Journal ArticleDOI
Data-driven stochastic modeling for multi-purpose reservoir simulation
Mosaad Khadr,Andreas Schlenkhoff +1 more
TLDR
In this paper, an adaptive neuro-fuzzy inference system, Thomas-Fiering model and hidden Markov model were integrated in a simulation model for simulation of reservoir operation.Abstract:
This paper presents an integration of data-driven modeling and stochastic models for simulation of reservoir operation. The simulation model developed in this study was applied to the Ruhr river reservoirs system in Germany. An adaptive neuro-fuzzy inference system, Thomas–Fiering model and hidden Markov model were integrated in a simulation model. The set of model input included the time of the year, reservoir storage, inflow and Standardized Precipitation Index; and the target output was the reservoir release. Predicted and observed release values were evaluated using several common evaluation criteria. Results of model performance showed that the proposed model is capable of simulating reservoir operation and provides reliable reservoir release prediction. Results showed also that the proposed approach could be a good tool at the real-time operation stage to quickly check operational alternatives due to emergency events or planning and real-time incongruence.read more
Citations
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Journal ArticleDOI
A Comparative Analysis of Hidden Markov Model, Hybrid Support Vector Machines, and Hybrid Artificial Neural Fuzzy Inference System in Reservoir Inflow Forecasting (Case Study: The King Fahd Dam, Saudi Arabia)
TL;DR: The performance evaluation results for the developed models showed that the GA-induced improvement in the ANFIS and SVR forecasts was matched by an approximately 25% decrease in RMSE and around a 13% increase in Nash–Sutcliffe efficiency.
Journal ArticleDOI
Data-driven cloud simulation architecture for automated flexible production lines: application in real smart factories
TL;DR: In recent years, more manufacturing enterprises are building automated flexible production lines (AFPLs) to satisfy the dynamic and diversified demand.
Journal ArticleDOI
Machine learning approach to handle data‐driven model for simulation and forecasting of the cone crusher output in the stone crushing plant
TL;DR: This research investigated the ability of the adaptive neuro fuzzy inference system (ANFIS) to simulate the effects of throw, eccentric speed, closed side setting, and the size of the particle on crusher output and resolved that the model fostered was a suitable instrument for the onsite cone crusher assessment.
Journal ArticleDOI
ANNs and inflow forecast to aid stochastic optimization of reservoir operation
TL;DR: In this paper, the authors used nonlinear regression to correlate release as a function of initial storage plus inflow forecasted for the month, and showed that the nonlinear regressions can be used to find the optimal solution.
Book ChapterDOI
Modeling of Water Quality Parameters in Manzala Lake Using Adaptive Neuro-Fuzzy Inference System and Stochastic Models
TL;DR: In this article, an adaptive neuro-fuzzy inference system (ANFIS) was used to predict water quality parameters in Manzala Lake based on water quality parameter of drains associated with the Lake.
References
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Markov Chain Models - Rarity and Exponentiality
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Journal ArticleDOI
Artificial neural networks as rainfall-runoff models
A. W. Minns,M. J. Hall +1 more
TL;DR: In this paper, a series of numerical experiments, in which flow data were generated from synthetic storm sequences routed through a conceptual hydrological model consisting of a single nonlinear reservoir, has demonstrated the closeness of fit that can be achieved to such data sets using ANNs.
Journal ArticleDOI
An artificial neural network approach to rainfall-runoff modelling
TL;DR: The ability of the ANN to cope with missing data and to “learn” from the event currently being forecast in real time makes it an appealing alternative to conventional lumped or semi-distributed flood forecasting models.