Faculty Publications
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Item Simulation of varada aquifer system for sustainable groundwater development(2008) Ramesh, H.; Mahesha, A.Groundwater flow modeling has been used extensively worldwide with varying degrees of success. The ability to predict the groundwater flow is critical in planning and implementing groundwater development projects under increasing demand for fresh water resources. This paper presents the simulation of the aquifer system for planning the groundwater development of Varada basin, Karnataka, India using the Galerkin finite-element method. The government of Karnataka State, India is implementing the World Bank assisted project, "Jal Nirmal" for a sustainable development of the region, thereby ensuring a safe supply of drinking water to the northern districts of the state. Varada basin is one of the beneficiaries of the project in Haveri district. Field tests carried out in the study area indicate that the region is predominantly a confined aquifer with transmissivity and storage coefficients ranging from 5.787×10-6m2/s (0.500 m2/day) to 4.213×10-3m2/s (3.640×102m2/day) and 0.011-0.001× 10-2, respectively. This study mainly emphasizes the spatial and temporal variability of groundwater potential under different developmental scenarios. The model predictions were reasonably good with correlation coefficients ranging from 0.78 to 0.91 with the root mean square error of about 0.46-0.78 during calibration and validation. The stated accuracies are based on comparisons between measured and calculated heads. The outcome of the study would be a useful input for the conjunctive use of surface water and groundwater planning for the sustainable development of the region. © 2008 ASCE.Item Tropical, Seasonal River Basin Development through a Series of Vented Dams(2011) Shetkar, R.V.; Mahesha, A.Tropical rivers are predominantly seasonal in nature, and managing water resources during the deficit period is becoming more difficult because of the rapidly increasing demand for water. The present investigation focuses on harvesting Netravathi River water in the southern Indian peninsula through a series of vented dams with an estimated storage capacity of 102 Mm3 for use during the deficit period. A brief hydraulic design of a vented dam at a specific location is presented. The spacing and capacity of these reservoirs were worked out on the basis of the dam height and the river characteristics. The proposed vented dams are seasonal dams, and the closure of the vents will be decided on the flow available (i.e., 95% dependable flow), the storage capacity, and the minimum water release required for the downstream ecosystem. The appropriate time to start storing water in the vented dams was estimated to be in the month of November, and the entire process of storing water in the vented dams may last for about 41 days. An operational protocol for the storing process is presented. The investigations of aquifer parameters were performed by using electrical resistivity, pumping, and soil tests. The results indicated that the aquifer is shallow, unconfined in nature, and had a depth ranging from 18 to 30 m and hydraulic conductivity ranging from 62.6 to 406 m/day. A multiple regression model developed to assess the groundwater recharge in the adjoining well fields indicated that water table fluctuations may be 30% of reservoir level fluctuations. Because the river is also tidal in nature, a saltwater exclusion dam is proposed at the lower reaches of the river to prevent the entry of saltwater along the river during the summer period. © 2011 American Society of Civil Engineers.Item Assessment of Bi-Decadal Groundwater Fluctuations in a Coastal Region Using Innovative Trends and Singular Spectrum Analysis(Springer, 2023) Krishnan, C.; Mahesha, A.Coastal areas are among the densely populated regions in the world with growing population and subsequent increasing demands for water. Understanding the long-term variations in available water resources aids in efficient water conservation, management and allocation strategies. The present study investigated the long-term trends in groundwater depths (GWDs) for pre-monsoon and post-monsoon seasons in the coastal district of Kollam during the period 1996∼2017, where groundwater is the primary source for domestic and agricultural uses during summer season. The trends examined using the modified Mann Kendall (mMK), innovative trend analysis (ITA) and Sen’s slope estimator indicated a decreasing pre-monsoon GWD trends at an average of −0.5m/decade in 63% of the wells, while increasing post-monsoon GWD trends at an average rate of +0.43m/decade in 72% of the wells at 5% significance level. The singular spectrum analysis (SSA) captured monotonic as well as non-monotonic trend trajectories for the GWDs. About 41% wells exhibited a correlation below — 0.5 (p<0.05) between post-monsoon GWDs and JJASO (June, July, August, September and October) rainfall totals. The increasing post-monsoon GWDs could be related to recent changes in the southwest monsoon patterns over the peninsular India. Adequate planning and management of existing water resources could impart better control on water conservation strategies under the scenario of climate change. © 2023, Geological Society of India, Bengaluru, India.Item Groundwater level modeling using Augmented Artificial Ecosystem Optimization(Elsevier B.V., 2023) Nguyen, N.; Deb Barma, S.D.; van Lam, T.; Kisi, O.; Mahesha, A.Nature-inspired optimization is an active area of research in the artificial intelligence (AI) field and has recently been adopted in hydrology for the calibration (training) of both process-based and statistical models. This study proposes an improved AI model, Augmented Artificial Ecosystem Optimization-based Multi-Layer Perceptron (AAEO-MLP), to build a monthly groundwater level (GWL) forecasting model. AAEO-MLP model is built on the novel Augmented version of Artificial Ecosystem Optimization and traditional MLP network. In AAEO, Levy-flight trajectory and Gaussian random are utilized in exploration and exploitation to improve the optimizing ability. The AAEO-MLP model is tested on two time-series (1989–2012) datasets collected at two wells in India. Various explanatory variables such as monthly cumulative precipitation, mean temperature, tidal height, and previous measurements of GWL were considered for predicting 1-month ahead of GWL. The performance of AAEO-MLP was benchmarked against 17 different models (original AEO, 3 different variants of AEO, and 13 well-known models) in terms of forecasting accuracy based on six metrics (e.g., mean absolute error, root mean square error, Kling–Gupta efficiency, normalized Nash–Sutcliffe efficiency, Pearson's correlation index, a20 index). Furthermore, convergence analysis and model stability are employed to indicate the effectiveness of AAEO-MLP. The compared results express that the AAEO-MLP is superior to other models in terms of prediction accuracy, convergence, and stability. Overall, the results depict that the AAEO is a promising approach for optimizing machine learning models (e.g., MLP) and should be explored for other hydrological forecasting applications (e.g., streamflow, rainfall) to further assess its strengths over existing methods. © 2022 Elsevier B.V.