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In recent years, ANNs have been successfully applied in a broad range of areas including science, engineering, telecommunication, technology, and business (Widrow, Rumelhard, & Lehr, 1994). Some specific applications include remote sensing, stock trading, speech and handwriting recognition, face recognition, e-mail spam filtering, credit scoring, fraud detection, and medical diagnosis. ANNs have also been used in environmental engineering to develop hydrological models, taking advantage of their ability to capture and learn both linear and complex non-linear relationships from modelling data, especially in situations where the underlying physical relationships are not fully understood (Lingireddy & Brion, 2005).

Maier and Dandy (1996) used a back-propagation ANN model to obtain a 14-day forecast of the salinity of the Murray River in South Australia. The K-fold cross-validation method was used to validate the model. The average absolute percentage errors for the model ranged from 5.3–7.0%, indicating the model gave good prediction. This model could help save money, because high salinity levels in the Murray River cost consumers in Adelaide approximately $US22 million per year in damages (Maier & Dandy, 1996). The model could be further improved by using an optimisation technique to fine-tune the number of hidden neurons and layers to obtain appropriate numbers to use during the training process. Aafjes, Verberne, Hendrix and Vingerhoeds (1997) used a combination of expert systems and ANN to predict water consumption at Friesland, Netherlands. They used a two-year data set which included independent variables such as hourly precipitation, global radiation, temperature, and air pressure. The day of the week and past holidays were also used as independent variables. The dependent variable was hourly water consumption. They also used a traditional statistical-based model, auto regressive integrated moving

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average (ARIMA) to make water consumption predictions from the same data. They observed that the ANN model gave better predictions than the ARIMA model.

A number of factors can contribute to drinking water discolouration. One of them is the dissipation of residual chlorine (a chemical that kills microorganisms or prevents their growth), which eventually leads to increased biological oxidation. Rodriguez, West, Powell and Serodes (1997) used ANN and traditional first-order modelling approach to predict residual chlorine in WDNs. They observed that the ANN model gave better predictions than the first-order model.

Many researchers have also used ANNs to predict raw water quality (DeSilets, Golden, Wang, & Kumar, 1992; Zhang & Stanley, 1997). Knowledge of the concentrations of incoming raw water quality variables such as turbidity, Fe, Mn, water colour, and coliform bacteria in advance is very important in drinking water treatment process, because it enables water utilities to optimise the treatment process to prevent inadequate or over- treatment of the raw water. For instance, insufficient chlorination in the treatment process can increase microbial re-growth, and subsequently cause waterborne diseases like typhoid fever, cholera, and hepatitis A. Over-chlorination can lead to an increase in customer complaints due to the taste and smell of chlorine. Zhang and Stanley (1997) developed a back-propagation ANN model that uses a five-year data set consisting of variables such as river flow rate, precipitation, and turbidity to forecast raw water colour. The ability of this ANN model to deal with multiple complex nonlinear input variables makes it an improvement on other conventional models. The model was able to reasonably predict all the peaks and recognise 355 out of 365 patterns. ANNs have also been used to forecast turbidity and colour removal through enhanced coagulation (Stanley, Baxter, Zhang, & Shariff, 2000), predict source water salinity (DeSilets et al., 1992), and forecast the dose of alum and polymers required for coagulation (Mirsepassi, Cathers, & Dharmappa, 1995). Gautam (1999) used an auto-regression neural network (ARNN) model to predict the level of Lake Ijsselmeer at North Holland. The input variables for the model were wind speed, discharge of the lake, daily low tide water level, and water level of the sea. Data from two seasons were divided into two and used to train and verify the ARNN model. Figure 3.1 shows a graph of the observed (target) and predicted water levels by the ARNN model.

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From the graph it was observed that the model was able to predict most of the peak levels of the lake.

Figure 3.1 Graph of the observed (target) and predicted water levels by the ARNN model Gautam (1999)

Lint and Vonk (1999) developed an ANN model to predict water levels in reservoirs for South Holland Province Water Authority because the expert system they had previously used gave inaccurate results. The water authority needed to know the water levels of the reservoirs 24 hours in advance in order to optimise the pumping of water from high-level reservoirs to low-level reservoirs during night, when energy costs are cheaper. The input variables used for the model were pump status, precipitation, water level, and temperature at hourly intervals for the preceding 12 hours. Other input variables required by the model were one-hour-in-advance predicted temperature and precipitation. The output variable was 24-hours-in-advance water level of reservoirs at time steps of one hour. The model was able to predict water level with a coefficient of determination value of 0.71. In a related study, Raman and Sunilkumar (1995) used multivariate auto-regression (MAR) and an ANN model to predict monthly reservoir inflows at two sites in Kerala, India, namely, Mangalam and Pothundy reservoirs. A data set from the two reservoirs over a 14-year period was used to train and test the model. The four input variables used were two consecutive normalised monthly inflow values for each of the reservoirs. Table 3.1 shows the mean of the historic and generated inflow series by the ANN and MAR models. Comparing the two models, they observed that the ANN model generated better results than the MAR model.

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Table 3.1 Mean of the historic and generated inflow series by the ANN and MAR models Mangalam reservoir Pothimdy reservoir Month Mean Historic data ANN model MAR model Mean Historic data ANN model MAR model January 1.254 1.397 1.412 4.57 4.123 5.464 February 0.641 0.643 0.962 0.91 0.808 1.088 March 0.2 0.21 0.178 0.503 0.443 0.578 April 0.413 0.392 0.641 0.531 0.489 0.901 May 0.898 0.744 1.199 0.836 0.848 0.752 June 12.528 12.507 13.19 8.519 8.594 9.668 July 24.275 24.416 23.131 15.543 15.616 15.691 August 24.452 24.091 24.432 15.615 15.148 14.457 September 10.63 10.324 11.323 5.14 5.134 5.124 October 9.644 9.993 7.998 4.18 4.029 4.072 November 5.633 5.787 4.929 4.683 4.268 4.627 December 1.477 1.778 1.88 4.479 4.633 4.486