Shishir Gaur

Application of Artificial Neural Networks and Particle Swarm Optimization for the Management of Groundwater Resources

Ground management problems are typically solved by the simulation-optimization approach where complex numerical models are used to simulate the groundwater flow and/or contamination transport. These numerical models take a lot of time to solve the management problems and hence become computationally expensive. In this study, Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) models were developed and coupled for the management of groundwater of Dore river basin in France. The Analytic Element Method (AEM) based flow model was developed and used to generate the dataset for the training and testing of the ANN model. This developed ANN-PSO model was applied to minimize the pumping cost of the wells, including cost of the pipe line. The discharge and location of the pumping wells were taken as the decision variable and the ANN-PSO model was applied to find out the optimal location of the wells. The results of the ANN-PSO model are found similar to the results obtained by AEM-PSO model. The results show that the ANN model can reduce the computational burden significantly as it is able to analyze different scenarios, and the ANN-PSO model is capable of identifying the optimal location of wells efficiently.

Combined use of groundwater modeling and potential zone analysis for management of groundwater

A methodology for groundwater evaluation has been developed by the combined use of numerical model and spatial modeling using GIS. The developed methodology has been applied on the sub-basin of the Banganga River, India. Initially, the groundwater potential zones have been delineated by spatial modeling. Different thematic maps of the basin like geology, geomorphology, soil, drainage, slope factor and landuse/landcover have been used to identify the groundwater potential zones. Further, the groundwater flow model for the study area has been developed in the MODFLOW. The groundwater flow vector map has been developed and superimposed on the potential zone map to validate the results of spatial modeling. Finally, the different scenarios have been conceptualized by varying the discharge of the wells and purposing the location for new rainwater harvesting structures. Results reveal that increasing the discharge of the wells in the potential zones put less stress on the aquifer. The suggested locations of rainwater harvesting structures also help to reduce the overall decline of groundwater in the area. The hydrological and spatial modeling presented in this study is highly useful for the evaluation of groundwater resources and for deciding the location of rainwater harvesting structures in semi-arid regions.

Storm-water management through Infiltration trenches

With urbanization, the permeable soil surface area through which recharge by infiltration can occur is reducing. This is resulting in much less ground-water recharge and greatly increased surface run-off. Infiltration devices, which redirect run-off waters from the surface to the sub-surface environments, are commonly adopted to mitigate the negative hydrologic effects associated with urbanization. An infiltration trench alone or in combination with other storm water management practice is a key element in present day sustainable urban drainage systems. A solution for the infiltration rate from an infiltration trench and, consequently, the time required to empty the trench is presented. The solution is in the form of integrals of complicated functions and requires numerical computation. The solution is useful in quantifying infiltration rate and/or artificial recharge of ground-water through infiltration trenches and the drain time of the trench, which is a key parameter in operation of storm water manage...

Prediction of ground water levels in the uplands of a tropical coastal riparian wetland using artificial neural networks

Artificial Neural Networks (ANNs) have been found to be a robust tool to model many non-linear hydrological processes. The present study aims at evaluating the performance of ANN in simulating and predicting ground water levels in the uplands of a tropical coastal riparian wetland. The study involves comparison of two network architectures, Feed Forward Neural Network (FFNN) and Recurrent Neural Network (RNN) trained under five algorithms namely Levenberg Marquardt algorithm, Resilient Back propagation algorithm, BFGS Quasi Newton algorithm, Scaled Conjugate Gradient algorithm, and Fletcher Reeves Conjugate Gradient algorithm by simulating the water levels in a well in the study area. The study is analyzed in two cases-one with four inputs to the networks and two with eight inputs to the networks. The two networks-five algorithms in both the cases are compared to determine the best performing combination that could simulate and predict the process satisfactorily. Ad Hoc (Trial and Error) method is followed in optimizing network structure in all cases. On the whole, it is noticed from the results that the Artificial Neural Networks have simulated and predicted the water levels in the well with fair accuracy. This is evident from low values of Normalized Root Mean Square Error and Relative Root Mean Square Error and high values of Nash-Sutcliffe Efficiency Index and Correlation Coefficient (which are taken as the performance measures to calibrate the networks) calculated after the analysis. On comparison of ground water levels predicted with those at the observation well, FFNN trained with Fletcher Reeves Conjugate Gradient algorithm taken four inputs has outperformed all other combinations.

Multi-Criteria Decision Analysis for Identifying a Suitable Location for Groundwater Pumping Wells

The paper presents the methodology for the combined use of GIS-based multi-criteria analysis and simulation-optimisation modelling for management of the groundwater resources of the Dore river basin in France. The study identifies the suitable location and maximum discharge for the new groundwater pumping wells. The multi-criteria analysis (MCA), with the help of GIS-based geospatial analysis, was performed to identify those areas suitable for pumping wells by considering different criteria, such as hydraulic conductivity, land use, river aquifer exchange, depth to water, and geomorphology. Different criteria were selected with the help of regional experts and stakeholders. For the study area, the groundwater flow model was developed. Further, new pumping wells in the suitable zones, those identified by MCA, were considered and a simulation-optimisation technique was used to identify the maximum discharge from those wells. Finally, the results obtained from both the methods were to finalise the potential zone. The developed methodology proves to be a more realistic approach to identifying new locations for pumping wells.