluate the Performance of an Intercept Dam for Debris-Flow Mitigation. Engineering Geology, 276: 105771. https://doi.org/10.1016/j.enggeo.2020.105771 |
Lu, Y. H., Liang, M. H., Ye, Z. Y., et al., 2015. Improved Particle Swarm Optimization Algorithm and Its Application in Text Feature Selection. Applied Soft Computing, 35: 629–636. https://doi.org/10.1016/j.asoc.2015.07.005 |
Mikaeil, R., Shaffiee Haghshenas, S., Sedaghati, Z., 2019. Geotechnical Risk Evaluation of Tunneling Projects Using Optimization Techniques (Case Study: The Second Part of Emamzade Hashem Tunnel). Natural Hazards, 97(3): 1099–1113. https://doi.org/10.1007/s11069-019-03688-z |
Ni, H. Y., Tang, C., Zheng, W. M., et al., 2014. An Overview of Formation Mechanism and Disaster Characteristics of Post-Seismic Debris Flows Triggered by Subsequent Rainstorms in Wenchuan Earthquake Extremely Stricken Areas. Acta Geologica Sinica - English Edition, 88(4): 1310–1328. https://doi.org/10.1111/1755-6724.12290 |
Ni, H. Y., Zheng, W. M., Song, Z., et al., 2014. Catastrophic Debris Flows Triggered by a 4 July 2013 Rainfall in Shimian, SW China: Formation Mechanism, Disaster Characteristics and the Lessons Learned. Landslides, 11(5): 909–921. https://doi.org/10.1007/s10346-014-0514-9 |
Ouyang, C. J., Wang, Z. W., An, H. C., et al., 2019. An Example of a Hazard and Risk Assessment for Debris Flows—A Case Study of Niwan Gully, Wudu, China. Engineering Geology, 263: 105351. https://doi.org/10.1016/j.enggeo.2019.105351 |
Pal, D., Galelli, S., Tang, H. L., et al., 2018. Toward Improved Design of Check Dam Systems: A Case Study in the Loess Plateau, China. Journal of Hydrology, 559: 762–773. https://doi.org/10.1016/j.jhydrol.2018.02.051 |
Pei, W., Fu, Q., Liu, D., et al., 2016. Assessing Agricultural Drought Vulnerability in the Sanjiang Plain Based on an Improved Projection Pursuit Model. Natural Hazards, 82(1): 683–701. https://doi.org/10.1007/s11069-016-2213-4 |
Qiao, J., Huang, D., Wang, M., et al., 2017. The Optimal Value Model for the Benefit from Mitigation Measures Reducing Landslide and Debris Flow. The Chinese Journal of Geological Hazard and Control, 28: 80–86 (in Chinese with English Abstract) |
Shi, M. Y., Chen, J. P., Sun, D. Y., et al., 2015. Hazard Assessment of Debris Flows Based on the Catastrophe Progression Method: A Case Study from the Wudongde Dam Site. International Journal of Heat and Technology, 33(4): 217–220. https://doi.org/10.18280/ijht.330429 |
Sun, H., You, Y., Liu, J. F., 2018. Experimental Study on Blocking and Self-Cleaning Behaviors of Beam Dam in Debris Flow Hazard Mitigation. International Journal of Sediment Research, 33(4): 395–405. https://doi.org/10.1016/j.ijsrc.2018.04.004 |
Tang, C., Zhu, J., Li, W. L., et al., 2009. Rainfall-Triggered Debris Flows Following the Wenchuan Earthquake. Bulletin of Engineering Geology and the Environment, 68(2): 187–194. https://doi.org/10.1007/s10064-009-0201-6 |
Tang, X. C., Liu, H. P., Sun, D. H., et al., 2000. A Study on the Ecologic Benefit Evaluation Model of Valley Debris Flow Controlling. The Chinese Journal of Geological Hazard and Control, 11: 89–91 (in Chinese with English Abstract) |
Tang, X. C., Wang, W. D., 1994. Preliminary Study on Social Benefit Assessment and Its SystemTargets of Debris Flow Control. Journal of Soil Erosion and Soil and Water Conservation, 8: 64–68 (in Chinese with English Abstract) |
Tian, S., Zhang, J., Zhang, S., 2020. Effectiveness Evaluation of Disaster Reduction for Debris Flows Control Engineering after Wenchuan Earthquake. Journal of Catastrophology, 35: 102–109 (in Chinese with English Abstract) |
Villacorta, S. P., Evans, K. G., Nakatani, K., et al., 2020. Large Debris Flows in Chosica, Lima, Peru: the Application of Hydraulic Infrastructure for Erosion Control and Disaster Prevention. Australian Journal of Earth Sciences, 67: 425–436. https://doi.org/10.1080/08120099.2020.1690577 |
Wang, X., 2000. A Systematic Appraisal of the Benifits of the Preventive Measures Against Mud-rock Flow. Journal of Chongqing Normal University (Natural Science Edition) 17: 67–70 (in Chinese with English Abstract) |
Wang, L., Zhao, Q. J., Wen, Z. M., et al., 2018. RAFFIA: Short-Term Forest Fire Danger Rating Prediction via Multiclass Logistic Regression. Sustainability, 10(12): 4620. https://doi.org/10.3390/su10124620 |
Wang, N., Han, B., Pang, Q., et al., 2015. Post-Evaluation Model on Effectiveness of Debris Flow Control. Journal of Engineering Geology, 23: 219–226 (in Chinese with English Abstract) |
Wang, W., Tang, X., Xie, S., et al., 1996. Study on Evaluation Factors Of Economic Benefit in Debris Flow Control. The Chinese Journal of Geological Hazard and Control, 7: 83–86 (in Chinese with English Abstract) |
Wang, W., Xu, W. L., Liu, S. J., 2001. Prevention of Debris Flow Disasters on Chengdu-Kunming Railway. J. Environ. Sci. (China), 13(3): 333–336 |
Wu, Q. H., Song, T., Liu, H. M., et al., 2017. Particle Swarm Optimization Algorithm Based on Parameter Improvements. Journal of Computational Methods in Sciences and Engineering, 17(3): 557–568. https://doi.org/10.3233/jcm-170742 |
Yu, S., Lu, H. W., 2018. An Integrated Model of Water Resources Optimization Allocation Based on Projection Pursuit Model – Grey Wolf Optimization Method in a Transboundary River Basin. Journal of Hydrology, 559: 156–165. https://doi.org/10.1016/j.jhydrol.2018.02.033 |
Yu, X. B., Chen, X. Q., Wang, H. L., et al., 2020. Numerical Study on the Interaction between Debris Flow Slurry and Check Dams Based on Fluid-Solid Coupling Theory. Geotechnical and Geological Engineering, 38(3): 2427–2445. https://doi.org/10.1007/s10706-019-01160-0 |
Yuan, D., Liu, J. F., You, Y., et al., 2019. Experimental Study on the Performance Characteristics of Viscous Debris Flows with a Grid-Type Dam for Debris Flow Hazards Mitigation. Bulletin of Engineering Geology and the Environment, 78(8): 5763–5774. https://doi.org/10.1007/s10064-019-01524-z |
Zhang, W. J., Wei, F., Zhou, R., 2018. Risk Assessment of Cotton Textile Enterprise Working Environment Based on Projection Pursuit Model. China Safety Science Journal, 28: 103–108 (in Chinese with English Abstract) |
Zhang, Z. L., Xie, J., Yu, D. K., et al., 2019. Analysis of a Debris Flow after Wenchuan Earthquake and Discussion on Preventive Measures. Thermal Science, 23(3 Part A): 1563–1570. https://doi.org/10.2298/tsci180811224z |
Zhao, W. Y., You, Y., Chen, X. Q., et al., 2020. Case Study on Debris-Flow Hazard Mitigation at a World Natural Heritage Site, Jiuzhaigou Valley, Western China. Geomatics, Natural Hazards and Risk, 11(1): 1782–1804. https://doi.org/10.1080/19475705.2020.1810784 |
Zhi, G. Z., Liao, Z. L., Tian, W. C., et al., 2020. Urban Flood Risk Assessment and Analysis with a 3D Visualization Method Coupling the PP-PSO Algorithm and Building Data. Journal of Environmental Management, 268: 110521. https://doi.org/10.1016/j.jenvman.2020.110521 |
Zhu, Z. X., Tang, B. W., Yuan, J. P., 2017. Multirobot Task Allocation Based on an Improved Particle Swarm Optimization Approach. International Journal of Advanced Robotic Systems, 14(3): 172988141771031. https://doi.org/10.1177/1729881417710312 |