Sediment load prediction by genetic algorithms

Altunkaynak, Abdüsselam

doi:10.1016/j.advengsoft.2008.12.009

Cited by 62 publications

(25 citation statements)

References 14 publications

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“…Recently, optimal control of sediment has become a key issue in watershed management. Accordingly, some studies have investigated the use of GAs as an optimization tool [18][19][20]. At present, water quality issues (from the environmental perspective) are some of the most pressing optimization problems that can be solved by meta-heuristic algorithms.…”

Section: Meta-heuristic Algorithms and Their Applications In Hydrologmentioning

confidence: 99%

Meta-heuristic algorithms as tools for hydrological science

Yoo

Kim

2014

Geoscience Letters

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In this paper, meta-heuristic optimization techniques are introduced and their applications to water resources engineering, particularly in hydrological science are introduced. In recent years, meta-heuristic optimization techniques have been introduced that can overcome the problems inherent in iterative simulations. These methods are able to find good solutions and require limited computation time and memory use without requiring complex derivatives. Simulation-based meta-heuristic methods such as Genetic algorithms (GAs) and Harmony Search (HS) have powerful searching abilities, which can occasionally overcome the several drawbacks of traditional mathematical methods. For example, HS algorithms can be conceptualized from a musical performance process and used to achieve better harmony; such optimization algorithms seek a near global optimum determined by the value of an objective function, providing a more robust determination of musical performance than can be achieved through typical aesthetic estimation. In this paper, meta-heuristic algorithms and their applications (focus on GAs and HS) in hydrological science are discussed by subject, including a review of existing literature in the field. Then, recent trends in optimization are presented and a relatively new technique such as Smallest Small World Cellular Harmony Search (SSWCHS) is briefly introduced, with a summary of promising results obtained in previous studies. As a result, previous studies have demonstrated that meta-heuristic algorithms are effective tools for the development of hydrological models and the management of water resources.

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Section: Meta-heuristic Algorithms and Their Applications In Hydrologmentioning

confidence: 99%

Meta-heuristic algorithms as tools for hydrological science

Yoo

Kim

2014

Geoscience Letters

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“…The following are the main advantages of the MP approach over standard linear regression: (1) MP modeling does not include restrictive assumptions such as linearity, normality, homoscedasticity, etc. [ Sen et al , 2003; Altunkaynak , 2009]; (2) the method can achieve very complex computations easily with the network of neurons; (3) MP modeling can generate multiple outputs once the training phase is complete; and (4) they have an ability to nonlinearly link input and output variables.…”

Section: Multilayer Perceptron (Mp) Modelmentioning

confidence: 99%

A predictive model for reach morphology classification in mountain streams using multilayer perceptron methods

Altunkaynak

Strom

2009

Water Resources Research

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[1] This study uses multilayer perceptron (MP) methods to develop classification models for predicting cascade, step-pool, plane bed, and pool-riffle type reach morphologies in mountain streams. Several models were developed with MP and classical linear regression methods on the basis of the following input variables: channel slope (S), sediment size (d 84 ), bankfull depth (h), and bankfull width (w). Data for model calibration and testing were compiled from previous studies in mountain environments. The data were divided into separate calibration (training) and testing (prediction) sets for both the MP and classical linear regression methods; model performance was based on the percentage of accurately predicted reach morphologies using the testing portion of the data. The results indicate that (1) the MP models outperformed the linear regression models for reach morphology classification; (2) relative submergence (h/d 84 ) was useful for classifying step-pool and pool-riffle reaches but performed poorly in discriminating cascade and plane bed type reaches; (3) inclusion of channel slope in models was important for classifying cascade type reaches; and (4) plane bed reaches were the most difficult to classify and delineate from pool-riffle reaches. The two best performing MP models included the input variables (S, h/d 84 ) and (S, h/d 84 , w). The overall predictive accuracy for classification of reach type for the two models was 81% and 83%, respectively, with predictive accuracies by reach type as follows: cascade, 100%; step-pool, 81%; plane bed, 67%; pool-riffle, 88% (first model) and cascade, 100%; step-pool, 87%; plane bed, 70%; pool-riffle, 90% (second model).Citation: Altunkaynak, A., and K. B. Strom (2009), A predictive model for reach morphology classification in mountain streams using multilayer perceptron methods, Water Resour. Res., 45, W12501,

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“…While useful, these regression approaches have some restrictive assumptions, and require preprocess of the data to put it in proper form for the analysis to keep application results from leading to erroneous conclusions. The restrictive assumptions inherent in linear regression are listed as follows (Sen et al, 2003;Uyumaz et al, 2006;Altunkaynak, 2009 With a fuzzy logic approach, one can avoid these restrictive assumption mentioned above. Aquifer and well parameters are important to operation of groundwater resources.…”

Section: Introductionmentioning

confidence: 99%

A predictive model for well loss using fuzzy logic approach

Altunkaynak

2010

Hydrological Processes

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Abstract:Simple methods for calculating well losses are important for well design and optimization of groundwater source operation. Well losses arise from both laminar flow within the aquifer and turbulent flow within the well, and are often ignored in theoretical aquifer test analysis. The Jacob (1947) and Rorabaugh (1953) techniques for predicting well losses are widely used in the literature; however, inherent in these techniques are the assumptions of linearity, normality and homoscedascity. In the Rorabaugh technique, prior knowledge, or prediction of, the parameters A, C and n is required for calculation of well losses. Unfortunately, as of yet, no method for adequately obtaining these parameters without experimental data and linear regression exist. For these reasons, the Rorabaugh methodology has some practical and realistic limitations. In this paper, a fuzzy logic approach is employed in the calculation of well losses. An advantage of the fuzzy logic approach is that it does not make any assumptions about the form of the well loss functionality and does not require initial estimates for the calculation of well losses. Results show that the fuzzy model is a practical alternative to the Rorabaugh technique, producing lower errors (mean absolute error, mean square error and root mean square error) relative to observed data, for the case presented, comparatively to the Rorabaugh model.

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Sediment load prediction by genetic algorithms

Cited by 62 publications

References 14 publications

Meta-heuristic algorithms as tools for hydrological science

Meta-heuristic algorithms as tools for hydrological science

A predictive model for reach morphology classification in mountain streams using multilayer perceptron methods

A predictive model for well loss using fuzzy logic approach

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