{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T15:03:11Z","timestamp":1779375791171,"version":"3.53.1"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2015,6,30]],"date-time":"2015-06-30T00:00:00Z","timestamp":1435622400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2016,8]]},"DOI":"10.1007\/s00521-015-1976-y","type":"journal-article","created":{"date-parts":[[2015,6,29]],"date-time":"2015-06-29T04:55:15Z","timestamp":1435553715000},"page":"1771-1784","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Application of multi-gene genetic programming based on separable functional network for landslide displacement prediction"],"prefix":"10.1007","volume":"27","author":[{"given":"Jiejie","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhigang","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ping","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huiming","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2015,6,30]]},"reference":[{"issue":"4","key":"1976_CR1","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1007\/s100640000062","volume":"59","author":"SQ Qin","year":"2001","unstructured":"Qin SQ, Jiao JJ, Wang SJ (2001) The predictable time scale of landslides. Bull Eng Geol Environ 59(4):307\u2013312","journal-title":"Bull Eng Geol Environ"},{"key":"1976_CR2","first-page":"377","volume":"4","author":"SQ Qin","year":"2002","unstructured":"Qin SQ, Jiao JJ, Wang SJ (2002) A nonlinear dynamical model of landslide evolution. Geomorphol 4:377\u201385","journal-title":"Geomorphol"},{"key":"1976_CR3","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1007\/s11069-009-9431-y","volume":"53","author":"G Sorbino","year":"2010","unstructured":"Sorbino G, Sica C, Cascini L (2010) Susceptibility analysis of shallow landslides source areas using physically based models. Nat Hazards Rev 53:313\u2013332","journal-title":"Nat Hazards Rev"},{"key":"1976_CR4","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.ijrmms.2013.12.006","volume":"67","author":"B Francesca","year":"2014","unstructured":"Francesca B, Ivan C, Paolo M, Alberto P (2014) A field experiment for calibrating landslide time-of-failure prediction functions. Int J Rock Mech Min Sci 67:69\u201377","journal-title":"Int J Rock Mech Min Sci"},{"key":"1976_CR5","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.enggeo.2013.11.018","volume":"171","author":"CT Chen","year":"2014","unstructured":"Chen CT, Lin ML, Wang KL (2014) Landslide seismic signal recognition and mobility for an earthquake-induced rockslide in Tsaoling, Taiwan. Eng Geol 171:31\u201344","journal-title":"Eng Geol"},{"issue":"3","key":"1976_CR6","doi-asserted-by":"crossref","first-page":"1491","DOI":"10.1007\/s11069-011-9847-z","volume":"59","author":"DP Kanungo","year":"2011","unstructured":"Kanungo DP, Sarkar S, Sharma S (2011) Combining neural network with fuzzy, certainty factor and likelihood ratio concepts for spatial prediction of landslides. Nat Hazards Rev 59(3):1491\u20131512","journal-title":"Nat Hazards Rev"},{"issue":"1","key":"1976_CR7","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1007\/s12559-012-9148-1","volume":"5","author":"HQ Chen","year":"2013","unstructured":"Chen HQ, Zeng ZG (2013) Deformation prediction of landslide based on improved back-propagation neural network. Cognit Comput 5(1):56\u201362","journal-title":"Cognit Comput"},{"key":"1976_CR8","doi-asserted-by":"crossref","first-page":"759","DOI":"10.1007\/s11069-012-0517-6","volume":"66","author":"C Lian","year":"2013","unstructured":"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 66:759\u2013771","journal-title":"Nat Hazards"},{"key":"1976_CR9","unstructured":"Lian C, Zeng ZG, Yao W, Tang HM (2013) Displacement prediction of landslide based on PSOGSA-ELM with mixed kernel, 2013. In: Sixth international conference on advanced computational intelligence, Hangzhou, China, pp 52\u201357"},{"key":"1976_CR10","unstructured":"Yao W, Zeng ZG, Lian C, Tang HM (2013) Ensembles of echo state networks for time series prediction, 2013. In: Sixth international conference on advanced computational intelligence. Hangzhou, China, pp 299\u2013304"},{"key":"1976_CR11","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.neucom.2013.10.044","volume":"149","author":"J Chen","year":"2015","unstructured":"Chen J, Zeng ZG, Jiang P, Tang HM (2015) Deformation prediction of landslide based on functional networt. Neurocomputing 149:151\u2013157","journal-title":"Neurocomputing"},{"key":"1976_CR12","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.enggeo.2008.01.004","volume":"97","author":"HA Nefelioglu","year":"2008","unstructured":"Nefilioglu 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 97:171\u2013191","journal-title":"Eng Geol"},{"issue":"3","key":"1976_CR13","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1023\/A:1009656525752","volume":"7","author":"E Castillo","year":"1998","unstructured":"Castillo E (1998) Functional networks. Neural Process Lett 7(3):151\u2013159","journal-title":"Neural Process Lett"},{"key":"1976_CR14","volume-title":"An introduction to functional networks with applications","author":"E Castillo","year":"1998","unstructured":"Castillo E, Cobo A, Guti\u00e9rrez JM, Pruneda E (1998) An introduction to functional networks with applications. Kluwer Academic Publishers, New York"},{"issue":"9","key":"1976_CR15","doi-asserted-by":"crossref","first-page":"899","DOI":"10.1016\/j.advwatres.2005.03.001","volume":"28","author":"M Bruen","year":"2005","unstructured":"Bruen M, Yang JQ (2005) Functional networks in real-time flood forecasting\u2014a novel application. Adv Water Resour 28(9):899\u2013909","journal-title":"Adv Water Resour"},{"issue":"1","key":"1976_CR16","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1007\/s00521-004-0402-7","volume":"13","author":"A Iglesias","year":"2004","unstructured":"Iglesias A, Arcay B, Cotos JM, Taboada A, Dafonte C (2004) A comparison between functional networks and artificial neural networks for the prediction of fishing catches. Neural Comput Appl 13(1):24\u201331","journal-title":"Neural Comput Appl"},{"key":"1976_CR17","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1016\/j.eswa.2005.04.008","volume":"29","author":"AI Nun","year":"2005","unstructured":"Nun AI, Arcay LH, Cotos M, Varela J (2005) Optimisation of fishing prediction of artificial neural networks, anfis, functional network and remote sensing images. Expert Syst Appl 29:356\u2013363","journal-title":"Expert Syst Appl"},{"issue":"4","key":"1976_CR18","doi-asserted-by":"crossref","first-page":"2129","DOI":"10.1016\/j.csda.2007.07.006","volume":"52","author":"E Castillo","year":"2008","unstructured":"Castillo E, Hadi AS, Lacruz B, Pruneda RE (2008) Semi-parametric nonlinear regression and transformation using functional networks. Comput Stat Data Anal 52(4):2129\u20132157","journal-title":"Comput Stat Data Anal"},{"issue":"3","key":"1976_CR19","doi-asserted-by":"crossref","first-page":"2187","DOI":"10.1016\/j.eswa.2010.08.005","volume":"8","author":"EA El-Sebakhy","year":"2011","unstructured":"El-Sebakhy EA (2011) Functional networks as a novel data mining paradigm in forecasting software development efforts. Expert Syst Appl 8(3):2187\u20132194","journal-title":"Expert Syst Appl"},{"issue":"12","key":"1976_CR20","doi-asserted-by":"crossref","first-page":"10359","DOI":"10.1016\/j.eswa.2012.01.157","volume":"39","author":"EA El-Sebakhy","year":"2012","unstructured":"El-Sebakhy EA, Asparouhov O, Abdulraheem AA, Al-Majed AA, Wu DH, Latinski K, Raharja I (2012) Functional networks as a new data mining predictive paradigm to predict permeability in a carbonate reservoir. Expert Syst Appl 39(12):10359\u201310375","journal-title":"Expert Syst Appl"},{"issue":"3","key":"1976_CR21","doi-asserted-by":"crossref","first-page":"844","DOI":"10.1109\/TNN.2007.891632","volume":"18","author":"EA El-Sebakhy","year":"2007","unstructured":"El-Sebakhy EA, Hadi AS, Faisal KA (2007) Iterative least squares functional networks classier. IEEE Trans Neural Netw 18(3):844\u2013850","journal-title":"IEEE Trans Neural Netw"},{"issue":"2","key":"1976_CR22","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1002\/nag.1610060206","volume":"6","author":"E Castillo","year":"1982","unstructured":"Castillo E, Luceno A (1982) A critical analysis of some variational methods in slope stability analysis. Int J Numer Anal Methods Geomech 6(2):195\u2013209","journal-title":"Int J Numer Anal Methods Geomech"},{"issue":"2","key":"1976_CR23","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1137\/050641600","volume":"50","author":"E Castillo","year":"2008","unstructured":"Castillo E, Conejo AJ, Aranda E (2008) Sensitivity analysis in calculus of variations. Some applications. SIAM Rev 50(2):294\u2013312","journal-title":"SIAM Rev"},{"issue":"1","key":"1976_CR24","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1142\/S0218202508002607","volume":"18","author":"E Castillo","year":"2008","unstructured":"Castillo E, Luceno A, Pedregal (2008) Composition functionals in calculus of variations. Application to products and quotients. Math Models Methods Appl Sci 18(1):47\u201375","journal-title":"Math Models Methods Appl Sci"},{"key":"1976_CR25","unstructured":"Sara A, Jonas A (2003) GPLAB-A genetic programming toolbox for MATLAB. In: Proceedings of the Nordic MATLAB conference, pp 273\u2013278"},{"issue":"4","key":"1976_CR26","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1023\/A:1010065123132","volume":"1","author":"P Collet","year":"2000","unstructured":"Collet P, Lutton E, Raynal F (2000) Polar IFS + parisian genetic programming = efficient IFS inverse problem solving. Genet Program Evol Mach J 1(4):339\u2013361","journal-title":"Genet Program Evol Mach J"},{"key":"1976_CR27","unstructured":"Ochoa G, Lutton E, Burke E (2007) Cooperative royal road functions. In: Evolution artificielle, vol 10. Tours, France, pp 29\u201331"},{"issue":"2","key":"1976_CR28","first-page":"87","volume":"13","author":"C Ferreira","year":"2001","unstructured":"Ferreira C (2001) Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst 13(2):87\u2013129","journal-title":"Complex Syst"},{"key":"1976_CR29","unstructured":"Miller JF (1999) An empirical study of the efficiency of learning boolean functions using a cartesian genetic programming approach, GECCO 1999. In: Proceedings of the genetic and evolutionary computation conference, Orlando, Florida, pp 1135\u20131142, Morgan Kaufmann, San Francisco"},{"key":"1976_CR30","doi-asserted-by":"crossref","unstructured":"Miller JF, Thomson P (2000) Cartesian genetic programming. In: Proceedings of the 3rd European conference on genetic programming, Edinburgh, lecture notes in computer science, vol 1802. Springer, Berlin, pp 121\u2013132","DOI":"10.1007\/978-3-540-46239-2_9"},{"key":"1976_CR31","unstructured":"Searson DP (2009) GPTIPS: genetic programming and symbolic regression for MATLAB"},{"key":"1976_CR32","unstructured":"Searson DP, Leahy DE, Willis MJ (2010) GPTIPS: an open source genetic programming toolbox for multigene symbolic regression. In: Proceedings of international multi conference on engineering computer science, Hong Kong"},{"key":"1976_CR33","volume-title":"Genetic programming: on the programming of computers by means of natural selection","author":"JR Koza","year":"1992","unstructured":"Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. MIT Press, USA"},{"key":"1976_CR34","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.procs.2014.08.093","volume":"35","author":"CH Lee","year":"2014","unstructured":"Lee CH, Yang CB, Chen HH (2014) Taiwan stock investment with gene expression programming. Proc Comput Sci 35:137\u2013146","journal-title":"Proc Comput Sci"},{"issue":"24","key":"1976_CR35","doi-asserted-by":"crossref","first-page":"18972","DOI":"10.1016\/j.ijhydene.2012.08.101","volume":"37","author":"A Nazari","year":"2012","unstructured":"Nazari A (2012) Prediction performance of PEM fuel cells by gene expression programming. Int J Hydrog Energy 37(24):18972\u201318980","journal-title":"Int J Hydrog Energy"},{"issue":"9","key":"1976_CR36","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1016\/j.neucom.2013.04.005","volume":"121","author":"MM Khan","year":"2013","unstructured":"Khan MM, Ahmad AM, Khan GM, Miller JF (2013) Fast learning neural networks using Cartesian genetic programming. Neurocomputing 121(9):274\u2013289","journal-title":"Neurocomputing"},{"key":"1976_CR37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.csr.2013.09.020","volume":"71","author":"EB Goldstein","year":"2013","unstructured":"Goldstein EB, Coco G, Murray AB (2013) Prediction of wave ripple characteristics using genetic programming. Cont Shelf Res 71:1\u201315","journal-title":"Cont Shelf Res"},{"issue":"1","key":"1976_CR38","first-page":"71","volume":"21","author":"AH Gandomi","year":"2012","unstructured":"Gandomi AH, Alavi AH (2012) A new multi-gene genetic programming approach to nonlinear system modeling. Part I: materials and structural engineering problems. Neural Comput Appl 21(1):71\u2013187","journal-title":"Neural Comput Appl"},{"key":"1976_CR39","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1007\/s00521-011-0735-y","volume":"21","author":"AH Gandomi","year":"2012","unstructured":"Gandomi AH, Alavi AH (2012) A new multi-gene genetic programming approach to nonlinear system modeling. Part II: geotechnical and earthquake engineering problems. Neural Comput Appl 21:189\u2013202","journal-title":"Neural Comput Appl"},{"issue":"3","key":"1976_CR40","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1061\/(ASCE)MT.1943-5533.0000154","volume":"23","author":"AH Gandomi","year":"2011","unstructured":"Gandomi AH, Alavi AH, Mirzahosseini MR, Moqaddas NF (2011) Nonlinear genetic-based models for prediction of flow number of asphalt mixtures. J Mater Civil Eng ASCE 23(3):248\u2013263","journal-title":"J Mater Civil Eng ASCE"},{"key":"1976_CR41","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1016\/j.jcsr.2011.01.014","volume":"67","author":"AH Gandomi","year":"2011","unstructured":"Gandomi AH, Tabatabaei SM, Moradian MH, Radfar A, Alavi AH (2011) A new prediction model for the load capacity of castellated steel beams. J Construct Steel Res 67:1096\u20131105","journal-title":"J Construct Steel Res"},{"key":"1976_CR42","first-page":"14026","volume":"38","author":"CM Hsu","year":"2011","unstructured":"Hsu CM (2011) A hybrid procedure for stock price prediction by integrating self-organizing map and genetic programming. Expert Syst Appl 38:14026\u201314036","journal-title":"Expert Syst Appl"},{"key":"1976_CR43","first-page":"203","volume":"454\u2013455","author":"CM Hsu","year":"2012","unstructured":"Hsu CM (2012) Flow discharge prediction in compound channels using linear genetic programming. J Hydrol 454\u2013455:203\u2013207","journal-title":"J Hydrol"},{"key":"1976_CR44","first-page":"134","volume":"7","author":"Q Xua","year":"2013","unstructured":"Xua Q, Chen QW, Maa JF, Blanckaerta K (2013) Optimal pipe replacement strategy based on break rate prediction through genetic programming for water distribution network. Cont Shelf Res 7:134\u2013140","journal-title":"Cont Shelf Res"},{"issue":"3","key":"1976_CR45","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1007\/s11242-014-0313-8","volume":"103","author":"A Garg","year":"2014","unstructured":"Garg A, Garg A, Tai K, Barontini S, Stokes A (2014) A computational intelligence-based genetic programming approach for the simulation of soil water retention curve. Transp Porous Media 103(3):497\u2013513","journal-title":"Transp Porous Media"},{"key":"1976_CR46","doi-asserted-by":"crossref","unstructured":"Garg A, Tai K (2013) Genetic programming for modeling vibratory finishing process: role of experimental designs and fitness functions. Swarm, evolutionary and memetic computing. Lecture notes in computer science, vol 8298, pp 23\u201331","DOI":"10.1007\/978-3-319-03756-1_3"},{"key":"1976_CR47","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.simpat.2014.07.008","volume":"48","author":"A Garg","year":"2014","unstructured":"Garg A, Tai K (2014) Combined CI-MD approach in formulation of engineering moduli of single layer graphene sheet. Simul Model Pract Theory 48:93\u2013111","journal-title":"Simul Model Pract Theory"},{"issue":"20","key":"1976_CR48","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.tca.2014.08.029","volume":"594","author":"V Vijayaraghavan","year":"2014","unstructured":"Vijayaraghavan V, Garg A, Wong CH, Tai K, Singrub Pravin M, Liang Gao, Sangwan KS (2014) A molecular dynamics based artificial intelligence approach for characterizing thermal transport in nanoscale material. Thermochim Acta 594(20):39\u201349","journal-title":"Thermochim Acta"},{"key":"1976_CR49","doi-asserted-by":"crossref","first-page":"493","DOI":"10.4028\/www.scientific.net\/AMM.575.493","volume":"575","author":"A Garg","year":"2014","unstructured":"Garg A, Tai K (2014) An ensemble approach of machine learning in evaluation of mechanical property of the rapid prototyping fabricated prototype. Appl Mech Mater 575:493\u2013496","journal-title":"Appl Mech Mater"},{"key":"1976_CR50","doi-asserted-by":"crossref","first-page":"3715","DOI":"10.1016\/j.physa.2013.04.027","volume":"392","author":"GJ Wang","year":"2013","unstructured":"Wang GJ, Xie C, Chen S, Yang JJ, Yang MY (2013) Random matrix theory analysis of cross-correlations in the US stock market: evidence from Pearson\u2019s correlation coefficient and detrended cross-correlation coefficient. Phys A Stat Mech Appl 392:3715\u20133730","journal-title":"Phys A Stat Mech Appl"},{"key":"1976_CR51","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1103\/PhysRevLett.99.204101","volume":"99","author":"S Frenzel","year":"2007","unstructured":"Frenzel S, Pompe B (2007) Partial mutual information for coupling analysis of multivariate time series. Phys Rev Lett 99:1\u20134","journal-title":"Phys Rev Lett"},{"key":"1976_CR52","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1103\/PhysRevE.69.066138","volume":"69","author":"A Kraskov","year":"2004","unstructured":"Kraskov A, Stogbauer H, Grassberger P (2004) Estimating mutual information. Phys Rev E Stat Nonlinear Soft Matter Phys 69:1\u201316","journal-title":"Phys Rev E Stat Nonlinear Soft Matter Phys"},{"key":"1976_CR53","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/0304-4076(92)90104-Y","volume":"54","author":"D Kwiatkowski","year":"1992","unstructured":"Kwiatkowski D, Phillips PCB, Schmidt P, Shin Y (1992) Testing the null hypothesis of stationarity against the alternative of a unit root: how sure are we that economic time series have a unit root ? J Econom 54:159\u2013178","journal-title":"J Econom"},{"issue":"8","key":"1976_CR54","doi-asserted-by":"crossref","first-page":"1957","DOI":"10.1007\/s00477-014-0875-6","volume":"28","author":"C Lian","year":"2014","unstructured":"Lian C, Zeng ZG, Yao W, Tang HM (2014) Extreme learning machine for the displacement prediction of landslide under rainfall and reservoir level. Stoch Enviorn Res Risk Assess 28(8):1957\u20131972","journal-title":"Stoch Enviorn Res Risk Assess"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-015-1976-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-015-1976-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-015-1976-y","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,27]],"date-time":"2019-08-27T19:52:27Z","timestamp":1566935547000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-015-1976-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,6,30]]},"references-count":54,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2016,8]]}},"alternative-id":["1976"],"URL":"https:\/\/doi.org\/10.1007\/s00521-015-1976-y","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015,6,30]]}}}