{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T08:55:59Z","timestamp":1773996959326,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2020,1,15]],"date-time":"2020-01-15T00:00:00Z","timestamp":1579046400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Hydropower dam displacement is influenced by various factors (dam ageing, reservoir water level, air, water, and concrete temperature), which cause complex nonlinear behaviour that is difficult to predict. Object deformation monitoring is a task of geodetic and civil engineers who use different instruments and methods for measurements. Only geodetic methods have been used for the object movement analysis in this research. Although the whole object is affected by the influencing factors, different parts of the object react differently. Hence, one model cannot describe behaviour of every part of the object precisely. In this research, a localised approach is presented\u2014two individual models are developed for every point strategically placed on the object: one model for the analysis and prediction in the direction of the X axis and the other for the Y axis. Additionally, the prediction of horizontal dam movement is not performed directly from measured values of influencing factors, but from predicted values obtained by machine learning and statistical methods. The results of this research show that it is possible to perform accurate short-term time series dam movement prediction by using machine learning and statistical methods and that the only limiting factor for improving prediction length is accurate weather forecast.<\/jats:p>","DOI":"10.3390\/ijgi9010047","type":"journal-article","created":{"date-parts":[[2020,1,17]],"date-time":"2020-01-17T04:14:41Z","timestamp":1579234481000},"page":"47","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Multistage Cascade Predictor of Structural Elements Movement in the Deformation Analysis of Large Objects Based on Time Series Influencing Factors"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8841-7676","authenticated-orcid":false,"given":"Adis","family":"Hamzic","sequence":"first","affiliation":[{"name":"Department for technical monitoring of Hydro Power Plants on the Neretva River, Public Enterprise Electric Utility of Bosnia and Herzegovina, Jablanica 88420, Bosnia and Herzegovina"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0933-2699","authenticated-orcid":false,"given":"Zikrija","family":"Avdagic","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering, University of Sarajevo, Sarajevo 71000, Bosnia and Herzegovina"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1632-570X","authenticated-orcid":false,"given":"Ingmar","family":"Besic","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering, University of Sarajevo, Sarajevo 71000, Bosnia and Herzegovina"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,15]]},"reference":[{"key":"ref_1","unstructured":"Lombardi, G. (2004). Structural safety assessment of dams. Advanced Data Interpretation for Diagnosis of Concrete Dams, CISM."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1489","DOI":"10.1080\/19475705.2015.1047902","article-title":"Displacement response of a concrete arch dam to seasonal temperature fluctuations and reservoir level rise during the first filling period: Evidence from geodetic data","volume":"7","author":"Yigit","year":"2015","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"54981","DOI":"10.1109\/ACCESS.2019.2912143","article-title":"Deformation monitoring of reservoir dams using GNSS: An application to south-to-north water diversion project, China","volume":"7","author":"Xiao","year":"2019","journal-title":"IEEE Access"},{"key":"ref_4","first-page":"421","article-title":"Hydraulic models and finite elements for monitoring of an earth dam, by using GNSS techniques","volume":"61","author":"Dardanelli","year":"2017","journal-title":"Period. Polytech.-Civ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"720","DOI":"10.1007\/s11430-010-4101-1","article-title":"Three gorges dam stability monitoring with time-series InSAR image analysis","volume":"54","author":"Wang","year":"2011","journal-title":"Sci. China Earth Sci."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Voege, M., Frauenfelder, R., and Larsen, Y. (2012, January 22\u201327). Displacement monitoring at Svartevatn dam with interferometric SAR. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany.","DOI":"10.1109\/IGARSS.2012.6350561"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Dardanelli, G., La Loggia, G., Perfetti, N., Capodici, F., Puccio, L., and Maltese, A. (2014, January 23\u201325). Monitoring displacements of an earthen dam using GNSS and remote sensing. Proceedings of the Remote Sensing for Agriculture, Ecosystems, and Hydrology, Amsterdam, The Netherlands.","DOI":"10.1117\/12.2071222"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Al-Husseinawi, Y., Li, Z., Clarke, P., and Edwards, S. (2018). Evaluation of the stability of the Darbandikhan Dam after the 12 November 2017 Mw 7.3 Sarpol-e Zahab (Iran\u2013Iraq border) earthquake. Remote Sens., 10.","DOI":"10.3390\/rs10091426"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1495","DOI":"10.1007\/s00521-016-2666-0","article-title":"A novel hybrid artificial intelligent approach based on neural fuzzy inference model and particle swarm optimization for horizontal displacement modeling of hydropower dam","volume":"29","author":"Bui","year":"2018","journal-title":"Neural Comput. Appl."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1080\/10095020.2017.1386848","article-title":"Dam deformation analysis based on BPNN merging models","volume":"21","author":"Zou","year":"2018","journal-title":"Geo-Spat. Inf. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Liu, H.F., Ren, C., Zheng, Z.T., Liang, Y.J., and Lu, X.J. (2018). Study of a gray genetic BP neural network model in fault monitoring and a diagnosis system for dam safety. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7010004"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"e1997","DOI":"10.1002\/stc.1997","article-title":"Concrete dam deformation prediction model for health monitoring based on extreme learning machine","volume":"24","author":"Kang","year":"2017","journal-title":"Struct. Control Health"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1515\/intag-2017-0007","article-title":"Forecasting daily meteorological time series using ARIMA and regression models","volume":"32","author":"Murat","year":"2018","journal-title":"Int. Agrophys."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"604","DOI":"10.1080\/15598608.2017.1292484","article-title":"Seasonal time-series modeling and forecasting of monthly mean temperature for decision making in the Kurdistan Region of Iraq","volume":"11","author":"Chawsheen","year":"2017","journal-title":"J. Stat. Theory Pract."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Rizkina, M.A., Adytia, D., and Subasita, N. (2019, January 24\u201326). Nonlinear autoregressive neural network models for sea level prediction, study case: In Semarang, Indonesia. Proceedings of the 7th International Conference on Information and Communication Technology (ICoICT), Kuala Lumpur, Malaysia.","DOI":"10.1109\/ICoICT.2019.8835307"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Cadenas, E., Rivera, W., Campos-Amezcua, R., and Heard, C. (2016). Wind speed prediction using a univariate ARIMA model and a multivariate NARX model. Energies, 9.","DOI":"10.3390\/en9020109"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Hamzic, A., Avdagic, Z., and Omanovic, S. (2016, January 24\u201326). A sequential approach for short-term water level prediction using nonlinear autoregressive neural networks. Proceedings of the 2016 XI International Symposium on Telecommunications, Sarajevo, Bosnia-Hercegovina.","DOI":"10.1109\/BIHTEL.2016.7775713"},{"key":"ref_18","unstructured":"Frankic, K. (2010). The Least Squares Method in Geodesy, Faculty of Civil Engineering. [1st ed.]."},{"key":"ref_19","unstructured":"Perovic, G. (2007). Precise Geodetic Measurements, Faculty of Civil Engineering. [1st ed.]."},{"key":"ref_20","unstructured":"Bencic, D., and Solaric, N. (2008). Measuring Instruments and Systems in Geodesy and Geoinformatics, \u0160kolska Knjiga. [1st ed.]."},{"key":"ref_21","unstructured":"J\u00e4ger, R., Hoscislawski, A., and Oswald, M. (2009). GNSS\/LPS\/LS based Online Control and Alarm System (GOCA)\u2014Mathematical models and technical realization of a scalable system for natural and geotechnical deformation monitoring and analysis. Geodetic Deformation Monitoring: From Geophysical to Engineering Roles, Springer."},{"key":"ref_22","unstructured":"J\u00e4ger, R., and Gonz\u00e1lez, F. (2006). GNSS\/LPS based Online Control and Alarm System (GOCA)\u2014Mathematical models and technical realization of a system for natural and geotechnical deformation monitoring and hazard prevention. Geodetic Deformation Monitoring: From Geophysical to Engineering Roles, Springer."},{"key":"ref_23","unstructured":"(2020, January 01). EPBIH. Available online: https:\/\/www.epbih.ba\/eng\/page\/hydro-power-plants-on-neretva#hydro-power-plant-jablanica."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1109\/MCI.2009.932254","article-title":"Time series prediction using support vector machines: A survey","volume":"4","author":"Sapankevych","year":"2009","journal-title":"IEEE Comput. Intell. Mag."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Hamzic, A., and Avdagic, Z. (2016, January 9\u201312). Multilevel prediction of missing time series dam displacements data based on artificial neural networks voting evaluation. Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, Budapest, Hungary.","DOI":"10.1109\/SMC.2016.7844597"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1051\/ro\/1977110100031","article-title":"The Box-Jenkins approach to time series analysis","volume":"11","author":"Anderson","year":"1977","journal-title":"RAIRO-Oper. Res."},{"key":"ref_27","unstructured":"Wheelwright, S., Makridakis, S., and Hyndman, R.J. (1998). Forecasting: Methods and Applications, John Wiley & Sons, Inc.. [3rd ed.]."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1093\/biomet\/65.2.297","article-title":"On a measure of lack of fit in time series models","volume":"65","author":"Ljung","year":"1978","journal-title":"Biometrika"},{"key":"ref_29","first-page":"197","article-title":"Forecasting of monthly mean rainfall in Coastal Andhra","volume":"7","author":"Reddy","year":"2017","journal-title":"Int. J. Stat. Appl."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Pereira, F., Bezerra, F., Junior, S., Santos, J., Chabu, I., Souza, G., Micerino, F., and Nabeta, S. (2018). Nonlinear autoregressive neural network models for prediction of transformer oil-dissolved gas concentrations. Energies, 11.","DOI":"10.3390\/en11071691"},{"key":"ref_31","unstructured":"Beale, M.H., Hagan, M.T., and Demuth, H.B. (2015). Neural Network Toolbox User\u2019s Guide, The MathWorks Inc."},{"key":"ref_32","unstructured":"Heaton, J. (2008). Introduction to Neural Networks with Java, Heaton Research, Inc."},{"key":"ref_33","unstructured":"Lu, T., Chen, X., and Zhou, S. (2010, January 11\u201312). Optimization for impact factors of dam deformation based on BP neural network model. Proceedings of the International Conference on Intelligent Computation Technology and Automation, Changsha, China."},{"key":"ref_34","unstructured":"Hyndman, R.J., and Athanasopoulos, G. (2018). Forecasting: Principles and Practice, OTexts. [2nd ed.]."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Yu, H., and Wilamowski, B.M. (2011). Levenberg-marquardt training. Industrial Electronics Handbook, CRC Press.","DOI":"10.1201\/b10604-15"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.csda.2015.12.012","article-title":"Regression correlation coefficient for a Poisson regression model","volume":"98","author":"Takahashi","year":"2016","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1038\/nature14956","article-title":"The quiet revolution of numerical weather prediction","volume":"525","author":"Bauer","year":"2015","journal-title":"Nature"},{"key":"ref_38","unstructured":"(2017, February 13). AccuWeather. Available online: https:\/\/www.accuweather.com\/bs\/ba\/jablanica\/33158\/weather-forecast\/33158."},{"key":"ref_39","unstructured":"(2017, February 13). The Weather Channel. Available online: https:\/\/weather.com\/weather\/today\/l\/BKXX2909:1:BK."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/9\/1\/47\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T14:05:11Z","timestamp":1760364311000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/9\/1\/47"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,15]]},"references-count":39,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2020,1]]}},"alternative-id":["ijgi9010047"],"URL":"https:\/\/doi.org\/10.3390\/ijgi9010047","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,15]]}}}