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Evaluation of Hantush's S Function Estimation Methods for Predicting Rise in Water Table

机译:汉班函数估计方法预测水位升高的评价

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Hantush's model is widely used for predicting rise in water table in response to groundwater recharge. Several approximate methods of the Hantush mound function, S(, ) have been developed to overcome the limitations of Hantush's tabulated values of the S(, ) function. These approximate methods have their own advantages and disadvantages, and it is difficult to identify the most accurate and computationally efficient S(, ) estimation method. In this study, performance of four different algebraic approximate S(, ) estimation methods are compared with the Hantush method using the published data. The four different methods considered are Swamee and Ohja (1997) (SO), Singh (2012) (SI), Vatankhah (2013) (VA) and Gauss-Legendre quadrature (GL) method with various Gaussian points (GP). Seven statistical accuracy and computation efficiency indicators are used to assess the performance of different S(, ) estimation methods. The GL method with 100 to16 GPs is found to be the most accurate S(, ) estimation method. This is followed by the SO, GL with 14 to 12 GPs, VA, GL with 10 GP, SI, and GL with 9 to 3 GPs. A good trade off between accuracy and efficiency is found with the SO, VA, and GL method with 14, 12 and 10 GPs. Comprehensive analysis of different S(, ) estimation methods, and their ranking based on overall performance index will be helpful in modelling water table rise due to groundwater recharge, optimum design of recharge basin, and evaluation of effectiveness of recharge basins in groundwater recharging.
机译:汉班的模型广泛用于预测水表中的升高,以应对地下水充电。已经开发了几种汉班Mound函数,S(,)的近似方法以克服汉尚的表格值的函数的局限性。这些近似方法具有自身的优点和缺点,很难识别最准确和计算的高效的S(,)估计方法。在本研究中,将四种不同代数近似S(,)估计方法的性能与使用已发布的数据的汉申方法进行比较。考虑了四种不同的方法是Swamee和Ohja(1997)(So),Singh(2012)(Si),VataMaNHAH(2013)(VA)和高斯 - Legendre Quadrature(GL)方法,具有各种高斯分数(GP)。七种统计准确性和计算效率指标用于评估不同S(,)估计方法的性能。发现具有100〜16 GPS的GL方法是最精确的S(,)估计方法。其次是所以,GL与14至12 GPS,VA,GL,具有10 GP,SI和GL,具有9至3 GPS。使用14,12和10 GPS的SO,VA和GL方法发现了精度和效率之间的良好折衷。不同S(,)估计方法的综合分析及其基于整体绩效指标的排名将有所帮助,由于地下水补给,充电盆地的最佳设计,以及对地下水再充电的充电盆地有效性的评价。

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