Implications of uncertainty in inflow forecasting on reservoir operation for irrigation
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ARTICLE
Implications of uncertainty in inflow forecasting on reservoir operation for irrigation K. S. Kasiviswanathan1 · K. P. Sudheer2,3 · Bankaru‑Swamy Soundharajan4 · Adebayo J. Adeloye5 Received: 26 September 2019 / Revised: 10 September 2020 / Accepted: 16 September 2020 © The International Society of Paddy and Water Environment Engineering 2020
Abstract Accurate and reliable forecasting of reservoir inflows is crucial for efficient reservoir operation to decide the quantity of the water to be released for various purposes. In this paper, an artificial neural network (ANN) model has been developed to forecast the weekly reservoir inflows along with its uncertainty, which was quantified through accounting the model’s input and parameter uncertainties. Further, to investigate how the effect of uncertainty is translated in the process of decision making, an integrated simulation–optimization framework that consists of (i) inflow forecasting model; (ii) reservoir operation model; and (iii) crop simulation model was developed to assess the impacts of uncertainty in forecasted inflow on the irrigation scheduling and total crop yield from the irrigation system. A genetic algorithm was used to derive the optimal reservoir releases for irrigation and the area of irrigation. The proposed modeling framework has been demonstrated through a case example, Chittar river basin, India. The upper, lower, and mean of forecasted inflow from the ANN model were used to arrive at the prediction interval of the depth of irrigation, total crop yield, and area of irrigation. From the analysis, the ANN model forecast error of ± 69% to the mean inflow was estimated. However, the error to mean value of simulation for total irrigation, total yield, and area of irrigation was ± 13.3%, ± 6.5%, and ± 4.6%, respectively. The optimizer mainly contributed to the reduction in the errors (i.e., maximizing the total production with the optimal water releases from the reservoir irrespective of inflow to the reservoir). The results from this study suggested that the information on the uncertainty quantification helps in better understanding the reliability of the systems and for effective decision making. Keywords Artificial neural network · Crop simulation · Reservoir operation · Optimization · Uncertainty
Introduction
* K. S. Kasiviswanathan [email protected] 1
Department of Water Resources Development and Management, Indian Institute of Technology Roorkee, Roorkee 247667, India
2
Department of Civil Engineering, Indian Institute of Technology Madras, Chennai 600036, India
3
Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN 47907, USA
4
Department of Civil Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore 641112, India
5
Institute for Infrastructure and Environment, Heriot-Watt University, Edinburgh EH14 4AS, UK
Modeling and simulation are prevalent techniques for the effective management of water and agricultural production in the ir
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