Reservoir Inflow Prediction Research Articles

Wavelet neural network model for reservoir inflow prediction

In this study, a Wavelet Neural Network (WNN) model is proposed for monthly reservoir inflow prediction by combining the Discrete Wavelet Transform (DWT) and Levenberg-Marquardt optimization algorithm-based Feed Forward Neural Networks (FFNN). The study area covers the basin of Kemer Dam which is located in the Aegean region of Turkey. Monthly meteorological data were decomposed into wavelet sub-time series by DWT. Ineffective sub-time series have been eliminated by using all possible regression method and evaluating the Mallows’ Cp coefficients to prevent collinearity. Then, effective sub-time series components have been used as the new inputs of neural networks. DWT has been also integrated with multiple linear regressions (WREG) within the study. The results of Wavelet Neural Network (WNN) model and WREG have been compared with conventional Feed Forward Neural Networks (FFNN) and multiple linear regression (REG) models. When the statistical-based criteria are examined, it has been observed that the DWT method has increased the performances of feed forward neural networks and regression methods. The results determined in the study indicate that the WNN is a successful tool to model the monthly inflow series of dam and can give good prediction performances than other methods.

Multi-time-step ahead daily and hourly intermittent reservoir inflow prediction by artificial intelligent techniques using lumped and distributed data

In this study, artificial intelligent (AI) techniques such as artificial neural network (ANN), Adaptive neuro-fuzzy inference system (ANFIS) and Linear genetic programming (LGP) are used to predict daily and hourly multi-time-step ahead intermittent reservoir inflow. To illustrate the applicability of AI techniques, intermittent Koyna river watershed in Maharashtra, India is chosen as a case study. Based on the observed daily and hourly rainfall and reservoir inflow various types of time-series, cause-effect and combined models are developed with lumped and distributed input data. Further, the model performance was evaluated using various performance criteria. From the results, it is found that the performances of LGP models are found to be superior to ANN and ANFIS models especially in predicting the peak inflows for both daily and hourly time-step. A detailed comparison of the overall performance indicated that the combined input model (combination of rainfall and inflow) performed better in both lumped and distributed input data modelling. It was observed that the lumped input data models performed slightly better because; apart from reducing the noise in the data, the better techniques and their training approach, appropriate selection of network architecture, required inputs, and also training–testing ratios of the data set. The slight poor performance of distributed data is due to large variations and lesser number of observed values.

Hydrological time series modeling: A comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques

Summary Time series modeling is necessary for the planning and management of reservoirs. More recently, the soft computing techniques have been used in hydrological modeling and forecasting. In this study, the potential of artificial neural networks and neuro-fuzzy system in monthly reservoir inflow forecasting are examined by developing and comparing monthly reservoir inflow prediction models, based on autoregressive (AR), artificial neural networks (ANNs) and adaptive neural-based fuzzy inference system (ANFIS). To take care the effect of monthly periodicity in the flow data, cyclic terms are also included in the ANN and ANFIS models. Working with time series flow data of the Sutlej River at Bhakra Dam, India, several ANN and adaptive neuro-fuzzy models are trained with different input vectors. To evaluate the performance of the selected ANN and adaptive neural fuzzy inference system (ANFIS) models, comparison is made with the autoregressive (AR) models. The ANFIS model trained with the input data vector including previous inflows and cyclic terms of monthly periodicity has shown a significant improvement in the forecast accuracy in comparison with the ANFIS models trained with the input vectors considering only previous inflows. In all cases ANFIS gives more accurate forecast than the AR and ANN models. The proposed ANFIS model coupled with the cyclic terms is shown to provide better representation of the monthly inflow forecasting for planning and operation of reservoir.

Applying fuzzy grey modification model on inflow forecasting

This paper investigates a modified grey model for forecasting the inflow of a reservoir. The integral form of the background value is employed for the original grey model, GM(1,1), to improve accuracy and applicability. Thereafter, the Fourier series is altered to handle extreme values with regard to prediction; exponential smoothing is used to improve the drawbacks of the prediction delay phenomenon. Finally, we are hybridised as the ultimate grey model with outstanding prediction accuracy, namely EFGM(1,1). As a typhoon causes significant changes in the inflow of a reservoir, this paper applies the fuzzy membership function for dealing with it during the flood season to construct the fuzzy grey modification model, FEFGM(1,1). Results of grey models are compared with those of the Autoregressive Integrated Moving Average (ARIMA). By evaluating different indices, the errors of the predicted extreme value of EFGM(1,1) perform better than those of GM(1,1) and ARIMA, however worse than that of FEFGM(1,1). The final FEFGM(1,1) shows high precision with regard to reservoir inflow prediction during typhoons with combined effects of fuzzy, exponential smoothing, Fourier series.

Effect of Pruning and Smoothing while Using M5 Model Tree Technique for Reservoir Inflow Prediction

This study reports the performance of an M5 model tree (MT) and the effects of pruning and smoothing applied to reservoir inflow prediction. The full year and seasonal monthly time step MT predictions were compared with conventional univariate autoregressive integrated moving average (stochastic) models. It was found that stochastic models could not predict the future inflows in a better way, because the observed series had not followed any particular distribution. However, it was found that the stochastic models showed better improvement using a logarithmic-transformed series, but the logarithmic-transformed MT results showed otherwise. The model validation was performed using the comparison of goodness of fit measures, standard statistics, time series, and scatter plots of predicted inflows with observed inflows. The effect of pruning each leaf in the MT model was also studied. Instead of pruning all the leaves, leading to lesser predictive accuracy, selective pruning was carried out based on the import...

Multivariate Reservoir Inflow Forecasting Using Temporal Neural Networks

An experiment on predicting multivariate water resource time series, specifically the prediction of hydropower reservoir inflow using temporal neural networks, is presented. This paper focuses on dynamic neural networks to address the temporal relationships of the hydrological series. Three types of temporal neural network architectures with different inherent representations of temporal information are investigated. An input delayed neural network (IDNN) and a recurrent neural network (RNN) with and without input time delays are proposed for multivariate reservoir inflow forecasting. The forecast results indicate that, overall, the RNN obtained the best performance. The results also suggest that the use of input time delays significantly improves the conventional multilayer perceptron (MLP) network but does not provide any improvement in the RNN model. However, the RNN with input time delays remains slightly more effective for multivariate reservoir inflow prediction than the IDNN model. Moreover, it is ...

River flow time series prediction with a range-dependent neural network

Artificial neural networks provide a promising alternative to hydrological time series modelling. However, there are still many fundamental problems requiring further analyses, such as structure identification, parameter estimation, generalization, performance improvement, etc. Based on a proposed clustering algorithm for the training pairs, a new neural network, namely the range-dependent neural network (RDNN) has been developed for better accuracy in hydrological time series prediction. The applicability and potentials of the RDNN in daily streamflow and annual reservoir inflow prediction are examined using data from two watersheds in China. Empirical comparisons of the predictive accuracy, in terms of the model efficiency R2 and absolute relative errors (ARE), between the RDNN, back-propagation (BP) networks and the threshold auto-regressive (TAR) model are made. The case studies demonstrated that the RDNN network performed significantly better than the BP network, especially for reproducing low-flow events.

Application of ANN for Reservoir Inflow Prediction and Operation

Artificial neural networks (ANNs) are new computing architectures in the area of artificial intelligence. The present study aims at the application of ANNs for reservoir inflow prediction and operation. The Upper Indravati multipurpose project, in the state of Orissa, India, has been selected as the focus area. The project has primarily two objectives: To provide irrigation to 128,000,000 ha of agricultural land and to generate 600 MW of electric power. An autoregressive integrated moving average time-series model and an ANN-based model were fitted to the monthly inflow data series and their performances were compared. The ANN was found to model the high flows better, whereas low flows were better predicted through the autoregressive integrated moving average model. Reservoir operation policies were formulated through dynamic programming. The optimal release was related with storage, inflow, and demand through linear and nonlinear regression and the ANN. The results of intercomparison indicate that the AN...