Forecasting of Lake Level by Soft Computing Approaches

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dc.contributor.authorDemir, Vahdettin
dc.contributor.authorTamer, Mehmet Ali
dc.contributor.authorÇarbaş, Serdar
dc.date.accessioned2025-01-12T17:08:42Z
dc.date.available2025-01-12T17:08:42Z
dc.date.issued2024
dc.departmentKMÜ, Mühendislik Fakültesi, İnşaat Mühendisliği Bölümü
dc.description.abstractTo ensure the sustainability and management of water resources, regularly monitoring the water levels in lakes, rivers, basins, dam reservoirs, etc. is a very important engineering task. Our freshwater resources are gradually decreasing due to the destruction of freshwater resources and climate change. For this reason, monitoring, modelling, and researching of freshwater resources, especially lakes, are increasingly important issue for nowadays. In this chapter, soft computing approaches are used to forecast of lake water levels at Beyşehir Lake, located in the central part of Turkey. To do this, three artificial neural network algorithms (Multilayer, Radial Basis and Generalized Regression), two heuristic algorithms (Model 5-Tree and Multivariate Adaptive Regression Spline), and a Support Vector Machines containing three different functions (Radial, Polynomial, and Linear) are used. In addition to being models used successfully in hydrological modelling of civil engineering, the changes in modelling performance with the number of iterations, kernel functions, optimization algorithms, and data input sets that constitute the internal dynamite of the techniques are investigated. The attained results show that through these multiple parameters, radial basis artificial neural networks are the most successful when compared with mean absolute error, root mean square error, coefficient of determination, Taylor diagrams and Violin plots. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
dc.identifier.citationDemir, V., Tamer, M. A., & Carbas, S. (2024). Forecasting of Lake Level by Soft Computing Approaches. In New Advances in Soft Computing in Civil Engineering (pp. 119–148). https://doi.org/10.1007/978-3-031-65976-8_6
dc.identifier.doi10.1007/978-3-031-65976-8_6
dc.identifier.endpage148
dc.identifier.issn2198-4182
dc.identifier.scopus2-s2.0-85201937836
dc.identifier.scopusqualityQ2
dc.identifier.startpage119
dc.identifier.urihttps://hdl.handle.net/11492/8978
dc.identifier.volume547
dc.indekslendigikaynakScopus
dc.institutionauthorÇarbaş, Serdar
dc.institutionauthoridÇarbaş, Serdar/0000-0002-3612-0640
dc.language.isoen
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.relation.ispartofStudies in Systems, Decision and Control
dc.relation.publicationcategoryKitap Bölümü - Uluslararası
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectArtificial Neural Network
dc.subjectHeuristic Algorithms
dc.subjectLake-Level Forecasting
dc.subjectSupport Vector Machines
dc.titleForecasting of Lake Level by Soft Computing Approaches
dc.typeBook Chapter

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