Faculty Publications
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Item Crop Yield Analysis using SIF and Climate Variables: A Case Study in Punjab, India(Institute of Electrical and Electronics Engineers Inc., 2022) Gautam, P.K.; Bhattacharjee, S.Regular and faster crop yield prediction can mitigate the extreme effects of severe weather events, such as drought, heavy rainfall, etc. This work explores a precise, scalable, and automatic way to understand rice yield dynamics and its correlation with satellite-based solar-induced fluorescence (SIF), climate variables, such as temperature and rainfall, as they exhibit a significant correlation with rice yield. A district-wise analysis in Punjab, India, is carried out using Pearson correlation coefficient and different regression techniques, such as linear, ridge, lasso, and elastic net for the Kharif season. A comparative study shows that the elastic net performs better than the other models, with the best coefficient of determination (R2) of 0.792 and root mean square error (RMSE) of 300.5 kg/ha. This study can be extended in multiple dimensions by including a variety of crops, climate factors, and multi-satellite SIF data for any crop yield pattern analysis and prediction. © 2022 IEEE.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 Prediction of water quality indices by regression analysis and artificial neural networks(2008) Rene, E.R.; Saidutta, M.B.The quality of wastewater generated in any process industry is generally indicated by performance indices namely BOD, COD and TOC, expressed in mg/L. The use of TOC as an analytical parameter has become more cornmon in recent years especially for the treatment of industrial wastewater. In this study, several empirical relationships were established between BOD and COD with TOC using regression analysis, so that TOC can be used to estimate the accompanying BOD or COD. A new, the use of Artificial Neural Networks has been explored in this study to predict the concentrations of BOD and COD, well in advance using some easily measurable water quality indices. The total data points obtained from a refinery wastewater (143) were divided into a training set consisting of 103 data points, while the remaining 40 were used as the test data. A total of 12 different models (Al-A12) were tested using different combinations of network architecture. These models were evaluated using the % Average Relative Error values of the test set. It was observed that three models gave accurate and reliable results, indicating the versatility of the developed models.Item Prediction of uniaxial compressive strength, tensile strength and porosity of sedimentary rocks using sound level produced during rotary drilling(2011) Rajesh Kumar, B.R.; Vardhan, H.; Govindaraj, M.The main purpose of the study is to develop a general prediction model and to investigate the relationships between sound level produced during drilling and physical properties such as uniaxial compressive strength, tensile strength and percentage porosity of sedimentary rocks. The results were evaluated using the multiple regression analysis taking into account the interaction effects of various predictor variables. Predictor variables selected for the multiple regression model are drill bit diameter, drill bit speed, penetration rate and equivalent sound level produced during rotary drilling (Leq). The constructed models were checked using various prediction performance indices. Consequently, it is possible to say that the constructed models can be used for practical purposes. © Springer-Verlag 2011.Item Development of prediction models for hydrodynamic performance of semicircular breakwater(2012) Aggarwal, A.; Gope, V.K.; Managiri, S.S.; Hegde, A.V.Breakwaters are structures built to protect harbors, shore areas, basins, and other areas from the fury of sea waves. They create calm waters and provide for the safe mooring and handling of ships, as well as protection to harbor facilities. The main function of a breakwater is the formation of an artificial harbor. Of late, certain new types of breakwaters have been constructed to cater to the tranquility requirements of managing marine traffic in ports. The semicircular breakwater (SBW) is one such new type of breakwater. The semicircular breakwater possesses a round top and, thus, offers more stability against the action of waves. It is expected that the SBW will be well suited as an offshore breakwater designed to protect beaches from coastal erosion. A number of experiments were conducted on scaled-down physical models of SBW for different values of parameters such as wave height H, wave period T, spacing of perforations on the seaside, etc. (radius of breakwater and diameter of perforations were kept constant), and data were collected. The paper presents the prediction models/equations for hydrodynamic performance characteristics such as reflection coefficient and relative wave runup, using the data obtained by a regression approach in MATLAB.Item Prediction of penetration rate and sound level produced during percussive drilling using regression and artificial neural network(2012) Kivade, S.B.; Murthy, C.S.N.; Vardhan, H.The main objective of this investigation is to develop a general prediction model and to study the effect of predictor variables such as uniaxial compressive strength, air pressure and thrust on penetration rate and sound level produced during percussive drilling of rocks. The experiment was carried out using three levels Box-Behnken design with full replication in 15 trials. Modeling was done using artificial neural network (ANN) and multipleregression analysis (MRA). These techniques can be utilized for the prediction of process parameters. Comparison of artificial neural network and multiple linear regression models was made and found that error rate was smaller in ANN than that predicted by MRA in terms of sound level and penetration rate. © 2012 CAFET-INNOVA TECHNICAL SOCIETY.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 Determinants of e-participation in the citizens and the government initiatives: Insights from India(Elsevier Ltd, 2016) Alathur, S.; Ilavarasan, P.; Gupta, M.P.The paper attempts to examine the determinants of two types of citizens' e-participation - initiated by the citizens and the government. The factors of e-participation were delineated from a review of democracy and e-participation literature and a regression model was developed. On the basis of 407 responses collected through an online and offline survey among the Indian participants, the model was tested. The analysis showed that the citizens' participation efficacy and values determine e-participation of both types. For the citizens' initiatives freedom to participate and legal support for the participation efforts were also the determining factors. The extant research on types of e-participation services is inadequate. The paper attempted to fill the gap and contributes in i) explaining the importance of facilitating multiple stakeholders' initiatives for improved citizens' participation ii) differentiating determining factors among e-participation initiatives and iii) suggesting policy recommendations for successful e-participation initiatives. The future research can focus on determinants for collaborative service initiatives from the citizens and government. © 2016 Elsevier Ltd.Item Can coffee certification schemes increase incomes of smallholder farmers? Evidence from Jinotega, Nicaragua(Springer Netherlands, 2017) Jena, P.R.; Stellmacher, T.; Grote, U.This paper investigates the impact of Fairtrade and organic certification on household income of smallholder coffee farmers in the Jinotega Municipality of Nicaragua. Using a sample of 233 coffee farming households and employing endogenous switching regression model and propensity score matching method, the results found that Fairtrade and organic certification standards have different effects on the certified farmers; while Fairtrade farmers had experienced yield gains, organic farmers had the price advantage. However, the overall impact of these certification standards on the total household income is found to be statistically not significant. While some of the Fairtrade-certified cooperatives have used the social premium in creating community-level infrastructure, there is a need for more investment. The major constraint the organic-certified farmers face is lack of availability of adequate organic inputs such as manures and organic herbicides. © 2015, Springer Science+Business Media Dordrecht.Item A Critical Comparison of Regression Models and Artificial Neural Networks to Predict Ground Vibrations(Springer International Publishing, 2017) Ram Chandar, K.; Sastry, V.R.; Hegde, C.Blasting is important and an essential prerequisite in any opencast mine for fragmenting hard deposits. Blasting always produces unwanted effects like ground vibrations, noise and fly rock; among which ground vibrations effect is more on surrounding structures. Propagation of ground vibrations can lead to destruction of surrounding structures. Prediction of ground vibrations especially in terms of peak particle velocity is beneficial as opposed to conventional data monitoring techniques which can be expensive as well as time consuming. This paper uses predictors to estimate the intensity of ground vibrations and compares different methods of prediction methods like linear regression, multiple linear regression, non linear regression (NLR) and artificial neural networks. Intensity of ground vibrations generated from blasting operations was monitored in three different mines of limestone, dolomite and coal; obtaining about 168 ground vibration recordings in total. The statistical modelling or data-driven modeling has shown promise in the prediction of blast vibrations. Proposed a system of introducing site specific rock parameters like poison’s ratio, uniaxial compressive strength of rock and Young’s modulus to improve the correlation coefficient using statistical modelling (commonly called feature engineering in machine learning circles). © 2016, Springer International Publishing Switzerland.