Reservoir water release dynamic decision model based on spatial temporal pattern
The multi-purpose reservoir water release decision requires an expert to make a decision by assembling complex decision information that occurred in real time. The decision needs to consider adequate reservoir water balance in order to maintain reservoir multi-purpose function and provide enough sp...
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Wan Ishak, Wan Hussain Md Norwawi, Norita |
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QA76.76 Fuzzy System. Suriyati, Abdul Mokhtar Reservoir water release dynamic decision model based on spatial temporal pattern |
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The multi-purpose reservoir water release decision requires an expert to make a decision by assembling complex decision information that occurred in real time. The decision needs to consider adequate reservoir water balance in order to maintain
reservoir multi-purpose function and provide enough space for incoming heavy rainfall and inflow. Crucially, the water release should not exceed the downstream maximum river level so that it will not cause flood. The rainfall and water level are
fuzzy information, thus the decision model needs the ability to handle the fuzzy information. Moreover, the rainfalls that are recorded at different location take different time to reach into the reservoir. This situation shows that there is spatial temporal relationship hidden in between each gauging station and the reservoir. Thus, this study proposed dynamic reservoir water release decision model that utilize both spatial and temporal information in the input pattern. Based on the patterns, the model will suggest when the reservoir water should be released. The model adopts Adaptive Neuro-Fuzzy Inference System (ANFIS) in order to deal with the fuzzy information. The data used in this study was obtained from the Perlis Department of Irrigation and Drainage. The modified Sliding Window algorithm was used to construct the rainfall temporal pattern, while the spatial information was established by simulating the mapped rainfall and reservoir water level pattern. The model
performance was measured based on the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Findings from this study shows that ANFIS produces the lowest RMSE and MAE when compare to Autoregressive Integrated Moving Average (ARIMA) and Backpropagation Neural Network (BPNN) model. The model can be used by the reservoir operator to assist their decision making and support the new reservoir operator in the absence of an experience reservoir operator. |
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Suriyati, Abdul Mokhtar |
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Suriyati, Abdul Mokhtar |
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Suriyati, Abdul Mokhtar |
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Reservoir water release dynamic decision model based on spatial temporal pattern |
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Reservoir water release dynamic decision model based on spatial temporal pattern |
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Reservoir water release dynamic decision model based on spatial temporal pattern |
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Reservoir water release dynamic decision model based on spatial temporal pattern |
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Reservoir water release dynamic decision model based on spatial temporal pattern |
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reservoir water release dynamic decision model based on spatial temporal pattern |
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Universiti Utara Malaysia |
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Awang Had Salleh Graduate School of Arts & Sciences |
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2016 |
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my-uum-etd.60382023-03-09T03:07:07Z Reservoir water release dynamic decision model based on spatial temporal pattern 2016 Suriyati, Abdul Mokhtar Wan Ishak, Wan Hussain Md Norwawi, Norita Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts and Sciences QA76.76 Fuzzy System. The multi-purpose reservoir water release decision requires an expert to make a decision by assembling complex decision information that occurred in real time. The decision needs to consider adequate reservoir water balance in order to maintain reservoir multi-purpose function and provide enough space for incoming heavy rainfall and inflow. Crucially, the water release should not exceed the downstream maximum river level so that it will not cause flood. The rainfall and water level are fuzzy information, thus the decision model needs the ability to handle the fuzzy information. Moreover, the rainfalls that are recorded at different location take different time to reach into the reservoir. This situation shows that there is spatial temporal relationship hidden in between each gauging station and the reservoir. Thus, this study proposed dynamic reservoir water release decision model that utilize both spatial and temporal information in the input pattern. Based on the patterns, the model will suggest when the reservoir water should be released. The model adopts Adaptive Neuro-Fuzzy Inference System (ANFIS) in order to deal with the fuzzy information. The data used in this study was obtained from the Perlis Department of Irrigation and Drainage. The modified Sliding Window algorithm was used to construct the rainfall temporal pattern, while the spatial information was established by simulating the mapped rainfall and reservoir water level pattern. The model performance was measured based on the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Findings from this study shows that ANFIS produces the lowest RMSE and MAE when compare to Autoregressive Integrated Moving Average (ARIMA) and Backpropagation Neural Network (BPNN) model. The model can be used by the reservoir operator to assist their decision making and support the new reservoir operator in the absence of an experience reservoir operator. 2016 Thesis https://etd.uum.edu.my/6038/ https://etd.uum.edu.my/6038/2/s813589_01.pdf text eng public https://etd.uum.edu.my/6038/3/s813589_02.pdf text eng public masters masters Universiti Utara Malaysia Ahn, H., & Kim, K. (2009). Bankruptcy prediction modeling with hybrid case-based reasoning and genetic algorithms approach. Applied Soft Computing, 9(2), 599–607. doi:10.1016/j.asoc.2008.08.002 Ahmed, J. A., & Sarma, A. K. (2005).Genetic Algorithm for Optimal Operating Policy of a Multipurpose Reservoir.Water Resources Management, 19 (2), 145–161. doi:10.1007/s11269-005-2704-7 Azuma, R., Daily, M., & Furmanski, C. (2006). 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