S Mohan

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.

This paper reports the results of the study on the performance of low-cost adsorbent such as raw rice husk (RRH) and phosphate treated rice husk (PRH) in removing the heavy metals such as lead, copper, zinc and manganese. The adsorbent materials adopted were found to be an efficient media for the removal of heavy metals in continuous mode using fixed bed column. The column studies were conducted with 10 mg/l of individual and combined metal solution with a flow rate of 20 ml/min with different bed depths such as 10, 20 and 30 cm. The breakthrough time was also found to increase from 1.3 to 3.5 h for Pb(II), 4.0 to 9.0 h for Cu(II), 12.5 to 25.4 h for Zn(II) and 3.0 to 11.3 h for Mn(II) with increase in bed height from 10 to 30 cm for PRH. Different column design parameters like depth of exchange zone, adsorption rate, adsorption capacity, etc. were calculated. It is found that the adsorption capacity and adsorption rate constant were increased and the minimum column bed depth required was reduced when the rice husk is treated with phosphate, when compared with that of RRH. © 2007 Elsevier B.V. All rights reserved.

Reservoirs are built usually to serve multiple purposes, viz. irrigation, municipal and industrial water supply, hydro-power and flood control. Due to high variability of annual rainfall and conflicting demands on scarce water resources, the study and operation of reservoir systems has assumed great significance to meet the short term and long-term requirements. The reservoir managers do not find previous techniques of complex optimization models are difficult to adopt practically. New methods have to be developed which are simple to understand and can be effectively adopted for the existing systems. Any reservoir problem is usually site specific in nature and hence any general modeling methodology cannot be directly applied to study the system behavior. Artificial intelligence approaches are now being adopted to effectively simulate the reservoir system based on the experience of human knowledge and expertise. These methods are simple to understand for the reservoir managers and also may be acceptable since the model is being developed with the output and opinion of experts who are really operating the reservoir system. The Fuzzy rule based systems are very much suitable for inferring developed operating policies. In the present work a rule based fuzzy model is attempted for long-term operation a multi reservoir system. The present model was developed on monthly basis for operation and the model was demonstrated with a case of two serial reservoirs on River Godavari sub system located in South India when they are operated individually. The recent artificial intelligence tools like Genetic programming, Artificial Neural networks and Fuzzy logic are increasingly used as soft computing techniques to address modelling issues. The main advantage of these techniques lies in handling noisy data, addressing non - linear and dynamic systems. These tools also useful when it is difficult to explain the physical relationship are not fully understood in order to enhance the performance of the system. The present paper is aimed to present a fuzzy logic methodology for long-term reservoir operation. In this method a monthly fuzzy rule based model was developed based on the historical operation. The performance of the model was tested with calibrated period and validation period.

Water quality management though a very important part of overall water resources management programs is also unfortunately given the last priority among all the other objectives like water supply for irrigation, drinking purpose, hydropower. flood mitigation etc. hi the present paper a multiobjective waste load allocation (MOWLA) model has been developed. Though there are many MOWLA models with cost minimization and DO maximization, there are very few models which have adequately addressed the issues of equity in waste load allocation models. The present paper includes equity as one of the objective and shows that by introducing equity in the objective function the solution obtained is more fair when compared to the overall cost minimization solution. However the decision maker has different satisfaction levels to different solutions. To address this uncertainty or ambiguity in a decision maker's (DM) satisfaction level, fuzzy membership functions are introduced for each objective function, thus by introducing fuzziness in the objective functions, a modeler can give a wide range solutions from which the DM can select the most satisfying solution for himself. Copyright © 2009 by IICAI.

Transit route network design for urban bus systems involves the selection of a set of routes and the associated frequencies that achieve the desired objective, subject to the operational constraints. This can be formulated as an optimization problem that minimizes the total system cost, which can be expressed as a function of bus operating cost and passenger total travel time. In the first phase of a two-phase solution process, a large set of candidate route is generated using a candidate route generation algorithm. In the second phase, a solution route set is selected from the candidate route set using genetic algorithms, a search and optimization method based on natural genetics. The simultaneous route and frequency coded model proposed in this investigation considers the frequency of the route as the variable, thus differing from the earlier models in terms of coding scheme adopted. A sample study on a medium-sized network has established that the coding scheme adopted for the route network design enhanced the performance of the model.

The paper presents a GIS based spatial decision support system for modelling contaminant intrusion into water distribution system. Three models have been developed to simulate the process and risk of contamination. A seepage model predicts the contaminant zone of pollution sources and the change of concentration during migration through soil. A pipe condition assessment model ranks the condition of water pipe in terms of the potential of contaminant ingress. An ingress model combines the geometry algorithm with contaminant zone to obtain the potential pollution areas of water distribution pipe. The three models were integrated with ArcView GIS for supporting decision making for risk mitigation. Zone VIII of water supply system in Guntur, India was selected for the case study. The contaminant ingress potential and potential pollution area of water pipes were displayed as thematic maps in GIS. The areas resulting in high risk were identified from the GIS maps. The availability of resources for maintenance activities are limited in developing countries. Thus GIS based spatial decision support system helps to achieve maximum risk reduction.