S Mohan

Neural networks as models for waste treatment systems have been studied. A brief summary about the neural network approach and its application to two case studies have been discussed. The study shows that with both long series and short series of data on waste treatment processes, the neural networks have produced comparable results. Neural network models are robust and provide good predictions for the performance of the waste treatment systems. These neural network models appear to provide a promising alternative for waste treatment systems modelling.

An expert system for flood management is developed for the flood control operation of a reservoir. The procedure uses both expert system tools and traditional computer programming techniques considering the complexity of the reservoir operation problem. The present work has been carried out in four phases, namely, flood estimation, flood simulation, reservoir operation, and expert system development. In the flood simulation phase, rainfall-runoff computation model, and model for computing water surface profiles have been utilized. The use of the developed system is demonstrated with a case study of the Adyar river in the Madras metropolitan city to evolve the safe releases that can be followed during flood considering the reservoir inflows and the overland flow from the urban drainage area. The developed expert system could be a valuable tool in reservoir operation decision-making and thereby help in minimizing the flood damages in the Adyar river flood plains.

The application of the Muskingum model to river and channel flood routing may have some limitations because of its inherent assumption of a linear relationship between channel storage and weighted flow. Although nonlinear forms of the Muskingum model have been proposed, an efficient method for parameter estimation in the calibration process is still lacking. In this paper, the objective approach of genetic algorithm is proposed for the purpose of estimating the parameters of two nonlinear Muskingum routing models. The performance of this algorithm is compared with other reported parameter estimation techniques. Results of the application of this approach to an example with high nonlinearity between storage and weighted-flow, show that the genetic algorithm approach is efficient in estimating parameters of the nonlinear routing models. A supplementary analysis of the sensitivity of the parameters during the performance of genetic algorithm shows that a unique set of parameters exists that would result in the best performance for a given problem. In addition, genetic algorithm does not demand any initial estimate of values of any of the parameters, and thus avoids the subjectivity and computation time associated with the traditional estimation methods.

Five methods of computing reference evapotranspiration from a reference crop (Penman, radiation, Blaney-Criddle, Hargreaves and pan evaporation) have been studied for their applicability under different climatic conditions. The Penman method was taken as the standard and the other four methods were compared against this method. Good correlation was obtained between the values estimated by the four methods and the Penman method although differences in magnitude were found. Regression equations were developed to correct those differences in magnitude. The method suitable for the estimation of reference evapotranspiration for each climatic condition is also suggested. © 1991 Taylor and Francis Group, LLC.

The explicit consideration of multiple objectives in decision making is becoming increasingly important in water resources systems analysis. Conflicting objectives like irrigation, flood control, hydropower production, and environmental preservation are inevitable while deriving the operating alternatives in an efficient way for any complex water resources system. In this paper, a linear multiobjective programming model has been developed and the constraint technique was used to derive the optimal releases for various purposes from a large-scale multireservoir system consisting of five reservoirs in India. Maximization of irrigation releases and maximization of hydropower production have been considered as the twin objectives in the model subjected to constraints on physical limitations, environmental restrictions, and storage continuity. This model was applied to three series of inflow sequences representing the normal, drought, and excess-flow conditions. The tradeoff analysis between the conflicting objectives of irrigation and hydropower production was also carried out and the transformation curve was plotted. The optimal point on this curve gives the best combination of the twin objectives considered in the model. © 1992 ASCE.

Evapotranspiration (ET) is an important process in the hydrological cycle and needs to be accurately quantified for proper irrigation scheduling and optimal water resources systems operation. The time variant characteristics of ET necessitate the need for forecasting ET. In this paper, two techniques, namely a seasonal ARIMA model and Winter’s exponential smoothing model, have been investigated for their applicability for forecasting weekly reference crop ET. A seasonal ARIMA model with one autoregressive and one moving average process and with a seasonality of 52 weeks was found to be an appropriate stochastic model. The ARIMA and Winter’s models were compared with a simple ET model to assess their performance in forecasting. The forecast errors produced by these models were very small and the models would be promisingly of great use in real-time irrigation management. © 1995 Taylor & Francis Group, LLC.

Irrigation has been practised in India from ancient time. Even now, India has the largest area under irrigation. Tamilnadu state in India stands one among the few states in successfully harnessing and utilising the available water. Future development and management of available water resources can be achieved through optimisation techniques. Water management at farm level is one of the most important activities to be carried out for optimal use of water. Evapotranspiration is the most important variable next to rainfall in the context of irrigation to crops. In this paper, the results of the comparative study on crop water requirements estimation by two methods namely FAO-Penman method and Duty-delta method are reported. It is found that duty-delta method underestimates the crop water requirements.

Most of the water resources systems in India are operated with conventional approaches formulated several decades ago without much knowledge about the system behaviour. In the present study, the HEC-3 simulation model has been used to derive the operating policies for an interstate, multivalley-multireservoir system in India, to achieve optimal operational policies. Four different HEC-3 policies were analysed together with the standard operating policy. The results of the simulation runs were analysed and a suitable optimal operating policy for a multivalley-multireservoir system operation has been evolved. Trade-off analysis between the conflicting objectives of irrigation releases and hydropower production and the probability distribution for the annual average hydropower production were also arrived at to estimate the reliability of the system. © 1993, Taylor & Francis Group, LLC. All rights reserved.

Models are proposed to extend the monthly streamflow data at a site where the available historic rainfall and streamflow data are too short for adequate systems study, subject to the condition that there are no gauging sites in the basin or adjacent basins with a longer period of streamflow data. Hence rainfall data of a nearby raingauge station are used. Five regression models, namely, runoff coefficient model, single linear regression, monthly linear regression, monthly linear regression with stochastic description for residuals, and a double regressed model are used. The results show that the monthly linear regression model with stochastic description for the residuals is best suited for the purpose when applied to a case study. © 1995 Taylor and Francis Group, LLC.