Hydrological models are applied frequently to scientific or practical problems. For many applications it is concluded that the model has been able to reproduce the measurements with ‘acceptable accuracy’. The question is what we mean by this term; the meaning of ‘acceptable accuracy’ can be quite subjective. We might compute statistical goodness-of-fit measures such as model efficiency, but even the use of such a measure does not necessarily allow an objective judgment of model performance. Does an efficiency of 0.8 for the runoff simulations indicate good or poor model performance? The answer depends on whom you ask. But it also depends on what could be achieved given the specific catchment and the observed data. What might be a poor fit for a watershed with excellent measurements might rightly be considered to be good in a watershed where the available data are of poor quality. To truly assess model performance, it is important to compare one’s results with results obtained in some other way, i.e. to choose an appropriate benchmark series.

On the need for benchmarks in hydrological modelling (Invited commentary)

Reservoir inflow forecasting plays an essential role in reservoir operation and management. Considering the characteristics of monthly inflow (trend, seasonality, and randomness throughout the hydrologic year), an additive model is proposed to forecast monthly reservoir inflow. Because different features are represented by different frequency bands of the time series, historical time series of the monthly inflow are decomposed by ensemble empirical mode decomposition into several intrinsic mode functions and a residue. According to frequency signatures analyzed by Fourier spectral representation, all intrinsic mode functions and residue are grouped into three terms: trend term, periodic term, and stochastic term. To accommodate the different characteristics of the three terms, an autoregressive model, a least-squares support vector machine, and an adaptive neuro-fuzzy inference system model are adopted for the three subforecasts, respectively. The additive model is subsequently used to integrate t...

Additive Model for Monthly Reservoir Inflow Forecast

Tunnel surrounding rock deformation is dynamic, sensitive to time and space, nonlinear, and highly complicated. By combining the firefly algorithm (FA) and nonlinear auto-regressive (NAR) dynamic neural network method, an algorithm model was proposed for predicting dynamic nonlinear surrounding rock deformation. The FA improved the prediction accuracy of the NAR dynamic neural network by determining the optimum values of two network parameters—delay order and number of units in the hidden layer; combined with the monitoring results of Beishan exploration tunnel (BET), this is demonstrated by a comparative analysis of predictions yielded by the FA–NAR dynamic neural network and by the least squares support vector machine (LS-SVM). In general, the comparation shows that the FA–NAR dynamic neural network model yielded predictions that are fundamentally consistent with measurements and exhibits higher prediction accuracy than the LS-SVM. Results also show that the surrounding rock deformation prediction of BET for March 4, 2020 was marginally smaller than 2.43 mm.

Research on deformation prediction of tunnel surrounding rock using the model combining firefly algorithm and nonlinear auto-regressive dynamic neural network

Considering the complexity of reservoir systems, a model fusion approach is proposed in this paper. According to different inflow information represented, the historical monthly data can be constructed as two time series, namely, yearly-scale series and monthly-scale series. Even grey model (EGM) and adaptive neuro-fuzzy inference system (ANFIS) are adopted for the forecasts at the two scales, respectively. Grey relational analysis (GRA) is subsequently used as a scale-normalized model fusion tool to integrate the two scales' results. The proposed method is evaluated using the data of the Three Gorges reservoir ranging from January 2000 to December 2012. The forecast performances of the individual-scale models are improved substantially by the suggested method. For comparison, two peer models, back-propagation neural network and autoregressive integrated moving average model, are also involved. The results show that, having combined together the small-sample forecast ability of the EGM in the yearly-scale, the nonlinearity of the ANFIS in the monthly-scale, and the grey fusion capability of the GRA, the present approach is more accurate for holistic evaluation than those models in terms of mean absolute percentage error, normalized root-mean-square error, and correlation coefficient criteria, and also for peak inflow forecasting in accordance with peak percent threshold statistics.

/pdf/model-fusion-approach-for-monthly-reservoir-inflow-40pyjcou6z.pdf

Model fusion approach for monthly reservoir inflow forecasting

Vector-valued function estimation by grammatical evolution for autonomous robot control

Streamflow forecasting is significantly important for planning and operating water resource systems. However, streamflow formation is a highly nonlinear, time varying, spatially distributed process and difficult to forecast. This paper proposes a nonlinear model which incorporates improved real-coded grammatical evolution (GE) with a genetic algorithm (GA) to predict the ten-day inflow of the De-Chi Reservoir in central Taiwan. The GE is a recently developed evolutionary-programming algorithm used to express complex relationships among long-term nonlinear time series. The algorithm discovers significant input variables and combines them to form mathematical equations automatically. Utilizing GA with GE optimizes an appropriate type of function and its associated coefficients. To enhance searching efficiency and genetic diversity during GA optimization, the macro-evolutionary algorithm (MA) is processed as a selection operator. The results using an example of theoretical nonlinear time series problems indicate that the proposed GEMA yields an efficient optimal solution. GEMA has the advantages of its ability to learn relationships hidden in data and express them automatically in a mathematical manner. When applied to a real world case study, the fittest equation generated through GEMA used only a single input variable in a reasonable nonlinear form. The predicting accuracies of GEMA were better than those of the traditional linear regression (LR) model and as good as those of the back-propagation neural network (BPNN). In addition, the predicting of ten-day reservoir inflows reveals the effectives of GEMA, and standardization is beneficial to model for seasonal time series.

Macro-grammatical evolution for nonlinear time series modeling—a case study of reservoir inflow forecasting

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Macro-grammatical evolution for nonlinear time series modeling—a case study of reservoir inflow forecasting