Abstract
The persistent problem in reservoir operation is that the derived optimal releases fail to incorporate the decision maker or reservoir operators’ knowledge into reservoir operation models. The reservoir operators’ knowledge is specific to that particular reservoir and incorporating such an experienced knowledge will help to derive field reality based operation rules. The available historical reservoir operation databases are the representative samples of reservoir operators’ knowledge or experience. Thus, an attempt has been made that deals with the development of a methodological framework to recover or explore the historical reservoir operation database to derive the reservoir operators’ knowledge as operational rules. The developed methodological framework utilizes the strength and capability of recently developed predictive datamining algorithms to recover the knowledge from large historical database. Predictive data-mining algorithms such as a) classifier: Artificial Neural Network (ANN), and b) regression: Support Vector Regression (SVR) have been used for single reservoir operation data-mining (SROD) modelling framework to explore the temporal dependence between different variables of reservoir operation. The rules of operation or knowledge learned from the training database have been used as guiding rules for predicting the future reservoir operators’ decision on operating the reservoir for the given condition on the inflow, initial storage, and demand requirements. The developed SROD model was found to be efficient in exploring the hidden relationships that exist in a single reservoir system.
Similar content being viewed by others
References
Basak D, Pal S, Patranabis DC (2007) Support vector regression. Neural Information Processing-Letters and Reviews 11(10):203–224
Bhatikar SR, DeGroff C, Mahajan RL (2005) “A classifier based on the artificial neural network approach for cardiologic auscultation in pediatrics.” Artif Intell Med 33(3):251–260
Chandramouli V, Deka P (2005) Network based decision support model for optimal reservoir operation. Water Resour Manag 19(4):447–464
Chaves P, Chang FJ (2008) Intelligent reservoir operation system based on evolving artificial neural networks. Adv Water Resour 31:89–98
Chaves P, Kojiri T (2007) Deriving reservoir operational strategies considering water quantity and quality objectives by stochastic fuzzy neural networks. Adv Water Resour 30(5):1329–1341
Cios KJ, Pedrycz W, Swiniarski RW, Kurgan LA (2010) Data Mining-A Knowledge Discovery Approach. Springer
Dawson CW, Wilby R (1998) An artificial neural network approach to rainfall-runoff modelling. Hydrol Sci J 43(1):47–66
Dunham MH (2003) Data mining introductory and advanced topics. Prentice Hall/Pearson Education, Upper Saddle River, N.J.
Fayyad U, Shapiro GP, Smyth P (1996) From data mining to knowledge discovery in databases. Artif Intell 17:37–53
Goswami S, Chakrabarti A (2014) “Feature selection: A practitioner view.” Int J Inf Technol Comput Sci 11:66–77
Gupta SK (1998) Peak decomposition using Pearson Type VII function. J Appl Crystallogr 31(1):474–476
Hall MM, Veeraraghavan VG, Rubin H, Winchell PG (1977) The approximation of symmetric X-ray peaks by Pearson type VII distributions. J Appl Crystallogr 10:66–68
Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software: an update. ACM SIGKDD Explorations Newsletter 11(1):10–18
Haykin S (1999) Neural Networks. Prentice-Hall, New Jersey, NY
Jain, S., Das, A., and Srivastava, D. (1999). "Application of ANN for Reservoir Inflow Prediction and Operation " J Water Resour Plan Manag, 125(5), 263–271.
Keskin M, Terzi O (2006) Artificial Neural Network Models of Daily Pan Evaporation. J Hydrol Eng 11(1):65–70
Kuo JT, Wang YY, Lung WS (2006) A hybrid neural genetic algorithm for reservoir water quality management. Water Res 40(7):1367–1376
Labadie JW (2004) Optimal operation of multi-reservoir systems: state-of-the-art review. J Water Resour Plan Manag 130(2):93–111
Liu JNK, Li BNL, Dillon TS (2001) An imporved naive Bayesian classifier technique coupled with a novel input solution method [rainfall prediction]. IEEE Transistion system Man Cybern Part C Applied research 31(2):249–256
Malekmohamadi I, Bazargan-Lari MR, Kerachian R, Nikoo MR, Fallahnia M (2011) Evaluating the efficacy of SVMs, BNs, ANNs and ANFIS in wave height prediction. Ocean Eng 38(2):487–497
Mohan, S., and Ramsundram, N. (2013). "Data Mining Models for Water Resource Applications " ISH Journal of Hydraulic Engineering.
Nourani V, Komasi M, Mano A (2009) “A multivariate ANN-wavelet approach for rainfall–runoff modeling.” Water Resour Manag 23(14):2877–2894
Parasuraman K, Elshorbagy A (2007) Cluster based hydrologic prediction using genetic algorithm-trained neural network. J Hydraul Div 12(1):52–62
Pearson K (1895) Contributions to mathematical theory of evolution: II. Skew variation in homogeneous material. Philos Trans R Soc Lond A 186:343–414
Sattari MT, Apaydin H, Ozturk F, Baykal N (2012) Application of a data mining approach to derive operating rules for the Eleviyan irrigation reservoir. Lake and Reservoir Management 28(2):142–152
Shirsath P, Singh A (2010) A Comparative Study of Daily Pan Evaporation Estimation Using ANN, Regression and Climate Based Models. Water Resour Manag 248(8):1571–1581
Sudha, V., Ambujam, N., and Venugopal, K. (2006). A data mining approach for deriving irrigation reservoir operating rules." Water Observation and Information System for Decision Support, Macedonia.
Sudheer KP, Gosain AK, Ramasastri KS (2002) A data-driven algorithm for constructing artificial neural network rainfall-runoff models. Hydrol Process 16:1325–1330
Üstün B, Melssen WJ, Buydens LM (2006) Facilitating the application of Support Vector Regression by using a universal Pearson VII function based kernel. Chemom Intell Lab Syst 81(1):29–40
Velickov S, Solomatine D (2000) Predictive data mining: Practical examples. Artificial Intelligence in Civil Engineering, Germany
Wang XL, Yin ZJ (2007) Artificial Immune Recognition System as a New Classifier for Reservoir Operating Rules Extraction. Intelligent Information Hiding and Multimedia Signal Processes:149–153
Wang XL, Cheng JH, Yin ZJ, Guo MJ (2011) A New Approach of Obtaining Reservoir Operation Rules: Artificial Immune Recognition System. Expert Systems with Application 38:11701–11707
Wei CC, Hsu NS (2008) Derived operating rules for a reservoir operating system: Comparision of decision trees, Neural decision trees and fuzzy decision trees. Water Resour Res 44(2)
Witten IH, Frank E (2005) Data-mining : practical machine learning tools and techniques. Morgan Kaufmann Publishers
Wurbs RA (1993) Reservoir system simulation and optimization models. J Water Resour Plan Manag 119(4):455–472
Yeh WWG (1985) Reservoir management and operation models: A state-of -the-Art Review. Water Resour Res 21(12):1797–1818
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Mohan, S., Ramsundram, N. Predictive Temporal Data-Mining Approach for Evolving Knowledge Based Reservoir Operation Rules. Water Resour Manage 30, 3315–3330 (2016). https://doi.org/10.1007/s11269-016-1351-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11269-016-1351-5