River flow estimation from upstream flow records by artificial intelligence methods

Turan, Mustafa Erkan; Yurdusev, Mehmet Ali

doi:10.1016/j.jhydrol.2009.02.004

Cited by 67 publications

(22 citation statements)

References 20 publications

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“…The application of artificial intelligence approaches, such as artificial neural networks (ANNs), adaptive neuro‐fuzzy inference system (ANFIS), gene expression programming (GEP) and support vector machines (SVM), have received much attention in the last decades (Aytek and Alp, ; Turan and Yurdusev, ; Kisi et al , ; Sanikhani et al , ; Li et al , ; Mehr et al , ). The comprehensive review of such applications is beyond the scope of this paper, and only some related studies will be given here.…”

Section: Introductionmentioning

confidence: 99%

Modelling long‐term monthly temperatures by several data‐driven methods using geographical inputs

Kisi

Hadi

2015

Intl Journal of Climatology

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This paper investigates the ability of five different data‐driven methods, artificial neural networks (ANNs), adaptive neuro‐fuzzy inference system (ANFIS) with grid partition (GP), ANFIS with subtractive clustering (SC), support vector regression (SVR) and gene expression programming (GEP), in predicting long‐term monthly temperatures by using data from 50 stations in Iran. The periodicity component (month of the year), station latitude, longitude and altitude values were used as inputs to the applied models to predict the long‐term monthly temperatures. The overall accuracy of the SVR model was found to be better than that of the other models. The GEP model gave the worst estimates. The maximum determination coefficient (R2) values were found to be 0.996, 0.999, 0.997 and 0.959 for the ANN, ANFIS‐GP, ANFIS‐SC and GEP models in Karaj and Qazvin stations, respectively. The highest R2 value (0.999) of SVR model was found for the Tabas station. The minimum R2 values were respectively found as 0.988, 0.946 and 0.985 for the ANFIS‐GP, ANFIS‐SC and SVR models in Bandar Abbas station while the ANN and GEP models gave the minimum R2 values of 0.982 and 0.886 in the Abadan and Kerman stations, respectively. The results indicated that the long‐term monthly temperatures of any site can be successfully estimated by data‐driven methods applied in this study using geographical inputs. The interpolated maps of temperatures were also obtained by using the optimal SVR model and evaluated in the study. The temperature maps showed that the highest temperatures were occurred in the southeastern and central parts of the Iran.

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Section: Introductionmentioning

confidence: 99%

Modelling long‐term monthly temperatures by several data‐driven methods using geographical inputs

Kisi

Hadi

2015

Intl Journal of Climatology

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“…However, MLR methods have some disadvantages such as not fitting the observed data very well or diverting from tails in skewed data (Haghighatjou et al, 2008). Moreover, due to the advancement of computer science, many artificial intelligence and machine learning approaches have been developed for hydrological predictions, such as generalized regression neural network (GRNN) (Cigizoglu, 2005), feed forward back propagation neural networks (Turan and Yurdusev, 2009;Wang et al, 2009), support vector machine (Sujay Raghavendra and Deka, 2014), and least squares support vector machines (Okkan and Serbes, 2012). The AI-based models confront some drawbacks, such as possibility of getting trapped in local minima, over training, subjectivity in the determining of model parameters, initialization of the weights in each simulation randomly, and the components of its complex structure (Okkan and Serbes, 2012).…”

Section: Methods For Hydrological Predictionmentioning

confidence: 99%

Uncertainty Quantification in Hydrologic Predictions: A Brief Review

Fan¹

2019

J ENVIRON INFORM LET

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This study provides a brief review for uncertainty quantification in hydrological predictions. The major approaches for hydrologic predictions are firstly introduced, including the widely used data-driven and process-based modelling approaches. The major uncertainties resulting from inputs, model structures, parameters and outputs are then briefly illustrated. The major review is then conducted for various uncertainty quantification approaches. In detail, the approaches for quantifying uncertainties in model parameters, structures and states are mainly reviewed, such as the Markov chain Monte Carlo, sequential data assimilation and model average approaches. Potential issues to be addressed in future are then concluded, summarizing some unclear issues which may be further investigated in further studies.

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“…Actually, in the literature relating to the application of the neural networks in forecasting models (Chtioui, Panigrahi & Francl, 1999;Cigizoglu & Alp, 2006;Firat & Gungor, 2009;Turan & Yurdusev, 2009), only two architectural formats are considered to be appropriate for the development of a forecasting model. One is the multilayer perceptron (MLP) format, which is a time-lag feed-forward neural network (TLFN), and the other is the generalized regression neural network…”

Section: (3)mentioning

confidence: 99%

A production throughput forecasting system in an automated hard disk drive test operation using GRNN

Samattapapong

Afzulpurkar

2016

JIEM

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Abstract:Purpose: The goal of this paper is to develop a pragmatic system of a production throughput forecasting system for an automated test operation in a hard drive manufacturing plant. The accurate forecasting result is necessary for the management team to response to any changes in the production processes and the resources allocations.Design/methodology/approach: In this study, we design a production throughput forecasting system in an automated test operation in hard drive manufacturing plant. The proposed system consists of three main stages. In the first stage, a mutual information method was adopted for selecting the relevant inputs into the forecasting model. In the second stage, a generalized regression neural network (GRNN) was implemented in the forecasting model development phase. Finally, forecasting accuracy was improved by searching the optimal smoothing parameter which selected from comparisons result among three optimization algorithms: particle swarm optimization (PSO), unrestricted search optimization (USO) and interval halving optimization (IHO).Findings: The experimental result shows that (1) the developed production throughput forecasting system using GRNN is able to provide forecasted results close to actual values, and to projected the future trends of production throughput in an automated hard disk drive test -330-Journal of Industrial Engineering and Management -http://dx.doi.org/10.3926/jiem.1464 operation; (2) IHO algorithm performed as appropriate optimization method better than the other two algorithms. (3) Compared with current forecasting system in manufacturing, the results show that the proposed system's performance is superior to the current system in prediction accuracy and suitable for real-world application. Originality/value:The production throughput volume is a key performance index of hard disk drive manufacturing systems that need to be forecast. The production throughput forecasting result is useful information for management team to respond to any changes in production processes and resources allocation. However, a practical forecasting system for production throughput has not been described in detail yet. The experiments were conducted on a real data set from the final testing operation of hard disk drive manufacturing factory by using Visual Basic Application on Microsoft Excel© to develop preliminary forecasting system for testing and verification process. The experimental result shows that the proposed model is superior to the performance of the current forecasting system.

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River flow estimation from upstream flow records by artificial intelligence methods

Cited by 67 publications

References 20 publications

Modelling long‐term monthly temperatures by several data‐driven methods using geographical inputs

Modelling long‐term monthly temperatures by several data‐driven methods using geographical inputs

Uncertainty Quantification in Hydrologic Predictions: A Brief Review

A production throughput forecasting system in an automated hard disk drive test operation using GRNN

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