S Mohan

Cellular Automata (CA) is an evolutionary computing technique that makes discrete idealizations of differential equations and represents the physical system at the mesoscopic scale. A novel CA approach for predicting the temporal and spatial variations of chlorine in Water Distribution Systems (WDSs) is presented in this paper. Random Walk Particle Tracking (RWPT), a stochastic Lagrangian technique, is used to represent the advection and dispersion processes. A one-dimensional CA-based reactive-transport model for chlorine, named as RWPT_CA model, incorporating advective-dispersive transport mechanism is developed and demonstrated. The significance of the cell dimension in the model algorithm is ascertained, and a deterministic approach is formulated for its selection. An indirect numerical solution technique is developed to improve the computational efficiency of the CA algorithm and to minimize the restrictions in the process of discretization of mass into equivalent particles. The numerical accuracy of the proposed RWPT_CA model is verified by applying it on to a benchmark problem. The RWPT_CA model provided excellent representations of the chlorine concentration profiles for low to medium range dispersion in WDSs. The model testing on a benchmark problem from the literature, well tested by researchers, revealed its effectiveness to derive the chlorine concentration patterns under dynamic hydraulic conditions. The dispersion mechanism was found significant in controlling the temporospatial distribution of chlorine at the nodes farther from the source nodes. The models which consider only advective transport mechanism were found over-predicting the chlorine concentrations, and thereby, establishing untrue representations of the quality of the delivered water.

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.

In this paper a novel approach for effective utilization of river assimilative capacity has been proposed. The method, referred to as waste load scheduling (WLS) is based on the principle that by restricting the effluent discharge into the river to only one polluter at any given day will allow us to utilize the available river assimilative capacity in a more efficient manner. This is achieved by scheduling the dischargeable waste load among the polluters, such that a waste load schedule once developed will specify two things: (1) which polluter has to discharge his/her effluent on a given day; and (2) what is the quantity of effluent that he/she can discharge. By scheduling the waste load discharge into the river thus, will considerably reduce the total effluent discharge into the river and hence a greater degree of water quality level can be achieved when compared to traditional waste load allocation methods. For the mathematical development of the model, the WLS problem was envisaged as analogous to a machine scheduling problem. In a simple single MS problem n number of jobs are required to be scheduled on a single machine to minimize/maximize a pre-defined performance measure. In a WLS problem, the river can be treated as a machine and the polluters discharging effluent directly into the river are analogous to the jobs to be scheduled. Treating the waste load scheduling problem in an analogous way to a MS problem enables us to apply the solution methods used for solving standard sequencing and scheduling problems to the proposed waste load scheduling problem. Although the present paper discusses the special case of waste load scheduling in which only one polluter can discharge effluent at any given day (suitable when the number of point load sources is small), it is however, possible to extend it to a more general case involving a large number of polluters as easily. In the accompanying paper, the application of the developed model to a case study has been explained in detail. The proposed model and its application proved that the model is highly efficient in solving the waste load allocation problem in a more comprehensive way.

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.

Flood routing through rivers and channels is an essential activity in hydrological analysis and this is particularly important because of the increasing emphasis that has been placed on dam-safety worldwide and due to the increasing urbanization near river channels. The routing of flood through river channels may be accomplished using two basic approaches namely hydrologic routing approach and hydraulic routing approach. There are different methods currently in usage and the Muskingum method is the most popular method and generally used by hydrologists and engineers. However, the reliability of this method is heavily depends upon the accuracy of the parameters namely K and x or C0, C1 and C2 of the model. These parameters are usually estimated by trial and error procedure. Muskingum model together with the Model proposed by Loucks (1989) have been considered for the present study and the parameters of these models were estimated using genetic algorithms, new search procedures for function optimization that apply the mechanics of natural genetics and natural selection to explore a given search space. This paper presents the results of the study of application of genetic algorithm for optimal parameter estimation of both linear and non-linear flood routing models to a case study. The sensitivity analysis of these estimated parameters was also carried out. The results had clearly depicted that the genetic algorithm is an efficient and robust means for estimation of flood routing model parameters. © 2009 ASCE.

An artificial neural network (ANN) model has been developed to generate the multisite streamflows and the results are compared with the classical multsite streamflow generation model developed by Hydrologic Engineering Centre named HEC-4. Both the models have been applied to the case study of Upper Krishna River Basin to evaluate their performances. Important statistical parameters, namely, mean, standard deviation, correlation coefficient of the historical and generated streamflows are compared for the evaluation. Hurst ratio has bem used to' evaluate the strength of persistence of the generated streamflows. This study shows that the streamflows predicted with simple ANN model are more satisfactory than the HEC-4 model in case of multisite streamflow generation.

Industrialisation is causing the environment and in particular groundwater to be affected by pollution. It is therefore imperative to adopt remediation techniques to control this contamination. In this study, the hydraulic containment method using extraction wells was adopted as the remediation technique. An optimisation model is developed to minimise costs of pumping used for the containment of groundwater contamination. The output from this is used to train a neural network model that has been developed for optimal evolution of pumping strategies. Neural networks are proving to be useful decision-making tools because they are able to store knowledge and can consider nonlinear relationships, fuzzy relations, etc. The optimisation model developed and the neural network model is applied in a case study. The feed forward neural network is adopted with the input nodes storing the water levels at the wells (five observation wells and one pumping well are considered) and the output node storing the optimal pumping rate for these water levels, which is obtained using the optimisation model. This neural network is trained with six input nodes, one output node and eleven nodes in the hidden layer. This neural network is trained with 45 patterns and tested with four patterns. The trained neural network proved to be very useful in making decisions on the number of pumping wells, and in obtaining the optimal pumping rate for each well. The user, on specifying a set of inputs (the water levels in the wells) to the network, can obtain the optimal pumping rate at all the extraction wells in order to ensure that the contaminant plumes have been contained within the specified area. © 2005 EPP Publications Ltd.

Reuse of wastewater for irrigation is a common practice adopted worldwide. Nearly 90% of such activity is done without proper treatment of wastewater. As a result, 10 % of the world population consuming foods produced from such reuse are exposed to a high risk of trace metal accumulation. In addition to human health risk, the lack of proper treatment before reuse may affect the environment polluting soil and groundwater. The study's objective is to evaluate the effect of wastewater irrigation on the transfer of metals from soil to plants and their toxic effect. The soil and plant samples were collected from the wastewater irrigation region, where such practice is done for more than five decades. The Daily Intake level of Metals (DIM) were found in order Cu> Mn> Cr> Sr> Al> Zn> Ba> Ni> Se> Cd> Co> Mo> As representing potential accumulation of these trace elements in human food chain. The BCF (Bioconcentration Factor) of Cu was critical, ranging from 2.5 to 11, and PLI (Pollution Load Index) was found higher in order Cu>Ni>Pb>Se>Zn. The concentration of certain metals (Al, Cu, Mn) was higher in the leaf than in the stem. Among six varieties of plant samples, Alternanthera sessile was found to exhibit good bioaccumulation capacity, followed by Moringa oleifera. The concentration of Cu and Pb was higher than the standards in all the plants.

Nitrate sorption potentials of three surface soils (soils-1-3) were evaluated under different solute concentrations, i.e. 1-100mgL-1. Batch and diffusion-cell adsorption experiments were conducted to delineate the diffusion property and maximum specific nitrate adsorption capacity (MSNAC) of the soils. Ho's pseudo-second order model well fitted the batch adsorption kinetics data (R2>0.99). Subsequently, the MSNAC was estimated using Langmuir and Freundlich isotherms; however, the best-fit was obtained with Langmuir isotherm. Interestingly, the batch adsorption experiments over-estimated the MSNAC of the soils compared with the diffusion-cell tests. On the other hand, a proportionate increase in the MSNAC was observed with the increase in soil organic matter content (OM) under the batch and diffusion-cell tests. Therefore, increasing the soil OM by the application of natural compost could stop nitrate leaching from agricultural fields and also increase the fertility of soil. © 2010 Elsevier Ltd.

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.