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Monitoring of TDS and conductivity in groundwater in the seismically active region in NW Himalayas,India 
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下载免费PDF全文 Arvind Kumar Surinder Singh Bikramjit Singh Bajwa Sandeep Mahajan Rajeev Kalia Sunil Dhar 《地震科学（英文版）》2010,23(3):295-299
<正>The earthquake precursors and earthquake prediction are the burning issue among the community of earth scientists and engineers. Studies of earthquake precursory phenomena since the last several decades have shown that significant geophysical and geochemical changes may occur prior to intermediate and large earthquakes (Hartmann and Levy, 2005; Yang et al, 2005; 相似文献
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M5 model tree based modelling of reference evapotranspiration 总被引：1，自引：0，他引：1
This paper investigates the potential of M5 model tree based regression approach to model daily reference evapotranspiration using climatic data of Davis station maintained by California irrigation Management Information System (CIMIS). Four inputs including solar radiation, average air temperature, average relative humidity, and average wind speed whereas reference evapotranspiration calculated using a relation provided by the CIMIS was used as output. To compare the performance of M5 model tree in predicting the reference evapotranspiration, FAO–56 Penman–Monteith equation and calibrated Hargreaves–Samani relation was used. A comparison of results suggests that M5 model tree approach works well in comparison to both FAO–56 and calibrated Hargreaves–Samani relations. To judge the generalization capability of M5 model tree approach, model created by using the Davis data set was tested with the datasets of four different sites. Results from this part of the study suggest that M5 model tree could successfully be employed in modeling the reference evapotranspiration. Further, sensitivity analysis with M5 model tree approach suggests the suitability of solar radiation, average air temperature, average relative humidity, and average wind speed as input parameters to model the reference evapotranspiration Copyright © 2009 John Wiley & Sons, Ltd. 相似文献
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Arvind Kumar Vishal Arora Vivek Walia Bikramjit Singh Bajwa Surinder Singh Tsanyao Frank Yang 《Environmental Earth Sciences》2014,72(8):2837-2847
Soil gas radon measurements were made in Chamba and Dharamshala regions of Himachal Pradesh, India, to study the correlation, if any, between the soil gas radon, radium activity concentration of soil, and the geology/active tectonics of the study region. Soil gas radon surveys were conducted around the local fault zones to check their tectonic activities using the soil gas technique. Soil gas radon activity concentration at thirty-five different locations in Dharamshala region has been found to be varying from 13.2 ± 1.5 to 110.8 ± 5.0 kBq m?3 with a geometrical mean of 35.9 kBq m?3 and geometrical standard deviation of 1.8. Radon activity concentration observed in the thirty-seven soil gas samples collected from the Chamba region of Himachal Pradesh varies from 5.2 ± 1.0 to 35.6 ± 2.5 kBq m?3, with geometrical mean of 15.8 kBq m?3 and geometrical standard deviation of 1.6. Average radium activity concentrations in thirty-four soil samples collected from different geological formations of Dharamshala region and Chamba region are found to be 40.4 ± 17 and 38.6 ± 1.7 Bq kg?1, respectively. It has been observed that soil gas radon activity concentration has a wide range of variation in both Dharamshala and Chamba regions, while radium activity concentrations in soil samples are more or less same in both the regions. Moreover, soil gas radon activity concentration has a better positive correlation with the radium activity concentration in soil samples collected from Chamba region as compared to Dharamshala region. 相似文献
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This paper investigates the potential of two variants of extreme learning machine based regression approaches in predicting the resilient modulus of cohesive soils. Support vector regression was used to compare the performance of the proposed extreme learning machine based regression approaches. The dataset used in this study was derived from literature and consists of 9 input parameters with a total of 891 cases. For testing, two methods i.e. train/test and tenfold cross validation was used. In case of train and test methods, a total of 594 randomly selected cases were used to train different algorithms and the remaining 297 data were used to test the created models. Correlation coefficient value of 0.991 (root mean square error = 3.47 MPa) was achieved by polynomial kernel based extreme learning machine in comparison to 0.990 and 0.990 (root mean square error = 4.790 and 4.290 MPa) by simple extreme learning machine and radial basis kernel function based support vector regression respectively with test dataset. Comparisons of results with tenfold cross validation also suggest that polynomial kernel based extreme learning machine works well in terms of root mean square error and computational cost with the used dataset. Sensitivity analysis suggests the importance of confining stress and deviator stress in predicting the resilient modulus when using with polynomial kernel based extreme learning machine modeling approach. 相似文献
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This paper investigates the potential of a Gaussian process (GP) regression approach to predict the load-bearing capacity of piles. Support vector machines (SVM) and empirical relations were used to compare the performance of the GP regression approach. The first dataset used in this study was derived from actual pile-driving records in cohesion-less soil. Out of a total of 94 pieces of data, 59 were used to train and the remaining 35 data were used to test the created models. A radial basis function and Pearson VII function kernels were used with both GP and SVM. The results from this dataset indicate improved performance by GP regression in comparison to SVM and empirical relations. To validate the performance of the GP regression approach, another dataset consisting of 38 pieces of data was considered. The results from this dataset also suggest improved performance by the Pearson VII function kernel-based GP regression modelling approach in comparison to SVM. 相似文献
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