2,175
Views
86
CrossRef citations to date
0
Altmetric
Articles

Potential of hybrid evolutionary approaches for assessment of geo-hazard landslide susceptibility mapping

, , , &
Pages 1667-1693 | Received 03 Jan 2019, Accepted 09 Apr 2019, Published online: 01 Jul 2019

References

  • Alnaqi AA, Moayedi H, Shahsavar A, Nguyen TK. 2019. Prediction of energetic performance of a building integrated photovoltaic/thermal system thorough artificial neural network and hybrid particle swarm optimization models. Energy Conv Manag. 183:137–148.
  • Althuwaynee OF, Pradhan B, Park H-J, Lee JH. 2014. A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping. Catena. 114:21–36.
  • Arora MK, Das Gupta AS, Gupta RP. 2004. An artificial neural network approach for landslide hazard zonation in the Bhagirathi (Ganga) Valley, Himalayas. Int J Remote Sens. 25(3):559–572.
  • Asadi A, Moayedi H, Huat BBK, Boroujeni FZ, Parsaie A, Sojoudi S. 2011a. Prediction of zeta potential for tropical peat in the presence of different cations using artificial neural networks. Int J Electrochem Sci. 6:1146–1158.
  • Asadi A, Moayedi H, Huat BBK, Parsaie A, Taha MR. 2011b. Artificial neural networks approach for electrochemical resistivity of highly organic soil. Int J Electrochem Sci. 6:1135–1145.
  • Baeza C, Corominas J. 2001. Assessment of shallow landslide susceptibility by means of multivariate statistical techniques. Earth Surf Process Landforms. 26(12):1251–1263.
  • Balamurugan G, Ramesh V, Touthang M. 2016. Landslide susceptibility zonation mapping using frequency ratio and fuzzy gamma operator models in part of NH-39, Manipur, India. Nat Hazards. 84(1):465–488.
  • Bui DT, Tuan TA, Hoang N-D, Thanh NQ, Nguyen DB, Van Liem N, Pradhan B. 2017. Spatial prediction of rainfall-induced landslides for the Lao Cai area (Vietnam) using a hybrid intelligent approach of least squares support vector machines inference model and artificial bee colony optimization. Landslides. 14:447–458.
  • Çevik E, Topal T. 2003. GIS-based landslide susceptibility mapping for a problematic segment of the natural gas pipeline, Hendek (Turkey). Environ Geol. 44(8):949–962.
  • Chaytor JD, Geist EL, Paull CK, Caress DW, Gwiazda R, Fucugauchi JU, Vieyra MR. 2016. Source characterization and tsunami modeling of submarine landslides along the yucatan shelf/campeche escarpment, Southern Gulf of Mexico. Pure Appl Geophys. 173(12):4101–4116.
  • Chen W, Li W, Chai H, Hou E, Li X, Ding X. 2016. GIS-based landslide susceptibility mapping using analytical hierarchy process (AHP) and certainty factor (CF) models for the Baozhong region of Baoji City, China. Environ Earth Sci. 75:63–76..
  • Chen W, Li W, Hou E, Bai H, Chai H, Wang D, Cui X, Wang Q. 2015. Application of frequency ratio, statistical index, and index of entropy models and their comparison in landslide susceptibility mapping for the Baozhong Region of Baoji, China. Arab J Geosci. 8(4):1829–1841.
  • Chen W, Panahi M, Pourghasemi HR. 2017a. Performance evaluation of GIS-based new ensemble data mining techniques of adaptive neuro-fuzzy inference system (ANFIS) with genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO) for landslide spatial modelling. Catena. 157:310–324.
  • Chen W, Panahi M, Tsangaratos P, Shahabi H, Ilia I, Panahi S, Li S, Jaafari A, Ahmad BB. 2019. Applying population-based evolutionary algorithms and a neuro-fuzzy system for modeling landslide susceptibility. Catena. 172:212–231.
  • Chen W, Pourghasemi HR, Panahi M, Kornejady A, Wang J, Xie X, Cao S. 2017b. Spatial prediction of landslide susceptibility using an adaptive neuro-fuzzy inference system combined with frequency ratio, generalized additive model, and support vector machine techniques. Geomorphology. 297:69–85.
  • Cheng M-Y, Hoang N-D. 2015. Typhoon-induced slope collapse assessment using a novel bee colony optimized support vector classifier. Nat Hazards. 78(3):1961–1978.
  • Clerc M, Kennedy J. 2002. The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Computat. 6(1):58–73.
  • Dong Y, Wang D, Randolph MF. 2017. Runout of submarine landslide simulated with material point method. Proceedings of the 1st International Conference on the Material Point Method. Amsterdam: Elsevier Science Bv. p. 357–364.
  • Eberhart R, Kennedy J. 1995. A new optimizer using particle swarm theory. Proceedings of the Micro Machine and Human Science, 1995. MHS'95. Proceedings of the Sixth International Symposium on IEEE: Nagoya, Japan, Japan.
  • El-Bakry MY. 2003. Feedforward neural networks modelling for K–P interactions. Chaos Solitons Fractals. 18:995–1000.
  • Ercanoglu M, Gokceoglu C. 2002. Assessment of landslide susceptibility for a landslide-prone area (north of Yenice, NW Turkey) by fuzzy approach. Environ. Geol. 41:720–730.
  • Ercanoglu M, Gokceoglu C. 2004. Use of fuzzy relations to produce landslide susceptibility map of a landslide-prone area (West Black Sea Region, Turkey). Eng Geol. 75(3–4):229–250.
  • Farrokhzad F, Barari A, Ibsen LB, Choobbasti AJ. 2011. Predicting subsurface soil layering and landslide risk with Artificial Neural Networks: a case study from Iran. Geol Carpath. 62(5):477–485.
  • Gao W, Dimitrov D, Abdo H. 2018a. Tight independent set neighbourhood union condition for fractional critical deleted graphs and ID deleted graphs. Discr Cont Dyn Syst-S. 123–144.
  • Gao W, Guirao JLG, Abdel-Aty M, Xi W. 2019. An independent set degree condition for fractional critical deleted graphs. Discr Cont Dyn Syst. 12:877–886.
  • Gao W, Guirao JLG, Basavanagoud B, Wu J. 2018b. Partial multi-dividing ontology learning algorithm. Inf Sci. 467:35–58.
  • Gao W, Wang W, Dimitrov D, Wang Y. 2018c. Nano properties analysis via fourth multiplicative ABC indicator calculating. Arab J Chem. 11(6):793–801.
  • Gao W, Wu H, Siddiqui MK, Baig AQ. 2018. Study of biological networks using graph theory. Saudi J Biol Sci. 25(6):1212–1219.
  • Hagan MT, Menhaj MB. 1994. Training feedforward networks with the Marquardt algorithm. IEEE Trans Neural Netw. 5(6):989–993.
  • Haykin S. 1994. Neural networks: a comprehensive foundation. Upper Saddle River, New Jersey (NJ): Prentice Hall PTR.
  • Hong H, Liu J, Zhu A-X, Shahabi H, Pham BT, Chen W, Pradhan B, Bui DT. 2017. A novel hybrid integration model using support vector machines and random subspace for weather-triggered landslide susceptibility assessment in the Wuning area (China). Environ Earth Sci. 76:652.
  • Hong HY, Naghibi SA, Pourghasemi HR, Pradhan B. 2016. GIS-based landslide spatial modeling in Ganzhou City, China. Arab J Geosci. 9:26.
  • Hoque Mozumder, M M, Shamsuzzaman, M M, Rashed-Un-Nabi, M, Karim, E. 2018. Social-ecological dynamics of the small scale fisheries in Sundarban Mangrove Forest, Bangladesh. Aquaculture and Fisheries. 3(1):38–49. doi:10.1016/j.aaf.2017.12.002.
  • Hornik K, Stinchcombe M, White H. 1989. Multilayer feedforward networks are universal approximators. Neural Netw. 2(5):359–366.
  • Hu, S, Guan, X, Guo, M, Wang, J. 2018. Environmental load of solid wood floor production from larch grown at different planting densities based on a life cycle assessment. J FOR Res. 29(5):1443–1448. doi:10.1007/s11676-017-0529-x.
  • Karaboga D. 2005. An idea based on honey bee swarm for numerical optimization. Turkey: Erciyes University; Technical report-TR06.
  • Karaboga D, Akay B, Ozturk C. 2007. Artificial bee colony (ABC) optimization algorithm for training feed-forward neural networks. Proceedings of the International conference on modeling decisions for artificial intelligence; Springer.
  • Koopialipoor M, Armaghani DJ, Hedayat A, Marto A, Gordan B. 2018. Applying various hybrid intelligent systems to evaluate and predict slope stability under static and dynamic conditions. Soft Comput. 1–17.
  • Lee S, Ryu J-H, Won J-S, Park H-J. 2004. Determination and application of the weights for landslide susceptibility mapping using an artificial neural network. Eng. Geol. 71(3–4):289–302.
  • Lian C, Zeng ZG, Yao W, Tang HM. 2013. Displacement prediction model of landslide based on a modified ensemble empirical mode decomposition and extreme learning machine. Nat Hazards. 66(2):759–771. Mar
  • Meng QK, Miao F, Zhen J, Wang XY, Wang A, Peng Y, Fan Q. 2016. GIS-based landslide susceptibility mapping with logistic regression, analytical hierarchy process, and combined fuzzy and support vector machine methods: a case study from Wolong Giant Panda Natural Reserve, China. Bull Eng Geol Environ. 75(3):923–944.
  • Moayedi H. 2018. Optimization of ANFIS with GA and PSO estimating α in driven shafts. Eng Comput. 35:1–12.
  • Moayedi H, Armaghani DJ. 2017. Optimizing an ANN model with ICA for estimating bearing capacity of driven pile in cohesionless soil. Eng Comput. 1–10.
  • Moayedi H, Hayati S. 2018a. Applicability of a CPT-based neural network solution in predicting load-settlement responses of bored pile. Int J Geomech. 18.
  • Moayedi H, Hayati S. 2018b. Artificial intelligence design charts for predicting friction capacity of driven pile in clay. Neural Comput Appl. 31:1–17. https://doi.org/10.1007/s00521-018-3555-5.
  • Moayedi H, Hayati S. 2018c. Modelling and optimization of ultimate bearing capacity of strip footing near a slope by soft computing methods. Appl Soft Comput. 66:208–219.
  • Moayedi H, Huat BBK, Mohammad Ali TA, Asadi A, Moayedi F, Mokhberi M. 2011. Preventing landslides in times of rainfall: case study and FEM analyses. Disaster Prev Manag. 20(2):115–124.
  • Moayedi H, Mehrabi M, Mosallanezhad M, Rashid ASA, Pradhan B. 2018. Modification of landslide susceptibility mapping using optimized PSO-ANN technique. Eng Comput. 35(2):1–18. DOI: 10.1007/s00366-018-0644-0.
  • Moayedi H, Rezaei A. 2017. An artificial neural network approach for under-reamed piles subjected to uplift forces in dry sand. Neural Comput Appl. 1–10.
  • Mokoena, B T, Musakwa, W. 2018. Mobile GIS occupancy audit of Ulana informal settlement in Ekurhuleni municipality, South Africa. Geo-Spatial Information Science. 21(4):322–330. doi:10.1080/10095020.2018.1519349.
  • Moore ID, Grayson RB, Ladson AR. 1991. Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrol Process. 5(1):3–30.
  • Mosallanezhad M, Moayedi H. 2017. Developing hybrid artificial neural network model for predicting uplift resistance of screw piles. Arab J Geosci. 10:10. Nov
  • Nandy S, Sarkar PP, Das A. 2012. Training a feed-forward neural network with artificial bee colony based backpropagation method. 1209:2548. arXiv preprint arXiv
  • Nefeslioglu HA, Gokceoglu C, Sonmez H. 2008. An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps. Eng Geol. 97(3–4):171–191.
  • Oh H-J, Pradhan B. 2011. Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area. Comput Geosci. 37(9):1264–1276.
  • Parsons T, Geist EL, Ryan HF, Lee HJ, Haeussler PJ, Lynett P, Hart PE, Sliter R, Roland E. 2014. Source and progression of a submarine landslide and tsunami: the 1964 Great Alaska earthquake at Valdez. J Geophys Res Solid Earth. 119(11):8502–8516. Nov
  • Pourghasemi HR, Beheshtirad M, Pradhan B. 2016. A comparative assessment of prediction capabilities of modified analytical hierarchy process (M-AHP) and Mamdani fuzzy logic models using Netcad-GIS for forest fire susceptibility mapping. Geomat Nat Hazards Risk. 7(2):861–885.
  • Pourghasemi HR, Jirandeh AG, Pradhan B, Xu C, Gokceoglu C. 2013a. Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran. J Earth Syst Sci. 122(2):349–369.
  • Pourghasemi HR, Mohammady M, Pradhan B. 2012a. Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin, Iran. Catena. 97:71–84.
  • Pourghasemi HR, Moradi HR, Aghda SF. 2013b. Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances. Nat Hazards. 69(1):749–779.
  • Pourghasemi HR, Pradhan B, Gokceoglu C. 2012b. Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Nat Hazards. 63(2):965–996.
  • Pradhan B. 2010. Landslide Susceptibility mapping of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches. J Indian Soc Remote Sens. 38(2):301–320.
  • Pradhan B, Lee S. 2010. Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland, Malaysia. Landslides. 7(1):13–30.
  • Regmi AD, Devkota KC, Yoshida K, Pradhan B, Pourghasemi HR, Kumamoto T, Akgun A. 2014. Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya. Arab J Geosci. 7(2):725–742.
  • Regmi AD, Dhital MR, Zhang J-Q, Su L-J, Chen X-Q. 2016. Landslide susceptibility assessment of the region affected by the 25 April 2015 Gorkha earthquake of Nepal. J Mt Sci. 13(11):1941–1957.
  • Sarangi PP, Sahu A, Panda M. 2014. Training a feed-forward neural network using artificial bee colony with back-propagation algorithm. Intell Comput Network Inf. 243:511–519.
  • Shahri AA. 2016. An optimized artificial neural network structure to predict clay sensitivity in a high landslide prone area using piezocone penetration test (CPTu) data: a case study in Southwest of Sweden. Geotech Geol Eng. 34:745–758.
  • Vakhshoori V, Zare M. 2016. Landslide susceptibility mapping by comparing weight of evidence, fuzzy logic, and frequency ratio methods. Geomat Nat Hazards Risk. 7(5):1731–1752.
  • Van Westen C. 1997a. Statistical landslide hazard analysis. ILWIS 2.1 for windows application guide. Enshede, The Netherlands: ITC Publication. p. 73–84.
  • Van Westen CJ. 1997b. Statistical landslide hazard analysis. ILWIS. 2:73–84.
  • Wang Q, Li W, Chen W, Bai H. 2015. GIS-based assessment of landslide susceptibility using certainty factor and index of entropy models for the Qianyang County of Baoji city, China. J Earth Syst Sci. 124(7):1399–1415.
  • Wang S-C. 2003. Interdisciplinary computing in java programming. New York, NY: Springer. p. 81–100.
  • Wu X, Ren F, Niu R. 2014. Landslide susceptibility assessment using object mapping units, decision tree, and support vector machine models in the Three Gorges of China. Environ Earth Sci. 71(11):4725–4738.
  • Xie Z, Chen G, Meng X, Zhang Y, Qiao L, Tan L. 2017. A comparative study of landslide susceptibility mapping using weight of evidence, logistic regression and support vector machine and evaluated by SBAS-InSAR monitoring: Zhouqu to Wudu segment in Bailong River Basin, China. Environ Earth Sci. 76:313.
  • Xu C, Dai F, Xu X, Lee YH. 2012. GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China. Geomorphology. 145:70–80.
  • Yavari-Ramshe S, Ataie-Ashtiani B. 2016. Numerical modeling of subaerial and submarine landslide-generated tsunami waves-recent advances and future challenges. Landslides. 13(6):1325–1368.
  • Yesilnacar EK. 2005. The application of computational intelligence to landslide susceptibility mapping in Turkey. Turkey: University of Melbourne.
  • Youssef AM, Pradhan B, Jebur MN, El-Harbi HM. 2015. Landslide susceptibility mapping using ensemble bivariate and multivariate statistical models in Fayfa area, Saudi Arabia. Environ Earth Sci. 73(7):3745–3761.
  • Zou, J, Bui, T, Xiao, Y, Doan, C V. 2018. Dam deformation analysis based on BPNN merging models. Geo-Spatial Information Science. 21(2):149–157. doi:10.1080/10095020.2017.1386848.