Journal Articles
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884
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Item Developing regression models for predicting pan evaporation from climatic data - A comparison of multiple least-squares, principal components, and partial least-squares approaches(2007) Kovoor, G.M.; Nandagiri, L.Regression models for predicting daily pan evaporation depths from climatic data were developed using three multivariate approaches: multiple least-squares regression (MLR), principal components regression (PCR), and partial least-squares (PLS) regression. The objective was to compare the prediction accuracies of regression models developed by these three approaches using historical climatic datasets of four Indian sites that are located in distinctly different climatic regimes. In all cases (three approaches applied to four climatic datasets), regression models were developed using a part of the data and subsequently validated with the remaining data. Results indicated that although performances of the regression models varied from one climate to another, more or less similar prediction accuracies were obtained by all three approaches, and it was difficult to identify the best approach based on performance statistics. However, the final forms of the regression models developed by the three approaches differed substantially from one another. In all cases, the models derived using PLS contained the smallest number of predictor variables; between two to three out of a possible maximum of six predictor variables. The MLR approach yielded models with three to six predictor variables, and PCR models included all six predictor variables. This implies that the PLS regression models are the most parsimonious in terms of input data required for estimating epan from climate variables, and yet yield predictions that are almost as accurate as the more data-intensive MLR and PCR models. © 2007 ASCE.Item Effect of soil parameters on dynamic cone penetration indices of laterite sub-grade soils from India(2009) George, V.; Nageshwar Rao, Ch.; Shivashankar, R.The focus of this study was on correlating the effect of grain-size, maximum dry-density (MDD), field moisture content, and the void ratios on penetration measured using the dynamic cone penetrometer (DCP) for laterite soils blended with fines. Tests were performed on soil samples compacted to MDD for moulding water contents set to the optimum moisture content (OMC), dry of OMC, and wet of OMC un-soaked condition. The results indicated that an increase in the fines-content caused a decrease in the MDD, and an increase in the OMC and the DCP penetration. Regressions were developed correlating various parameters. © Springer Science+Business Media B.V. 2008.Item A comparative study on RBF and NARX based methods for forecasting of groundwater level(2011) Dandagala, D.; Deka, P.C.Evaluation and forecasting of groundwater levels through time series model (s) helps for the sustainable development of groundwater resources. The focus of the present study is on the application of Radial Basis Function (RBF) and Non Linear auto-regressive with exogenous variable (NARX) data driven models to forecast groundwater level for multiple input scenario's and also multiple lead time. Weekly time series groundwater level data has been used as input and the models are developed to forecast one, two, three, four, five and sixth week ahead. Root mean square error (RMSE) and correlation coefficient (Cc) are used for evaluating the accuracy of the models. Based on the comparison of results, it was found that the RBF models are superior to the NARX models in forecasting groundwater level considering RMSE and Cc. The obtained result indicates that the RBF has high performance and consistent upto fourth week lead time and decaying performance for NARX models. Hence, RBF and NARX have the potential in forecasting groundwater level efficiently for multi step lead time. © 2011 CAFET-INNOVA TECHNICAL SOCIETY. All rights reserved.Item Prediction of daily pan evaporation using support vector machines(CAFET INNOVA Technical Society cafetinnova@gmail.com 1-2-18/103, Mohini Mansion, Gagan Mahal Road, Domalguda, Hyderabad 500029, 2014) Pammar, L.; Deka, P.C.Water scarcity globally has lead to severe problems in water management. Understanding the rate of evaporation, from surface water resources is essential for precise management of the water balance. However, evaporation is difficult to measure experimentally due to its nature. Preparing reliable forecasts of evaporation has become an essential element towards efficient water management. The objective of this paper is to predict daily pan evaporation using different kernel functions of Support Vector Machines (SVM's) based regression approach for the meteorological data obtained for the region 'Lake Abaya' which is located in the Great Rift Valley, southern part of Ethiopia. The meteorological parameters considered for study includes daily details of mean-temperature (T), wind speed (W), sunshine hours (Sh), relative humidity (Rh), rainfall (P). Among the kernel functions used for study, the polynomial kernel function proved its credibility by showing improved performance in training and testing periods. The evidence for performance of polynomial kernel function was seen in terms of correlation coefficient (CC) obtained for training and testing is respectively 0.940, 0.956 which is acceptable. © 2014 CAFET-INNOVA TECHNICAL SOCIETY.Item Comparison of various pan-sharpening methods using Quickbird-2 and Landsat-8 imagery(Springer Verlag service@springer.de, 2017) Pushparaj, J.; Hegde, A.V.Pan-sharpening is the process of transferring the spatial resolution of panchromatic (PAN) image to a multispectral (MS) image for producing a single image with high spatial detail and rich spectral information. In this study, PAN and MS imagery of Quickbird-2 and Landsat-8 are fused separately, using ten different pan-sharpening methods such as principal component analysis (PCA), modified-intensity hue saturation (M-IHS), multiplicative, brovey transform (BT), wavelet-principal component analysis (W-PCA), hyperspectral color space (HCS), high-pass filter (HPF), Gram-Schmidt (GS), Fuze Go, and non-subsampled contourlet transform (NSCT). The effectiveness of these techniques is assessed and compared by qualitative analysis and 14 quantitative analysis methods including bias, correlation coefficient (CC), difference in variance (DIV), relative dimensionless global error in synthesis (ERGAS), universal image quality index (Q), relative average spectral error (RASE), root mean square error (RMSE), structural similarity index method (SSIM), signal-to-noise ratio (SNR), peak SNR (PSNR), spatial correlation coefficient (SCC), image entropy (E), and gradient and quality with no reference image (QNR). The results of both analysis types show that the Fuze Go and NSCT produced the best fused image with high spatial detail and rich spectral information followed by the HPF and GS. © 2017, Saudi Society for Geosciences.Item Effect of Soil Parameters on Resilient Modulus Using Cyclic Tri-Axial Tests on Lateritic Subgrade Soils from Dakshina Kannada, India(Springer International Publishing, 2018) Kumar, A.; George, V.Resilient modulus (Mr) of a soil is used as a basic input in the analysis of sub-grade and sub-base in the mechanistic empirical design approaches. The present work focuses on evolving a cost effective approach for the determination of resilient modulus in the laboratory based on tests performed using the CBR method, and the DCP. Lateritic sub-grades in India exhibit wide-ranging variations in strength and stiffness due to varying fines content, and other characteristics. Additionally, soils of lateritic origin with a higher proportion of fines, also called as lithomargic soils, pose difficulties to pavement engineers due to the poor supporting strength. In order to investigate the strength and stiffness of a wide variety of lateritic soils, it was proposed to perform tests on lateritic soils blended with lithomargic fines in this study. The study focuses on correlating the effect of grain-size, maximum dry-density (MDD), and optimum moisture content (OMC) on the resilient modulus (Mr) measured using the cyclic tri-axial test for various blends of lateritic soils. Tests were performed on soil samples compacted to MDD for molding water contents set to the OMC, dry-side of OMC, and the wet-side of OMC. The results indicated that an increase in the fines-content resulted in an increase in the OMC, and a decrease in the MDD and Mr values. Regressions were developed correlating the fines content to the resilient modulus. This study is expected to provide the necessary basis for estimating the strength of a wide variety of lateritic sub-grades based on the fines content. © 2018, Springer International Publishing AG, part of Springer Nature.Item Effect of soil parameters on modulus of resilience based on portable falling weight deflectometer tests on lateritic sub-grade soils(Taylor and Francis Ltd. michael.wagreich@univie.ac.at, 2020) George, V.; Kumar, A.The use of portable falling weight deflectometers (PFWDs) has gained prominence among pavement engineers in the characterisation of soil sub-grade based on modulus of stiffness. The values of modulus of resilience and the corresponding values of the rebound deflection measured using the PFWD are largely influenced by the soil stiffness. The focus of the present investigation is on the study of the effect of soil parameters such as grain size distribution, maximum dry density (MDD) and optimum moisture content (OMC) on the values of modulus of stiffness (E PFWD) obtained using the PFWD for tests performed on lateritic soil blends. Tests were conducted on various blends of lateritic soil samples compacted to MDD at moulding water contents set to the optimum moisture content (OMC), dry-side of optimum at OMC?3% and wet-side of optimum at OMC+3%. The regressions developed between the values of E PFWD and the percentage of fines and the percentage of sand for lateritic and lithomargic soils indicate a strong linear relationship between these variables for tests on un-soaked and soaked soils. The regressions developed will be of immense benefit to pavement engineers in estimating the values of the modulus of resilience for lateritic soils for the design of pavement sub-grades and embankments. © 2017, © 2017 Informa UK Limited, trading as Taylor & Francis Group.Item Satellite-based top-down Lagrangian approach to quantify aerosol emissions over California(John Wiley and Sons Ltd vgorayska@wiley.com Southern Gate Chichester, West Sussex PO19 8SQ, 2020) Nizar, S.; Dodamani, B.M.Accurate forecasting of air quality demands better estimates of aerosol emissions. The accuracy of conventional bottom-up approaches to estimate aerosol emissions depends on the degree to which various influencing parameters are estimated. The availability of satellite observations not only enhances the capability of determining various influencing parameters, but also provides alternate ways of assessing aerosol sources. The present study employs a Lagrangian approach to the Advection Diffusion Equation (ADE) to estimate the transported aerosols and hence the Aerosol Source Strength (ASS) using satellite-measured Aerosol Optical Depth (AOD) and reanalysis wind data. This top-down approach is based on the advection and diffusion of atmospheric aerosols considering wind circulation and atmospheric conditions rather than using indicative parameters. ASS was computed every 3 hr at a 0.25°×0.25° grid across California during July 2018. For the computation, AOD retrievals were obtained from the Geostationary Operational Environmental Satellite (GOES)-16 with observations every 15 min. The data were resampled to the grid every 3 hr, and backward trajectories were run at every gridpoint to ascertain the initial aerosol concentration for the ADE. The final aerosol concentrations obtained from the ADE model were then compared with the observed AOD to obtain the ASS during that time period. The results are indicative of higher ASS around wildfire locations. The ASS values also show good correlation (R2=0.886) with Fire Radiative Power (FRP) obtained from Terra MODIS fire product. The method was further applied to investigate the spatial correlation of ASS with power plant density, which reveals a steady increase in ASS with power plant density (R2=0.82). © 2020 Royal Meteorological SocietyItem Spatial and temporal variations in river water quality of the Middle Ganga Basin using unsupervised machine learning techniques(Springer Science and Business Media Deutschland GmbH, 2020) Krishnaraj, A.; Deka, P.C.In this study, cluster analysis (CA), principal component analysis (PCA) and correlation were applied to access the river water quality status and to understand spatiotemporal patterns in the Ganga River Basin, Uttara Pradesh. The study was carried out using data collected over 12 years (2005–2017) regarding 20 water quality parameters (WQPs) covering spatially from upstream to downstream Ankinghat to Chopan, respectively (20 stations under CWC Middle Ganga Basin). The temporal variations of river water quality were established using the Spearman non-parametric correlation coefficient test (Spearman R). The highest Spearman R (?0.866) was observed for temperature with the season and a very significant p value of (0.0000). The parameters EC, pH, TDS, T, Ca, Cl, HCO3, Mg, NO2 + NO3, SiO2 and DO had a significant correlation with the season (p < 0. 05). K-means clustering algorithm grouped the stations into four different clusters in dry and wet seasons. Based on these clusters, box and whisker plots were generated to study individual clusters in different seasons. The spatial patterns of river WQ on both seasons were examined. PCA was applied to screen out the most significant water quality parameters due to spatial and seasonal variations out of a large data set. It is a data reduction process and a more conventional way of speeding up any machine learning algorithms. A reduced number of three principal components (PCs) were drawn for 20 WQPs with an explained total variance of 75.84% and 80.57% is observed in the dry and wet season, respectively. The parameters DO, EC_ Gen, P-Tot, SO4 are the most dominating parameters with PC score more than 0.8 in the dry season; similarly, TDS, K, COD, Cl, Na, SiO2 in the wet season. The different components of water quality monitoring, such as spatiotemporal patterns, scrutinize the most relevant water quality parameters and monitoring stations are well addressed in this study and could be used for the better management of the Ganga River Basin. © 2020, Springer Nature Switzerland AG.Item A variational pan-sharpening algorithm to enhance the spectral and spatial details(Taylor and Francis Ltd., 2021) Gogineni, R.; Chaturvedi, A.; Daya Sagar, B.S.Pan-sharpening is a remote sensing image fusion technique that generates a high-resolution multispectral (HRMS) image on combining a low resolution multispectral (MS) image and a panchromatic (PAN) image. In this paper, a new optimisation model is proposed for pan-sharpening. The proposed model consists of three terms: (i) a data synthesis fidelity term formulated on inferring the relationship between source MS image and fused image to preserve the spectral information, (ii) a total generalised variation-based prior term to inject the significant spatial details from PAN image to pan-sharpened image, and (iii) a spectral distortion reduction term that exploits the correlation between multispectral image bands. To solve the resultant convex optimisation problem, an efficient and convergence guaranteed operator splitting framework based on the alternating direction method of multipliers (ADMM) algorithm is formulated. Finally, the proposed model is experimentally validated using full-resolution and reduced-resolution data. The pan-sharpened outcomes exhibit the potential of the proposed method in enhancing the spatial and spectral quality. © 2020 Informa UK Limited, trading as Taylor & Francis Group.