A particle-based optimization of artificial neural network for earthquake-induced landslide assessment in Ludian county, China

References

• Ahmouda A, Hochmair HH, Cvetojevic S. 2018. Analyzing the effect of earthquakes on OpenStreetMap contribution patterns and tweeting activities. Geo Spat Inf Sci. 21(3):195–212.
   Google Scholar
• Asadi A, Moayedi H, Huat BBK, Parsaie A, Taha MR. 2011. Artificial neural networks approach for electrochemical resistivity of highly organic soil. Int J Electrochem Sci. 6:1135–1145.
   Google Scholar
• Beven KJ, Kirkby MJ. 1979. A physically based, variable contributing area model of basin hydrology/Un modèle à base physique de zone d'appel variable de l'hydrologie du bassin versant. Hydrol Sci J. 24(1):43–69.
   Google Scholar
• Binh Thai P, Manh Duc N, Kien-Trinh Thi B, Prakash I, Chapi K, Dieu Tien B. 2019. A novel artificial intelligence approach based on multi-layer perceptron neural network and biogeography-based optimization for predicting coefficient of consolidation of soil. Catena. 173:302–311.
   Google Scholar
• Binh Thai P, Prakash I, Dieu Tien B. 2018. Spatial prediction of landslides using a hybrid machine learning approach based on random subspace and classification and regression trees. Geomorphology. 303:256–270.
   Google Scholar
• Bui DT, Tuan TA, Klempe H, Pradhan B, Revhaug I. 2016. Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides. 13:361–378.
   Google Scholar
• Chen W, Chai H, Sun X, Wang Q, Ding X, Hong H. 2016. A GIS-based comparative study of frequency ratio, statistical index and weights-of-evidence models in landslide susceptibility mapping. Arab J Geosci. 9:204.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• Demir G, Aytekin M, Akgun A. 2015. Landslide susceptibility mapping by frequency ratio and logistic regression methods: an example from Niksar–Resadiye (Tokat, Turkey). Arab J Geosci. 8(3):1801–1812.
   Google Scholar
• 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.
   Google Scholar
• Eberhart R, Kennedy J. 1995. A new optimizer using particle swarm theory. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan'9. IEEE.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• Ghorbani MA, Kazempour R, Chau K-W, Shamshirband S, Ghazvinei PT. 2018. Forecasting pan evaporation with an integrated artificial neural network quantum-behaved particle swarm optimization model: a case study in Talesh, Northern Iran. Eng Appl Comput Fluid Mech. 12:724–737.
   Google Scholar
• Greco SE. 2018. Seven possible states of geospatial data with respect to map projection and definition: a novel pedagogical device for GIS education. Geo Spat Inf Sci. 21(4):288–293.
   Google Scholar
• Hebb D. 1949. The organization of behavior: a neurophysiological approach. A Wiley Book in Clinical Psychology., 62–78, 1949, John Wiley & Sons Inc, New York, United States.
   Google Scholar
• Jaafari A, Panahi M, Pham BT, Shahabi H, Bui DT, Rezaie F, Lee S. 2019. Meta optimization of an adaptive neuro-fuzzy inference system with grey wolf optimizer and biogeography-based optimization algorithms for spatial prediction of landslide susceptibility. Catena. 175:430–445.
   Google Scholar
• Jing-Chun X, Hui-Wen L, Zi-Li L. 2015. Analysis of landslide hazard area in Ludian earthquake based on Random Forests. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-7/W3, 2015 36th International Symposium on Remote Sensing of Environment, 11–15 May 2015, Berlin, Germany
   Google Scholar
• Li L, Liu R, Pirasteh S, Chen X, He L, Li J. 2017. A novel genetic algorithm for optimization of conditioning factors in shallow translational landslides and susceptibility mapping. Arab J Geosci. 10:209.
   Google Scholar
• McCulloch WS, Pitts W. 1943. A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys. 5(4):115–133.
   Google Scholar
• Moayedi H, Mehrabi M, Mosallanezhad M, Rashid ASA, Pradhan B. 2018a. Modification of landslide susceptibility mapping using optimized PSO-ANN technique. Eng Comput. Vol 35, 1–18.
   Google Scholar
• Moayedi H, Mosallanezhad M, Mehrabi M, Safuan A. 2018b. A systematic review and meta-analysis of artificial neural network application in geotechnical engineering: theory and applications. Neural Comput Appl. 35:1–24.
   Google Scholar
• Moore ID, Grayson R, Ladson A. 1991. Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrol Process. 5(1):3–30.
   Google Scholar
• Mosallanezhad M, Moayedi H. 2017. Developing hybrid artificial neural network model for predicting uplift resistance of screw piles. Arab J Geosci. 10:10.
   Google Scholar
• Nath SK. 2004. Seismic hazard mapping and microzonation in the Sikkim Himalaya through GIS integration of site effects and strong ground motion attributes. Nat Hazards. 31:319–342.
   Google Scholar
• Nguyen H, Bui X-N, Bui H-B, Mai NL. 2018. A comparative study of artificial neural networks in predicting blast-induced air-blast overpressure at Deo Nai open-pit coal mine, Vietnam. Neural Comput Appl. 32:1–17.
   Google Scholar
• 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. 37:1264–1276.
   Google Scholar
• Pourghasemi H, Pradhan B, Gokceoglu C, Moezzi KD. 2013. A comparative assessment of prediction capabilities of Dempster–Shafer and weights-of-evidence models in landslide susceptibility mapping using GIS. Geomat Nat Haz Risk. 4(2):93–118.
   Google Scholar
• Pradhan B, Lee S. 2010. Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland, Malaysia. Landslides. 7(1):13–30.
   Google Scholar
• Seyedashraf O, Mehrabi M, Akhtari AA. 2018. Novel approach for dam break flow modeling using computational intelligence. J Hydrol. 559:1028–1038.
   Google Scholar
• Tien Bui D, Pham BT, Nguyen QP, Hoang N-D. 2016. 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. 9(11):1077–1097.
   Google Scholar
• Tien Bui D, Shahabi H, Shirzadi A, Chapi K, Hoang N-D, Pham B, Bui Q-T, Tran C-T, Panahi M, Bin Ahmad B, et al. 2018. A novel integrated approach of relevance vector machine optimized by imperialist competitive algorithm for spatial modeling of shallow landslides. Remote Sens. 10(10):1538.
   Google Scholar
• Van-Dung H, Le M-H, Truc Thanh T, Van-Huy P. 2018. Improving traffic signs recognition based region proposal and deep neural networks. In: Nguyen N., Hoang D., Hong TP., Pham H., Trawiński B. (eds) Intelligent Information and Database Systems. ACIIDS 2018. Lecture Notes in Computer Science, vol 10752. Springer, Champ. 604–613.
   Google Scholar
• Wang J, Fu G, Li W, Shi Y, Pang J, Wang Q, Lü W, Liu C, Liu J. 2018. The effects of two free-floating plants (Eichhornia crassipes and Pistia stratiotes) on the burrow morphology and water quality characteristics of pond loach (Misgurnus anguillicaudatus) habitat. Aquac Fish. 3(1):22–29.
   Google Scholar
• Wang Q, Li W, Chen W, Bai H. 2015a. 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.
   Google Scholar
• Wang S-C. 2003. Artificial neural network. In: Interdisciplinary computing in java programming. The Springer International Series in Engineering and Computer Science, vol 743. Springer, Boston, MA; vol 743. Springer, Boston, MA, p. 81–100.
   Google Scholar
• Wang W-J, Huai W-X, Zeng Y-H, Zhou J-F. 2015b. Analytical solution of velocity distribution for flow through submerged large deflection flexible vegetation. Appl Math Mech Engl Ed. 36(1):107–120.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• Yilmaz I. 2009. Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from Kat landslides (Tokat—Turkey). Comput Geosci. 35:1125–1138.
   Google Scholar
• Zhong-Sheng L. 2003. The State of the Art of the Research on Seismic Landslide Hazard at Home and abroad. J Catastrophol. 4:64–70.
   Google Scholar
• Zhou S, Chen G, Fang L. 2016. Distribution pattern of landslides triggered by the 2014 Ludian earthquake of China: implications for regional threshold topography and the seismogenic fault identification. ISPRS Int J Geo Inf. 5(4):46.
   Google Scholar