Comparison of Two Data-Driven Streamflow Forecast Approaches in an Adaptive Optimal Reservoir Operation Model

9 pages•Published: September 20, 2018

Kayhan Gavahi, S. Jamshid Mousavi and Kumaraswamy Ponnambalam

Abstract

This study investigates the effect of two data-driven inflow prediction methods on the performance of a proposed adaptive real-time optimum reservoir operation model. The model consists of three modules; a forecasting module, which predicts the monthly future inflows, a reservoir operation optimization module, determining monthly optimum reservoir releases up to the end of a year, and an updating module, updating the current state of the system and provides the other two modules with the latest observed information on future inflows. K-nearest neighbor (KNN) and adaptive neuro- fuzzy inference system (ANFIS) approaches are used to forecast monthly inflows to the reservoir. The results demonstrate that ANFIS outperforms the KNN approach by 25, 23, 27 and 10 percent with respect to RMSE, PWRMSE, NSCE and correlation coefficient indices, respectively. However, the objective function values of the reservoir operation optimization model associated with each of those forecast models reveal that ANFIS-based adaptive reservoir operation model is only 5% better than the KNN-based model. This observation highlights the significance role of adaptation and updating procedure in the reduction of streamflow forecast errors.

Keyphrases: adaptive reservoir operation, anfis, inflow forecasting, knn

In: Goffredo La Loggia, Gabriele Freni, Valeria Puleo and Mauro De Marchis (editors). HIC 2018. 13th International Conference on Hydroinformatics, vol 3, pages 755-763.

Links:	https://easychair.org/publications/paper/KSHG
	https://doi.org/10.29007/wrn8

BibTeX entry

@inproceedings{HIC2018:Comparison_Two_Data_Driven,
  author    = {Kayhan Gavahi and S. Jamshid Mousavi and Kumaraswamy Ponnambalam},
  title     = {Comparison of Two Data-Driven Streamflow Forecast Approaches in an Adaptive Optimal Reservoir Operation Model},
  booktitle = {HIC 2018. 13th International Conference on Hydroinformatics},
  editor    = {Goffredo La Loggia and Gabriele Freni and Valeria Puleo and Mauro De Marchis},
  series    = {EPiC Series in Engineering},
  volume    = {3},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2516-2330},
  url       = {/publications/paper/KSHG},
  doi       = {10.29007/wrn8},
  pages     = {755-763},
  year      = {2018}}

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