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The effect of data pre-processing while developing artificial intelligence (AI) -based data-driven techniques, such as artificial neural networks (ANN), model trees (MT) and linear genetic programming (LGP), is studied for Pawana Reservoir in Maharashtra, India. The daily one-step-ahead inflow forecasts are compared with flows generated from a univariate autoregressive integrated moving average (ARIMA) model. For the full-year data series, a large error is found mainly due to the occurrence of zero values, since the reservoir is located in an intermittent river. Hence, all the techniques are evaluated using two data series: 18 years of daily full-year inflow data (from 1 January to 31 December); and 18 years of daily monsoon season inflow data (from 1 June to 31 October) to take into account the intermittent nature of the data. The relevant range of inputs for each category is selected based on autocorrelation and partial autocorrelation analyses of the inflow series. Conventional preprocessing methods, such as transformation and/or normalization of data, do not perform well because of the large variation in magnitudes, as well as the many zero values (65% of the full-year data series). Therefore, the input data are pre-processed into un-weighted moving average (MA) series of 3 days, 5 days and 7 days. The 3-day MA series performs better, maintaining the peak inflow pattern as in the actual data series, while the coarser-scale (5-day and 7-day) MA series reduce the peak inflow pattern, leading to more errors in peak inflow prediction. The results indicate that AI methods are powerful tools for modelling the daily flow time series with appropriate data preprocessing, in spite of the presence of many zero values. The time-lagged recurrent network (TLRN) ANN modelling technique applied in this study maps the inflow forecasting in a better way than the standard multilayer perceptron (MLP) neural networks, especially in the case of the seasonal data series. The MT technique performs equally well for low and medium inflows, but fails to predict the peak inflows. However, LGP outperforms the other AI models, and also the ARIMA model, for all inflow magnitudes. In the LGP model, the daily full-year data series with more zero inflow values performs better than the daily seasonal models.Key words data pre-processing; time-lagged recurrent network; model tree; linear genetic programming; moving average; transformation; full-year inflow data; seasonal inflow; Pawana Reservoir, India Amélioration de la performance de techniques conditionnées par les données par pré-traitement pour la modélisation de l'apport journalier d'un réservoir Résumé L'effet du pré-traitement des données à travers le développement des techniques d'intelligence artificielle (IA) conditionnées par les données, telles que les réseaux de neurones artificiels (RNA), les arbres modèles (MT) et la programmation génétique linéaire (PGL), est étudié pour le Réservoir Pawana dans le Maharashtra, Inde. Les prévisions à un jour des apports journaliers so...

Coagulation and chlorination are complex processes of a water treatment plant (WTP). Determination of coagulant and chlorine dose is time-consuming. Many times WTP operators in India determine the coagulant and chlorine dose approximately using their experience, which may lead to the use of excess or insufficient dose. Hence, there is a need to develop prediction models to determine optimum chlorine and coagulant doses. In this paper, artificial neural networks (ANN) are used for prediction due to their ability to learn and model non-linear and complex relationships. Separate ANN models for chlorine and coagulant doses are explored with radial basis neural network (RBFNN), feed-forward neural network (FFNN), cascade feed forward neural network (CFNN) and generalized regression neural network (GRNN). For modeling, daily water quality data of the last four years are collected from the plant laboratory of WTP in Maharashtra (India). In order to improve performance, these models are established by varying input variables, hidden nodes, training functions, spread factor, and epochs. The best models are selected based on the comparison of performance measures. It is observed that the best performing chlorine dose model using defined statistics is found to be RBFNN with R=0.999. Similarly, the CFNN coagulant dose model with Bayesian regularization (BR) training function provided excellent estimates with network architecture (2-40-1) and R=0.947. Based on the above models, two graphical user interfaces (GUIs) were developed for real-time prediction of chlorine and coagulant dose, which will be useful for plant operators and decision makers.

Although there have been many successful applications of artificial neural networks (ANNs) to capture non linear relationship of inflow into a reservoir, there are cases when ANNs have not been able to predict flow extremes accurately. Applicability of non linear model based on ANN with a random component embedded is explored for Pawana reservoir in Upper Bhima River Basin, Maharashtra, India. Suitability of time lagged recurrent networks (TLRNs) with time delay, gamma and laguarre memory structures is investigated for predicting seasonal (June to October) reservoir inflow with a monthly time step. Trial and error procedure is adopted in selecting input nodes and hidden nodes. Input to the network includes preceding records of reservoir inflow (varying from one lag to four lags). Performance of back propagation through time (BPTT) algorithm is investigated for various inputs assuming different training and testing combinations. Due to large variation in the observed data values, transformation of the series is also tried and found that the network trained for 50:50 training and testing data sets predicted better values. The validation of the models was performed using comparison of principal statistics (mean, standard deviation, skewness and kurtosis), goodness-of-fit measures (MSE, MAE, MRE and R), time series plots and scatter plots. Encouraging results obtained in the present study indicated that the log-transformed, BPTT trained TLRN resulted in better and reliable forecast of extreme high and low inflows as compared to non-transformed series.

In ogee spillway, the released flood water from crest to toe possesses a high amount of kinetic energy causing scour and erosion on the spillway structure. The dam projects normally have a stilling basin as an energy dissipater which has specific energy dissipation limitations. The stepped spillway is a better option to minimize kinetic energy along the chute and safely discharge water in the river domain. The Khadakwasla dam is situated in Pune, Maharashtra (India), and has scouring and erosion issues on the chute of ogee spillway and on the stilling basin. The present study develops a physical hydraulic model for the dam spillway with steps, plain and slotted roller bucket as per IS Code 6934 (1998) and IS Code 7365 (2010). Experiments were performed at heads of 4m (low head) and 6m (high head) on the developed physical models, namely on the plain and slotted roller bucket model for the ogee spillway and the plain and slotted roller bucket model for the stepped spillway. It was found that the plain roller bucket of ogee spillway dissipates 81.26% of energy at the low head, whereas the stepped spillway with slotted roller bucket dissipates the 83.86% of the energy at the high head.