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Volume 7 Issue 4
Dec.  2019
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Muhammad Nauman Malik, Mehdi Murtuza, Iqbal Asif, et al. 2019: Adaptive state estimation of groundwater contaminant boundary input flux in a 2-dimensional aquifer. Journal of Groundwater Science and Engineering, 7(4): 373-382. doi: DOI: 10.19637/j.cnki.2305-7068.2019.04.008
Citation: Muhammad Nauman Malik, Mehdi Murtuza, Iqbal Asif, et al. 2019: Adaptive state estimation of groundwater contaminant boundary input flux in a 2-dimensional aquifer. Journal of Groundwater Science and Engineering, 7(4): 373-382. doi: DOI: 10.19637/j.cnki.2305-7068.2019.04.008

Adaptive state estimation of groundwater contaminant boundary input flux in a 2-dimensional aquifer

doi: DOI: 10.19637/j.cnki.2305-7068.2019.04.008
  • Publish Date: 2019-12-28
  • In many circumstances involving heat and mass transfer issues, it is considered impractical to measure the input flux and the resulting state distribution in the domain. Therefore, the need to develop techniques to provide solutions for such problems and estimate the inverse mass flux becomes imperative. Adaptive state estimator (ASE) is increasingly becoming a popular inverse estimation technique which resolves inverse problems by incorporating the semi-Markovian concept into a Bayesian estimation technique, thereby developing an inverse input and state estimator consisting of a bank of parallel adaptively weighted Kalman filters. The ASE is particularly designed for a system that encompasses independent unknowns and /or random switching of input and measurement biases. The present study describes the scheme to estimate the groundwater input contaminant flux and its transient distribution in a conjectural two-dimensional aquifer by means of ASE, which in particular is because of its unique ability to efficiently handle the process noise giving an estimation of keeping the relative error range within 10% in 2-dimensional problems. Numerical simulation results show that the proposed estimator presents decent estimation performance for both smoothly and abruptly varying input flux scenarios. Results also show that ASE enjoys a better estimation performance than its competitor, Recursive Least Square Estimator (RLSE) due to its larger error tolerance in greater process noise regimes. ASE’s inherent deficiency of being slower than the RLSE, resulting from the complexity of algorithm, was also noticed. The chosen input scenarios are tested to calculate the effect of input area and both estimators show improved results with an increase in input flux area especially as sensors are moved closer to the assumed input location.
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