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This work assessed the effects of joint operation on the performance of multipurpose, multireservoir systems vis-à-vis standalone operation. The analysis used the multipurpose Pong and Bhakra reservoirs in northern India. Zone-based rule curves with or without hedging optimized using genetic algorithms were used for operating the reservoirs. The results showed that standalone configurations without hedging produced irrigation water reliability exceeding 90%, but the vulnerability indices (40% for Pong and 58% for Bhakra) were also high. Hedging tempered the standalone vulnerability; however, integrated operation also caused the vulnerability to reduce without hedging. Flood control was also enhanced during the flood season, with flood freeboard increasing by 12 and 3 m, respectively, at Bhakra and Pong reservoirs, relative to nonoptimized operation. Hydropower generation was lower than installed capacity at both reservoirs for both standalone and joint operations. The main conclusion of the study is that although hedging will moderate the vulnerability of standalone systems, the same outcome can be achieved without the deliberate water rationing of hedging if reservoirs are operated jointly. [ABSTRACT FROM AUTHOR]

For efficient irrigation water management and hydro-meteorological studies at both the field and catchment scales, a need exists to evaluate the existing evapotranspiration (ET) estimation methods under varied physiographical and data availability conditions. In this study, using the FAO-56 Penman-Monteith model as the benchmark model, a total of 16 various ET estimation methods were evaluated using both the continuous daily time series and average time series weather data of a subhumid valley cultivated rangeland in the eastern Indian Himalayas. The results revealed that some pan evaporation models have restricted performances. Consequently, for an improved performance, all of these models were standardized using a genetic algorithm-based linear corrector transformation model. Almost all of the models performed reasonably well when using the average daily time series data. Furthermore, the trend analysis of reference ET showed an increasing trend during the months of February to July, including October, and a decreasing trend during August to January, except October, with an annual decreasing rate of . [ABSTRACT FROM AUTHOR]

The optimal design of a groundwater remediation system using the pump-and-treat method is a complex task involving modeling physical phenomena such as groundwater flow and contaminant transport and optimizing several goals of concern, while satisfying bounds on certain parameters. Simulation tools such as the finite-element method (FEM) coupled with optimization tools such as the genetic algorithm (GA) has been found to be an efficient and easy to use methodology to solve such arduous problems. In this study, a simulation model using the FEM for groundwater flow and contaminant transport has been developed and coupled with a multiobjective optimization model based on the nondominated sorting genetic algorithm II (NSGA II). The model is used to minimize the cost optimization function as well as the time period for the remediation of the aquifer subject to bounds on pumping rates, groundwater heads, and concentration levels of the contaminant. The coupled FEM-NSGA II model has been applied for the remediation of a real field aquifer near Vadodara, India for a combination of flushing and pumping to demonstrate the effectiveness of the proposed multiobjective technique for achieving the optimal pumping policy. Total dissolved solids are considered to be the main pollutant. Three alternative remediation design scenarios with different pumping well locations have been compared and the best remediation design scenario has been identified to be the one that gives the best Pareto-optimal front. The FEM-NSGA II in general evolves a set of well-spread and consistent Pareto-optimal solutions, which represent the best designs that identify the trade-off between the expected cost and the time period of remediation for all the remediation design scenarios while satisfying the constraints, thus, giving the decision maker a wide set of choices. [ABSTRACT FROM AUTHOR]

Flood forecasting at Jamtara gauging site of the Ajay River Basin in Jharkhand, India is carried out using an artificial neural network (ANN) model, an adaptive neuro-fuzzy interference system (ANFIS) model, and an adaptive neuro-GA integrated system (ANGIS) model. Relative performances of these models are also compared. Initially the ANN model is developed and is then integrated with fuzzy logic to develop an ANFIS model. Further, the ANN weights are optimized by genetic algorithm (GA) to develop an ANGIS model. For development of these models, 20 rainfall–runoff events are selected, of which 15 are used for model training and five are used for validation. Various performance measures are used to evaluate and compare the performances of different models. For the same input data set ANGIS model predicts flood events with maximum accuracy. ANFIS and ANN model perform similarly in some cases, but ANFIS model predicts better than the ANN model in most of the cases. [ABSTRACT FROM AUTHOR]

The article focuses on the remediation system for groundwater, which was designed using nondominated sorting genetic algorithm II (NSGA II) and finite element model (FEM) for aquifer systems in Vadodara, India. It mentions the two objectives of the said system such as the minimization of remediation time and pumping and treatment costs. It also discusses the link of the objectives on the trade off or Pareto-optimal solutions, which had a discontinuity on the system's three design scenarios.