Improved Forecasting of Extreme Monthly Reservoir Inflow Using an Analogue-Based Forecasting Method: A Case Study of the Sirikit Dam in Thailand

Reservoir inflow forecasting is crucial for appropriate reservoir management, especially in the flood season. Forecasting for this season must be sufficiently accurate and timely to allow dam managers to release water gradually for flood control in downstream areas. Recently, several models and methodologies have been developed and applied for inflow forecasting, with good results. Nevertheless, most were reported to have weaknesses in capturing the peak flow, especially rare extreme flows. In this study, an analogue-based forecasting method, designated the variation analogue method (VAM), was developed to overcome this weakness. This method, the wavelet artificial neural network (WANN) model, and the weighted mean analogue method (WMAM) were used to forecast the monthly reservoir inflow of the Sirikit Dam, located in the Nan River Basin, one of the eight sub-basins of the Chao Phraya River Basin in Thailand. It is one of four major dams in the Chao Phraya Basin, with a maximum storage of 10.64 km3, which supplies water to 22 provinces in this basin, covering an irrigation area of 1,513,465 hectares. Due to the huge extreme monthly inflow in August, with inflow of more than 3 km3 in 1985 and 2011, monthly or longer lead time inflow forecasting is needed for proper water and flood control management of this dam. The results of forecasting indicate that the WANN model provided good forecasting for whole-year forecasting including both low-flow and high-flow patterns, while the WMAM model provided only satisfactory results. The VAM showed the best forecasting performance and captured the extreme inflow of the Sirikit Dam well. For the high-flow period (July–September), the WANN model provided only satisfactory results, while those of the WMAM were markedly poorer than for the whole year. The VAM showed the best capture of flow in this period, especially for extreme flow conditions that the WANN and WMAM models could not capture.

[1]  Paulin Coulibaly,et al.  Seasonal reservoir inflow forecasting with low-frequency climatic indices: a comparison of data-driven methods , 2007 .

[2]  M. Koch,et al.  THE ROLE OF OCEAN STATE INDICES IN SEASONAL AND INTER-ANNUAL CLIMATE VARIABILITY OF THAILAND , 2010 .

[3]  Panmao Zhai,et al.  A New Forecast Model Based on the Analog Method for Persistent Extreme Precipitation , 2016 .

[4]  I. Zin,et al.  Probabilistic flood forecasting on the Rhone River: evaluation with ensemble and analogue-based precipitation forecasts , 2016 .

[5]  Paul A. Watters,et al.  Statistics in a nutshell , 2008 .

[6]  Shenglian Guo,et al.  Comparative study of monthly inflow prediction methods for the Three Gorges Reservoir , 2014, Stochastic Environmental Research and Risk Assessment.

[7]  Chuntian Cheng,et al.  Daily reservoir inflow forecasting combining QPF into ANNs model , 2009 .

[8]  Faridah Othman,et al.  Reservoir inflow forecasting using artificial neural network , 2011 .

[9]  C. Obled,et al.  The analogue method for precipitation prediction: finding better analogue situations at a sub-daily time step , 2016 .

[10]  Faridah Othman,et al.  Optimization of Multiple and Multipurpose Reservoir System Operations by Using Matrix Structure (Case Study: Karun and Dez Reservoir Dams) , 2016, PloS one.

[12]  Soroosh Sorooshian,et al.  Developing reservoir monthly inflow forecasts using artificial intelligence and climate phenomenon information , 2017 .

[13]  Harold A Thomas,et al.  OPERATIONS RESEARCH IN WATER QUALITY MANAGEMENT. , 1963 .

[14]  Ezio Todini,et al.  The use of meteorological analogues to account for LAM QPF uncertainty , 2006 .

[15]  R Govindaraju,et al.  ARTIFICIAL NEURAL NETWORKS IN HYDROLOGY: II, HYDROLOGIC APPLICATIONS , 2000 .

[16]  H. Storch,et al.  Influence of similarity measures on the performance of the analog method for downscaling daily precipitation , 2008 .

[18]  Taikan Oki,et al.  Characteristics of the 2011 Chao Phraya River flood in Central Thailand , 2012 .

[19]  S. Mohan,et al.  OPTIMIZATION OF MULTIPURPOSE RESERVOIR SYSTEM OPERATION , 1991 .

[20]  ARTIFICIAL NEURAL NETWORKS IN HYDROLOGY . I : PRELIMINARY CONCEPTS By the ASCE Task Committee on Application of Artificial Neural Networks in Hydrology , 2022 .

[21]  Xiaohua Dong,et al.  Effect of flow forecasting quality on benefits of reservoir operation – a case study for the Geheyan reservoir (China) , 2006 .

[22]  Cecilia Svensson,et al.  Seasonal river flow forecasts for the United Kingdom using persistence and historical analogues , 2016 .

[23]  P. Vincent,et al.  Effect of inflow forecast accuracy and operating time horizon in optimizing irrigation releases , 2007 .

[24]  P. C. Nayak,et al.  Improving peak flow estimates in artificial neural network river flow models , 2003 .

[25]  A. Bridhikitti.  Connections of ENSO/IOD and aerosols with Thai rainfall anomalies and associated implications for local rainfall forecasts , 2013 .

[26]  Zhizhen Zhao,et al.  Analog forecasting with dynamics-adapted kernels , 2014, 1412.3831.

[27]  A. Gosain,et al.  Optimisation of Multipurpose Reservoir Operation by coupling SWAT and Genetic Algorithm for Optimal Operating Policy (Case Study: Ganga River basin) , 2018 .

[28]  Dionysia Panagoulia,et al.  Artificial neural networks and high and low flows in various climate regimes , 2006 .

[29]  C. Obled,et al.  Quantitative precipitation forecasts: a statistical adaptation of model outputs through an analogues sorting approach , 2002 .

[30]  Amnatsan Somchit,et al.  Application of Artificial Neural Networks and Wavelet Analysis in Prediction of Water Level in Nan River of Thailand , 2010 .

[31]  N. Null.  Artificial Neural Networks in Hydrology. I: Preliminary Concepts , 2000 .

[32]  Taesoon Kim,et al.  Inflow Forecasting for Real-Time Reservoir Operation Using Artificial Neural Network , 2009 .

[33]  Gaige Wang,et al.  Wavelet Neural Network Using Multiple Wavelet Functions in Target Threat Assessment , 2013, TheScientificWorldJournal.

[35]  Mohammad Ebrahim Banihabib,et al.  Monthly Inflow Forecasting using Autoregressive Artificial Neural Network , 2012 .

[36]  T. Oki,et al.  A simulation study on modifying reservoir operation rules : tradeoffs between flood mitigation and water supply , 2013 .

[37]  M. Babel,et al.  Hydroclimate variability and its statistical links to the large-scale climate indices for the Upper Chao Phraya River Basin, Thailand , 2009 .

[38]  M. Franca,et al.  Reservoir sedimentation , 2016 .

[39]  null null,et al.  Artificial Neural Networks in Hydrology. II: Hydrologic Applications , 2000 .

[40]  S.-C. Lin,et al.  Study On Optimal Operating Rule CurvesIncluding Hydropower Purpose InParallel Multireservoir Systems , 2005 .

[41]  K. Mohammadi,et al.  Comparison of regression, ARIMA and ANN models for reservoir inflow forecasting using snowmelt equivalent (a case study of Karaj). , 2005 .

[42]  Juan B. Valdés,et al.  NONLINEAR MODEL FOR DROUGHT FORECASTING BASED ON A CONJUNCTION OF WAVELET TRANSFORMS AND NEURAL NETWORKS , 2003 .