Intelligent Classification of Stable and Unstable Slope Conditions Based on Landslide Movement

Document Type : Regular Paper

Authors

1 Geofirst Pty Ltd., 2/7 Luso Drive, Unanderra, NSW 2526, Australia

2 Assistant Professor, Department of Engineering, Payame Noor University, Tehran, Iran

3 Department of Civil Engineering, Technical and Vocational University (TVU), Tehran, Iran

4 Department of Urban Planning, Engineering Networks and Systems, Institute of Architecture and Construction, South Ural State University, 76, Lenin Prospect, 454080 Chelyabinsk, Russia

5 Saint Petersburg State University of Architecture and Civil Engineering, 4, 2nd Krasnoarmeiskaya Str., St Petersburg 190005, Russian Federation

Abstract

One of the most critical problems in the study of geohazards is the displacement brought on by landslides. This research aims to investigate stable and unstable conditions for this important issue using new techniques. There are several effective parameters on landslide movement that need to be thoroughly investigated/observed, making the process of determining the movement of landslides a difficult one. In this research, different machine learning-based approaches were used to analyze and manage this problem. A set of data was compiled for this investigation including groundwater level, prior rainfall, infiltration coefficient, shear strength, and monitored slope gradient are all influential in landslide movement. Three models of Tree, Adaboost and artificial neural network (ANN) were developed for classification into two categories, stable and unstable. The results showed well that two Adaboost and Tree models can provide significant performance for determining stable and unstable conditions. For the test data, the Adaboost model with an accuracy of 0.857 has the highest accuracy, followed by the Tree model with an accuracy of 0.786. Finally, in this research, unstable data using machine learning was used to evaluate and predict the amount of slope movement. This system is well suited for its high flexibility and high-accuracy assessment for conditions with more movement.

Keywords

Main Subjects

References

[1]     Helmstetter A, Sornette D, Grasso J, Andersen JV, Gluzman S, Pisarenko V. Slider block friction model for landslides: Application to Vaiont and La Clapiere landslides. J Geophys Res Solid Earth 2004;109.
[2]     Corominas J, Moya J, Ledesma A, Lloret A, Gili JA. Prediction of ground displacements and velocities from groundwater level changes at the Vallcebre landslide (Eastern Pyrenees, Spain). Landslides 2005;2:83–96.
[3]     Gao W, Feng X. Study on displacement predication of landslide based on grey system and evolutionary neural network. ROCK SOIL Mech 2004;25:514–7.
[4]     Sornette D, Helmstetter A, Andersen JV, Gluzman S, Grasso J-R, Pisarenko V. Towards landslide predictions: two case studies. Phys A Stat Mech Its Appl 2004;338:605–32.
[5]     Crosta GB, Agliardi F. How to obtain alert velocity thresholds for large rockslides. Phys Chem Earth, Parts A/B/C 2002;27:1557–65.
[6]     Lu P, Rosenbaum MS. Artificial neural networks and grey systems for the prediction of slope stability. Nat Hazards 2003;30:383–98.
[7]     Jibson RW. Regression models for estimating coseismic landslide displacement. Eng Geol 2007;91:209–18.
[8]     Mufundirwa A, Fujii Y, Kodama J. A new practical method for prediction of geomechanical failure-time. Int J Rock Mech Min Sci 2010;47:1079–90.
[9]     Feng X-T, Zhao H, Li S. Modeling non-linear displacement time series of geo-materials using evolutionary support vector machines. Int J Rock Mech Min Sci 2004;41:1087–107.
[10]   Li X, Kong J, Wang Z. Landslide displacement prediction based on combining method with optimal weight. Nat Hazards 2012;61:635–46.
[11]   Mohamad ET, Armaghani DJ, Momeni E, Yazdavar AH, Ebrahimi M. Rock strength estimation: a PSO-based BP approach. Neural Comput Appl 2016:1–12. https://doi.org/10.1007/s00521-016-2728-3.
[12]   Fakharian P, Rezazadeh Eidgahee D, Akbari M, Jahangir H, Ali Taeb A. Compressive strength prediction of hollow concrete masonry blocks using artificial intelligence algorithms. Structures 2023;47:1790–802. https://doi.org/10.1016/j.istruc.2022.12.007.
[13]   Rezazadeh Eidgahee D, Jahangir H, Solatifar N, Fakharian P, Rezaeemanesh M. Data-driven estimation models of asphalt mixtures dynamic modulus using ANN, GP and combinatorial GMDH approaches. Neural Comput Appl 2022;34:17289–314. https://doi.org/10.1007/s00521-022-07382-3.
[14]   Skentou AD, Bardhan A, Mamou A, Lemonis ME, Kumar G, Samui P, et al. Closed-Form Equation for Estimating Unconfined Compressive Strength of Granite from Three Non-destructive Tests Using Soft Computing Models. Rock Mech Rock Eng 2023;56:487–514.
[15]   Ghanizadeh AR, Ghanizadeh A, Asteris PG, Fakharian P, Armaghani DJ. Developing bearing capacity model for geogrid-reinforced stone columns improved soft clay utilizing MARS-EBS hybrid method. Transp Geotech 2023;38:100906. https://doi.org/10.1016/j.trgeo.2022.100906.
[16]   He B, Armaghani DJ, Lai SH. Assessment of tunnel blasting-induced overbreak: A novel metaheuristic-based random forest approach. Tunn Undergr Sp Technol 2023;133:104979. https://doi.org/10.1016/j.tust.2022.104979.
[17]   Cavaleri L, Barkhordari MS, Repapis CC, Armaghani DJ, Ulrikh DV, Asteris PG. Convolution-based ensemble learning algorithms to estimate the bond strength of the corroded reinforced concrete. Constr Build Mater 2022;359:129504.
[18]   Ghanizadeh AR, Delaram A, Fakharian P, Armaghani DJ. Developing Predictive Models of Collapse Settlement and Coefficient of Stress Release of Sandy-Gravel Soil via Evolutionary Polynomial Regression. Appl Sci 2022;12:9986. https://doi.org/10.3390/app12199986.
[19]   Rabunal JR, Puertas J. Hybrid System with Artificial Neural Networks and Evolutionary Computation in Civil Engineering. Artif. Neural Networks Real-Life Appl., IGI Global; 2006, p. 166–87. https://doi.org/10.4018/978-1-59140-902-1.ch008.
[20]   Adamu M, Olalekan SS, Aliyu MM. Optimizing the Mechanical Properties of Pervious Concrete Containing Calcium Carbide and Rice Husk Ash using Response Surface Methodology. J Soft Comput Civ Eng 2020;4:98–115. https://doi.org/10.22115/scce.2020.229019.1216.
[21]   Hasanipanah M, Jahed Armaghani D, Khamesi H, Bakhshandeh Amnieh H, Ghoraba S. Several non-linear models in estimating air-overpressure resulting from mine blasting. Eng Comput 2016;32. https://doi.org/10.1007/s00366-015-0425-y.
[22]   Hasanipanah M, Monjezi M, Shahnazar A, Jahed Armaghani D, Farazmand A. Feasibility of indirect determination of blast induced ground vibration based on support vector machine. Meas J Int Meas Confed 2015;75. https://doi.org/10.1016/j.measurement.2015.07.019.
[23]   Rezaei M, Majdi A, Monjezi M. An intelligent approach to predict unconfined compressive strength of rock surrounding access tunnels in longwall coal mining. Neural Comput Appl 2014.
[24]   Tsang L, He B, Rashid ASA, Jalil AT, Sabri MMS. Predicting the Young’s Modulus of Rock Material Based on Petrographic and Rock Index Tests Using Boosting and Bagging Intelligence Techniques. Appl Sci 2022;12:10258. https://doi.org/10.3390/app122010258.
[25]   Karami H, Ghazvinian H, Dehghanipour M, Ferdosian M. Investigating the Performance of Neural Network Based Group Method of Data Handling to Pan’s Daily Evaporation Estimation (Case Study: Garmsar City). J Soft Comput Civ Eng 2021;5:1–18. https://doi.org/10.22115/scce.2021.274484.1282.
[26]   Khademi A, Behfarnia K, Kalman Šipoš T, Miličević I. The Use of Machine Learning Models in Estimating the Compressive Strength of Recycled Brick Aggregate Concrete. Comput Eng Phys Model 2021;4:1–25. https://doi.org/10.22115/cepm.2021.297016.1181.
[27]   Lu S, Koopialipoor M, Asteris PG, Bahri M, Armaghani DJ. A Novel Feature Selection Approach Based on Tree Models for Evaluating the Punching Shear Capacity of Steel Fiber-Reinforced Concrete Flat Slabs. Materials (Basel) 2020;13:3902.
[28]   Liao X, Khandelwal M, Yang H, Koopialipoor M, Murlidhar BR. Effects of a proper feature selection on prediction and optimization of drilling rate using intelligent techniques. Eng Comput 2019:https://doi.org/10.1007/s00366-019-00711-6.
[29]   Shirzadi A, Shahabi H, Chapi K, Bui DT, Pham BT, Shahedi K, et al. A comparative study between popular statistical and machine learning methods for simulating volume of landslides. Catena 2017;157:213–26.
[30]   Pourghasemi HR, Sadhasivam N, Kariminejad N, Collins A. Gully erosion spatial modelling: Role of machine learning algorithms in selection of the best controlling factors and modelling process. Geosci Front 2020.
[31]   Zhang R, Wu C, Goh ATC, Böhlke T, Zhang W. Estimation of diaphragm wall deflections for deep braced excavation in anisotropic clays using Ensemble Learning. Geosci Front 2020.
[32]   Tang D, Gordan B, Koopialipoor M, Jahed Armaghani D, Tarinejad R, Thai Pham B, et al. Seepage Analysis in Short Embankments Using Developing a Metaheuristic Method Based on Governing Equations. Appl Sci 2020;10:1761.
[33]   Chen L, Fakharian P, Rezazadeh Eidgahee D, Haji M, Mohammad Alizadeh Arab A, Nouri Y. Axial compressive strength predictive models for recycled aggregate concrete filled circular steel tube columns using ANN, GEP, and MLR. J Build Eng 2023;77:107439. https://doi.org/10.1016/j.jobe.2023.107439.
[34]   Al-Fahdawi OAS, Barroso LR, Soares RW. Adaptive Neuro-Fuzzy and Simple Adaptive Control Methods for Attenuating the Seismic Responses of Coupled Buildings with Semi-Active Devices: Comparative Study. J Soft Comput Civ Eng 2019;3:1–21. https://doi.org/10.22115/scce.2019.199731.1128.
[35]   Li XZ, Kong JM. Application of GA–SVM method with parameter optimization for landslide development prediction. Nat Hazards Earth Syst Sci 2014;14:525–33.
[36]   Zhang W, Zhang R, Wu C, Goh ATC, Lacasse S, Liu Z, et al. State-of-the-art review of soft computing applications in underground excavations. Geosci Front 2020;11:1095–106. https://doi.org/10.1016/j.gsf.2019.12.003.
[37]   Jahed Armaghani D, Safari V, Fahimifar A, Mohd Amin MF, Monjezi M, Mohammadi MA. Uniaxial compressive strength prediction through a new technique based on gene expression programming. Neural Comput Appl 2017. https://doi.org/10.1007/s00521-017-2939-2.
[38]   Faradonbeh RS, Hasanipanah M, Amnieh HB, Armaghani DJ, Monjezi M. Development of GP and GEP models to estimate an environmental issue induced by blasting operation. Environ Monit Assess 2018;190:351.
[39]   Tien Bui D, Pham BT, Nguyen QP, Hoang N-D. Spatial prediction of rainfall-induced shallow landslides using hybrid integration approach of Least-Squares Support Vector Machines and differential evolution optimization: a case study in Central Vietnam. Int J Digit Earth 2016;9:1077–1097.
[40]   Kotsiantis SB, Zaharakis I, Pintelas P. Supervised machine learning: A review of classification techniques. Emerg Artif Intell Appl Comput Eng 2007;160:3–24.
[41]   Cal Y. Soil classification by neural network. Adv Eng Softw 1995;22:95–97.
[42]   Wen-tao MA. Application of support vector machine to classification of expansive soils [J]. Rock Soil Mech 2005;11.
[43]   Zhai Y, Thomasson JA, Boggess III JE, Sui R. Soil texture classification with artificial neural networks operating on remote sensing data. Comput Electron Agric 2006;54:53–68.
[44]   Shaffiee Haghshenas S, Careddu N, Jafarzadeh Ghoushchi S, Mikaeil R, Kim T-H, Geem ZW. Quantitative and Qualitative Analysis of Harmony Search Algorithm in Geomechanics and Its Applications, 2022, p. 13–23. https://doi.org/10.1007/978-981-19-2948-9_2.
[45]   Akbarzadeh M, Shaffiee Haghshenas S, Jalali SME, Zare S, Mikaeil R. Developing the rule of thumb for evaluating penetration rate of TBM, using binary classification. Geotech Geol Eng 2022;40:4685–703.
[46]   Guido G, Haghshenas SS, Haghshenas SS, Vitale A, Astarita V, Park Y, et al. Evaluation of Contributing Factors Affecting Number of Vehicles Involved in Crashes Using Machine Learning Techniques in Rural Roads of Cosenza, Italy. Saf 2022, Vol 8, Page 28 2022;8:28. https://doi.org/10.3390/SAFETY8020028.
[47]   Neaupane KM, Achet SH. Use of backpropagation neural network for landslide monitoring: a case study in the higher Himalaya. Eng Geol 2004;74:213–26.
[48]   Murthy SK. Automatic construction of decision trees from data: A multi-disciplinary survey. Data Min Knowl Discov 1998;2:345–89.
[49]   Myles AJ, Feudale RN, Liu Y, Woody NA, Brown SD. An introduction to decision tree modeling. J Chemom A J Chemom Soc 2004;18:275–85.
[50]   Debeljak M, Džeroski S. Decision trees in ecological modelling. Model. complex Ecol. Dyn., Springer; 2011, p. 197–209.
[51]   Pham BT, Bui DT, Prakash I, Dholakia MB. Hybrid integration of Multilayer Perceptron Neural Networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS. Catena 2017;149:52–63.
[52]   Freund Y. Boosting a weak learning algorithm by majority. Inf Comput 1995;121:256–85.
[53]   Hong H, Liu J, Bui DT, Pradhan B, Acharya TD, Pham BT, et al. Landslide susceptibility mapping using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest ensembles in the Guangchang area (China). Catena 2018;163:399–413.
[54]   Monjezi M, Mehrdanesh A, Malek A, Khandelwal M. Evaluation of effect of blast design parameters on flyrock using artificial neural networks. Neural Comput Appl 2013;23:349–56.
[55]   Bui DT, Tsangaratos P, Nguyen V-T, Van Liem N, Trinh PT. Comparing the prediction performance of a Deep Learning Neural Network model with conventional machine learning models in landslide susceptibility assessment. CATENA 2020;188:104426.
[56]   McCulloch WS, Pitts W. A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 1943;5:115–33.
[57]   Khandelwal M, Singh TN. Correlating static properties of coal measures rocks with P-wave velocity. Int J Coal Geol 2009;79:55–60.
[58]   Yang H, Koopialipoor M, Armaghani DJ, Gordan B, Khorami M, Tahir MM. Intelligent design of retaining wall structures under dynamic conditions. STEEL Compos Struct 2019;31:629–40.
[59]   Koopialipoor M, Ghaleini EN, Tootoonchi H, Jahed Armaghani D, Haghighi M, Hedayat A. Developing a new intelligent technique to predict overbreak in tunnels using an artificial bee colony-based ANN. Environ Earth Sci 2019;78:165. https://doi.org/10.1007/s12665-019-8163-x.
[60]   Ye J, Koopialipoor M, Zhou J, Armaghani DJ, He X. A Novel Combination of Tree-Based Modeling and Monte Carlo Simulation for Assessing Risk Levels of Flyrock Induced by Mine Blasting. Nat Resour Res 2020:1–19.
[61]   Gordan B, Koopialipoor M, Clementking A, Tootoonchi H, Mohamad ET. Estimating and optimizing safety factors of retaining wall through neural network and bee colony techniques. Eng Comput 2018:https://doi.org/10.1007/s00366-018-0642-2.
[62]   Mohamad ET, Armaghani DJ, Mahdyar A, Komoo I, Kassim KA, Abdullah A, et al. Utilizing regression models to find functions for determining ripping production based on laboratory tests. Measurement 2017;111:216–25.
[63]   Hecht-Nielsen R. Kolmogorov’s mapping neural network existence theorem. Proc. Int. Jt. Conf. Neural Networks, vol. 3, 1989, p. 11–4.
[64]   Ripley BD. Statistical aspects of neural networks. Networks Chaos—Statistical Probabilistic Asp 1993;50:40–123.
[65]   Paola JD. Neural network classification of multispectral imagery. Master Tezi, Univ Arizona, USA 1994.
[66]   Wang JN. Seismic design of tunnels: a state-of-the-art approach. Parsons Brinckerhoff Quade & Douglas. Inc, New York, Monogr 1993;7.
[67]   Masters T, Schwartz M. Practical neural network recipes in C. IEEE Trans Neural Networks 1994;5:853.
[68]   Kaastra I, Boyd M. Designing a neural network for forecasting financial and economic time series. Neurocomputing 1996;10:215–36.
[69]   Kanellopoulos I, Wilkinson GG. Strategies and best practice for neural network image classification. Int J Remote Sens 1997;18:711–25.

Files

History

  • Receive Date: 31 March 2023
  • Revise Date: 29 August 2023
  • Accept Date: 14 September 2023

Share

How to cite

Statistics