Titlebook: Computational Intelligence for Water and Environmental Sciences; Omid Bozorg-Haddad,Babak Zolghadr-Asli Book 2022 The Editor(s) (if applic

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Bernd W. Wirtz,Nikolai Lihotzky MBAor forecasting DO several days in advance. First, the correlation between DO data at several times lags were calculated using the autocorrelation function (ACF) and the partial autocorrelation function (PACF). Second, the DO concentration time series were decomposed using the empirical wavelet trans

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Computational Intelligence for Water and Environmental Sciences978-981-19-2519-1Series ISSN 1860-949X Series E-ISSN 1860-9503

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1860-949X en imposed serious challenges to traditional deterministic precise frameworks. The topic caters to postgraduate students and senior researchers who are interested in computational intelligence approach to issues stemming from water and environmental sciences..978-981-19-2521-4978-981-19-2519-1Series ISSN 1860-949X Series E-ISSN 1860-9503

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https://doi.org/10.1007/978-3-642-57332-3 evaluated through 5000 epochs and the best values of NSE (0.91), ESP (0) and RTS (0) were obtained for the simulation based on the third storm event (05/10/2005). The non-dominated solutions, extracted from the AMALGAM approach were plotted in a three-dimensional space to form the Pareto front. Acc

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Asuman Koc Yurtkur,Tezcan Abasızff and semi-treated sanitary/industrial sewage discharge. Therefore, artificial intelligence (AI) techniques are used to decrease model development costs and improve prediction errors, achieving more efficient models. In this chapter, some well-known techniques and AI-based methods are introduced, a

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Strategic Thinking and Risk Attitudeal classification in advanced ways. After classifying the data, DT curiously searches for the rules governing the data decision space. It should be noted, however, that DT can be used for both classification and regression problems, but is often used more in classification problems. It should be not

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Wissensorientiertes Performance Measurementn appropriate model, particularly in modeling large (big) and complex datasets. This chapter provides a review of deep learning concepts, introduce some of the developed deep learning structures, and their application in water and environmental studies.