In this study, multi-layer perceptron (MLP) artificial neural networks have been applied to forecast one-month-ahead inflow for the Ubonratana reservoir, Thailand. To assess how well the forecast inflows have performed in the operation of the reservoir, simulations were carried out guided by the systems rule curves. As basis of comparison, four inflow situations were considered: (1) inflow known and assumed to be the historic (Type A); (2) inflow known and assumed to be the forecast (Type F); (3) inflow known and assumed to be the historic mean for month (Type M); and (4) inflow is unknown with release decision only conditioned on the starting reservoir storage (Type N). Reservoir performance was summarised in terms of reliability, resilience, vulnerability and sustainability. It was found that Type F inflow situation produced the best performance while Type N was the worst performing. This clearly demonstrates the importance of good inflow information for effective reservoir operation.

The planning of reservoirs for various purposes including flood and drought control relies on the historic inflow data at the reservoir site. Due to natural variability and other factors (e.g. climate and land-use changes), however, the inflow situation when the reservoir is being operated will be different. It is therefore important that reservoirs are properly operated so that they continue to perform satisfactorily during changing hydro-climatology.

Reservoir operation concerns taking decisions on water release from a reservoir based on the amount of water available vis-à-vis the demand placed on the system. The available water is the sum of starting period storage and the inflow expected during the period. Consequently, effective reservoir operation relies on reliable forecast of the inflow into the reservoir. Traditional forecasting methods using hydrologic, hydraulic and time-series models require specification of the functional relationship of the model which can be problematic (Zhang et al., 1998), which is why focus has recently shifted to the use of data-driven techniques that do not require knowledge of this functional relationship. In particular, artificial neural networks (ANN) have been widely used to forecast reservoir inflows (see e.g. Edossa and Babel, 2012; Mohammadi et al., 2005) due to their effectiveness and flexibility and have been proven to be superior to other approaches such as regression-based and time series models.

The aim of this study is to apply multi-layer perceptron (MLP)-ANN for the one-month-ahead inflow forecasting for the Ubonratana reservoir, Thailand. To investigate the effect of the forecasts on reservoir operation performance, four situations were considered for the one-month-ahead inflow: (1) inflow is known and assumed to be the historic (Type A); (2) inflow is known and assumed to be the ANN forecast (Type F); (3) inflow is known and assumed to be the historic average for the given month (Type M); and (4) inflow is not known and the release decision is conditioned only on the starting reservoir storage (Type N). Simulations of the Ubonratana reservoir were then carried out with these alternative inflow scenarios and the resulting reservoir performance was summarised in terms of reliability, resilience, vulnerability and sustainability.

In the next section, further details about the methodology will be given. This is then followed by the presentation of the case study. Next the results are presented and discussed and finally, the main conclusions are given.

The theory and mathematical basis of ANN have been described excellently by Shamseldin (1997). Essentially, the structure of ANN comprises an input layer, an output layer and one or more hidden layers as illustrated in Fig. 1. The schematic in Fig. 1 has a single hidden layer which is generally sufficient to approximate any complex, non-linear function (Mulia et al., 2015). The layers contain nodes or neurons which are connected by weights. Determining optimal values for these weights and other parameters of the network is the purpose of the ANN training exercise.

For a given problem, the number of nodes in the output layer is fixed by the problem, e.g. in the current work, it is the 1-month ahead inflow forecast. The input nodes must be determined by the factors known to affect the output variable and this has been achieved through an examination of the cross-correlation matrix (see Adeloye and De Munari, 2006). The number of neurons in the hidden layer is much more difficult to arrive at and is normally determined as part of the training by trial and error as described by Adeloye and De Munari (2006).

Training is often improved through the use of early-stop-rule (ESR) that helps to avoid over-fitting. In ESR, the available data are divided into three parts: (i) a training set, used to determine the network weights and biases, (ii) a validation set, used to estimate the network performance and decide when the training should be stopped, and (iii) a test set, used to verify the effectiveness of the stopping criterion and to estimate the expected performance in the future.

The tested ANN architectures (in trying to arrive at the best value for the
number of hidden neurons) were compared using the correlation coefficient
(

Schematic of artificial neural network.

Reservoir behaviour simulation employed the mass balance equation (McMahon
and Adeloye, 2005):

As noted previously, the water available for allocation during

Let the actual end-of-period storage be

Type A: WA

Type F: WA

Type M: WA

Type N: WA

With the available water determined, release then takes place guided by the rule curves as follows:

Case 1: For WA

Case 2: For LRC

Case 3: For WA

Once the simulation is complete, performance indices are then evaluated as
follows (McMahon and Adeloye, 2005):

Time-based Reliability (

Volume-based Reliability (

Resilience:

Vulnerability:

Sustainability index (Sandoval-Solis et al., 2011):

where

and

The Ubonratana reservoir is the largest, single multi-purpose reservoir in
the upper Chi River Basin in north-eastern Thailand. The dam provides water
for consumptive uses (domestic, industrial, irrigation), Pong River in-stream
flow augmentation as well as flood control (EGAT, 2002). However, the water
deliveries first pass through turbines for power generation (installed
capacity

Data collected for the study included daily reservoir inflows, evaporation,
area-height-storage relationship, weekly and monthly water requirements and
operating rule curves for the reservoir. The observed monthly inflow from
April 1970 to March 2012 and rainfall from April 1981 to March 2012 were
provided by the Electricity Generating Authority of Thailand (EGAT) and the
Royal Irrigation Department (RID). The analysis, however, used the
overlapping period of April 1982 to March 2012 (i.e. 360 months) for which
the rainfall and runoff data were complete. Data on historical water releases
to the various sectors were also provided by the RID. The gross water
requirements for the analysis period were 28 952 Mm

Rule curves for Ubonratana reservoir.

Based on extensive testing involving the examination of the auto-correlation
function (acf – Fig. 3a), partial-autocorrelation function (pcf – Fig. 3b)
and cross-correlation function (ccf – Fig. 3c), six input variables (i.e.
current month historic mean inflow, lagged inflows (

Inflow

Comparing the 1-month ahead observed and forecast inflow during

Time series of 1-month ahead of observed and forecast inflows for the complete data record.

Summary of evaluated reservoir performance indices for Ubonratana reservoir.

The ESR was used for the ANN training and for this the 360 months of data
were split into three (90 : 5 : 5) for training, validation and testing,
respectively. The number of hidden neurons was varied between 1 and 35 and
based on the

The results of the performance evaluation are summarised in Table 1. For convenience, the operating policy with Type A, Type F, Type M and Type N are denoted by P-A, P-F, P-M and P-N, respectively.

As seen in Table 1, in terms of the total amount of water released, P-A, P-F
and P-M were significantly better than P-N, which is not surprising given
that P-N did not have any additional water from inflows. In terms of
reliability (

The other performance indices reported in Table 1 all reveal the superiority of P-F relative to the other inflow situations. For example, the group sustainability index for P-F was the highest of all four; indeed, the same better performance of P-F was recorded across all three (public, instream and irrigation) demand sectors supplied by the reservoir. As expected, the conservative nature of P-N resulted in the least number of excursions below the LRC. This is likely to benefit the hydro-power generation potential of the reservoir albeit, as revealed by this study, at the expense of its performance in meeting the consumptive demands.

This study has developed MLP-ANN model to forecast one-month-ahead inflow for the Ubonratana reservoir in north-eastern Thailand. Extensive testing of the model showed that it was able to provide inflow forecasts with reasonable accuracy. The performance of the ANN forecasts was tested against those of three other inflow scenarios and the reservoir simulation results showed that the ANN forecasts produced superior reservoir performance. The worst performing inflow situation was when there was complete lack of knowledge about the inflow and release decision was based on the starting storage alone. All this represents an objective demonstration of good inflow forecast knowledge for effective reservoir operation.

This study formed part of the PhD research undertaken by the first author with PhD Scholarship provided by the Royal Thai Government. We also thank officials of EGAT for providing the data and other information used for the study.