The efficient and effective search for the optimum design solution of a water distribution network using genetic algorithms (GAs) is governed by several factors such as representation scheme, population size, hydraulic simulation model, fitness function, penalty method, GA operators, number of generations, and more importantly the size of the search space. This paper proposes a modified GA that uses basic operators along with their derivatives randomly. Further, a methodology based on critical path method is suggested to reduce the search space. A software tool, GA-WAT, based on the proposed methodology is developed and first tested and verified for its efficiency and effectiveness on two previously published single source networks. Later, it is applied to the optimal design of a larger, two-source hypothetical network. The results obtained indicate that the modified GA with reduction in search space proposed herein is more effective, especially for large practical networks.

In context of increasing traffic noise in urban India, the objective of the research study is to assess noise due to heterogeneous traffic conditions and the impact of honking on it. Traffic volume, noise levels, honking, road geometry and vehicular speed were measured on national highway, major and minor roads in Nagpur, India. Initial study showed lack of correlation between traffic volume and equivalent noise due to some factors, later identified as honking, road geometry and vehicular speed. Further, frequency analysis of traffic noise showed that honking contributed an additional 2 to 5 dB (A) noise, which is quite significant. Vehicular speed was also found to increase traffic noise. Statistical method of analysis of variance (ANOVA) confirms that frequent honking (p < 0.01) and vehicular speed (p < 0.05) have substantial impact on traffic noise apart from traffic volume and type of road. The study suggests that honking must also be a component in traffic noise assessment and to identify and monitor “No Honking” zones in urban agglomerations.

Security of water distribution networks (WDNs) through early warning systems is one of the topics of research over the last two decades to safeguard human health and the environment against accidental and intentional contamination. Several methodologies have been suggested by different researchers for placement of sensors in WDNs which can provide early warning. Owing to the cost of both placing and maintaining the sensors, the number of these must be limited. This constraint makes the sensor deployment locations crucial in early warning systems. In this paper various methodologies suggested for sensor placements have been broadly classified based on the number of performance objectives as single and multi-objective sensor location problems. Various objectives and their significance in the context of sensor placement strategy have been discussed. A complete review of literature is presented to understand complexities in the sensor design problems and to determine the directions in which research is heading. Several challenges for the design of robust sensor network for large WDNs with less computational efforts are discussed and recommendations are presented.

Online sensors in water distribution networks primarily serves two purposes: (1) Assures quality of water delivered to consumers; (2) Early detection of contamination events so as to minimize its consequences. Most of the multi-objective techniques consider the second purpose and almost ignore the first purpose. In this study, a sensor placement problem is formulated to cover these two performance objectives simultaneously through maximization of: (1) Demand coverage; and (2) Time-constrained detection likelihood. These two objectives are combined into a single objective by using weights. Genetic Algorithm (GA) is used to obtain optimum sensor locations. The methodology is applied on a bench mark problem. Several solutions are obtained by varying the weights of two objectives. A simple method as an alternative to GA is suggested for sensor locations in large water distribution networks for reducing the computational efforts. Comparison of the two methods showed that the proposed simple method provided solutions close to that provided by GA.