Abstract
Groundwater asset is the most critical freshwater asset. A large portion of the number of inhabitants in our nation relies upon this asset for their essential needs of water. Groundwater has assumed a noteworthy part in expanding nourishment generation and accomplishing sustenance security. Groundwater, an inexhaustible wellspring of water, has an exceedingly reliable water supply for horticulture, household, business, and mechanical needs. Groundwater sullying is a significant issue in our reality. Optimal groundwater monitoring network design models are developed to determine the mass estimation error of contaminant concentration over different management time periods in groundwater aquifers. The objective of the paper is to determine the mass estimation error of contamination concentration at 2.5 years and 7.5 years. The mass estimation error of contamination concentration over time is determined by using the various computer software such as Method of Characteristics (MOC, USGS), Surfer 7.0, and Simulated Annealing (SA). The Method of Characteristics (MOC) is used in this model to solve the solute-transport equation. Simulated annealing is a worldwide improvement strategy that is utilized to locate optimal monitoring well locations. The error of the estimated concentration at potential well locations is extrapolated over the entire study area by geostatistical instrument, kriging. The outlined observing system is dynamic in nature, as it gives time-shifting system plans to various management periods. The optimal monitoring wells design incorporates budgetary constraints in the form of limits on the number of monitoring wells installed in any particular management period. The solution results are evaluated for an illustrative study area comprising of a hypothetical aquifer. The performance evaluation results establish the potential applicability of the proposed methodology for the optimal design of the dynamic monitoring networks for determining the mass estimation error of contamination concentration.
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Reference
Cieniawski SE, Eheart JW, Ranjithan S (1995) Using hereditary calculations to explain a multi objective groundwater observing issue. Water Resour Res 31(2):399–409
Deb K (2002) Optimization for building outline, 2nd edn. Prentice Hall of India, New Delhi. ISBN 978-8120346789
Loaiciga HA (1989) An optimization approach for groundwater quality monitoring network design. Water Resour Res 25(8):1771–1782
McKinney DC, Loucks DP (1992) Network plan for foreseeing groundwater sullying. Water Resour Res 28(1):133–147
Meyer PD, Brill ED (1988) A strategy for finding wells in a groundwater checking system under states of vulnerability. Water Resour Res 24(8):1277–1282
Meyer PD, Valocchi AJ, Eheart JW (1994) Monitoring system configuration to give beginning location of groundwater defilement. Water Resour Res 30(9):2647–2659
Pinder GF, Bredehoeft JD (1968) Utilization of the computerized PC for aquifer assessments. Water Resour Res 4(5):1069–1093
Reed P, Minsker B, Valocchi AJ (2000) Financially savvy long haul groundwater checking configuration utilizing a hereditary calculation and worldwide mass introduction. Water Resour Res 36(12):3731–3741
Singh D (2008) Optimal monitoring network design for contamination detection and sequential characterization of contaminant olumes with feedback information using simulated annealing and linked kriging. Ph.D. thesis submitted at Indian Institute of Technology, Kanpur, India
Singh D (2015) Groundwater monitoring network design: an optimal approach. L. A. Distributing, Deutschland
Singh D, Datta B (2014) Optimal groundwater monitoring network design for pollution plume estimation with active sources. J Geomate 6(2):864–869
Singh D, Datta B (2016) Linked optimization model for groundwater monitoring network design. In: Urban hydrology, watershed management and socio-economic aspects. Springer International Publishing, pp 107–125. ISBN 978-3-319-40194-2
Zheng C, Wang PP (1999) MT3DMS: a modular three-dimensional multispecies transport model for simulation of advection, dispersion, and chemical reactions of contaminants in groundwater systems; documentation and user’s guide total 169 pages, Contract Report SERDP-99-1. US Army Engineer Research and Development Center, Vicksburg, Mississippi
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Gangwar, S., Chauhan, M.S., Singh, D. (2021). A Supervising Grid Model for Identification of Groundwater Pollute. In: Chauhan, M.S., Ojha, C.S.P. (eds) The Ganga River Basin: A Hydrometeorological Approach. Society of Earth Scientists Series. Springer, Cham. https://doi.org/10.1007/978-3-030-60869-9_4
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DOI: https://doi.org/10.1007/978-3-030-60869-9_4
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