Journal Articles
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Item Artificial intelligence application in drought assessment, monitoring and forecasting: a review(Springer Science and Business Media Deutschland GmbH, 2022) Kikon, A.; Deka, P.C.Drought is a natural hazard creating havoc on economic, social and environmental aspects. As a result of its slow and creeping nature, it is problematic to establish the onset as well as the termination of drought. Irrespective of its spatial and temporal variability, drought occurs in almost all regions. A wide range of drought studies has been conducted by many researchers over a long period of time. The damage caused by drought has a huge impact on the social, economic and agricultural sectors. Researchers have defined drought in different ways depending upon the parameters and its characteristics, and universally there is no proper definition for drought because of its complexity in nature. This review is focused mainly on various Artificial Intelligence techniques used in drought assessment, monitoring, management and forecasting. The findings from the study shows that drought prediction has become significance in the field of hydrology, Water Resources Management, sustainable agriculture, etc. by using the various AI techniques. In recent studies, AI has been used widely in analysing drought in different regions. The applications of AI techniques in the domain of drought assessing, monitoring, forecasting, etc., shows a rapid growth and that the impact of these will be increasing in future. For understanding the different concepts of drought study, it is needed to establish different system of drought management in order to monitor the different factors affecting drought and then take proper measures to mitigate the damage. Literature studies have been done to analyze the onset and other measures of drought management. Future research may be oriented towards Modeling and probabilistic analysis of climatic data for refining the drought vulnerability mapping, analysis of onset and termination, warning system and drought declaration process depending on the conditions of the region. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.Item Estimating and prediction of turn around time for incidents in application service maintenance projects(Academy Publisher, 2008) Basavaraj, M.J.; Shet, K.C.Application Service Maintenance Projects normally deals with Incidents as First Level support function. Incidents in majority directly link with Production Environment, so Turn around Time for Incidents is a significant factor. Many Companies are having Service Level Agreements with Customer for Turn around Time for Incidents. There is a need to focus on Estimating and Predicting Turn around Time for Incidents. Improvement in Turn around Time helps in improving the Service Level Agreements earlier agreed with the Customer. Saved time can be diverted to other Project Activities like Enhancements or for new requests. This will also helps as one of the paths for Companies to get new business with the Customer. We have used Capability Maturity Model Integration(CMMI)V1.2 Quantitative Project Management(QPM) methodology for Application Service Maintenance(ASM) Projects for estimating and predicting turn around time for incidents. By implementing this best practice in SEI CMMI Level 5 Company we have achieved a significant improvement of approximately 50 percent reduction in Average Turn around Time for incidents. © 2008 Academy Publisher.Item Artificial neural networks model for the prediction of steady state phenol biodegradation in a pulsed plate bioreactor(2008) Shetty K, K.V.; Nandennavar, S.; Srinikethan, G.Background: A recent innovation in fixed film bioreactors is the pulsed plate bioreactor (PPBR) with immobilized cells. The successful development of a theoretical model for this reactor relies on the knowledge of several parameters, which may vary with the process conditions. It may also be a time-consuming and costly task because of their nonlinear nature. Artificial neural networks (ANN) offer the potential of a generic approach to the modeling of nonlinear systems. Results: A feedforward ANN based model for the prediction of steady state percentage degradation of phenol in a PPBR by immobilized cells of Nocardia hydrocarbonoxydans (NCIM 2386) during continuous biodegradation has been developed to correlate the steady state percentage degradation with the flow rate, influent phenol concentration and vibrational velocity (amplitude x frequency). The model used two hidden layers and 53 parameters (weights and biases). The network model was then compared with a Multiple Regression Analysis (MRA) model, derived from the same training data. Further these two models were used to predict the percentage degradation of phenol for blind test data. Conclusions: The performance of the ANN model was superior to that of the MRA model and was found to be an efficient data-driven tool to predict the performance of a PPBR for phenol biodegradation. © 2008 Society of Chemical Industry.Item Hydrodynamics characterization of a counter-current spray column for particulate scrubbing from flue gases(2008) Biswas, S.; Rajmohan, B.; Meikap, B.C.Growing environmental concern and tightening of the regulations for particulate emission from various sources force us to think of an alternative technology for their control, which is cost effective and of high performance. A spray column using a wet process to control the particulates offers design simplicity, and has various other advantages over other conventional equipment used in industry. This work presents the hydrodynamic study of the spray column for the removal of particulates from gaseous wastes. Experiments were carried out to quantify pressure drop (?P), for varied gas and liquid rates ranging from 3.084 × 10-3 to 5.584 × 10-3 Nm3/s and 8.35 × 10-6 to 33.34 × 10 -6 m3/s, respectively with QL/QG ratio ranging from 1.59 to 10.81 m3 per 1000 ACM (actual cubic meter). The maximum pressure drop incurred in the column is 327 N/m3, which is at a gas rate of 5.584 × 10-3 Nm3/s, liquid rate of 33.34 × 10-6 m3/s, and an inlet solid loading range of 0-2.5 kg/m3. This is quite low compared to other wet process-based equipment, thus making it a low power loss scrubber. These results have further demonstrated the impact of solid dust (particulates) on the pressuredrop-hydrodynamics. A correlation was put forward for prediction of the overall pressure drop in the column. The experimental values agreed well with the predicted values, with minimum percentage error and standard deviation. © 2008 Curtin University of Technology and John Wiley & Sons, Ltd.Item Fuzzy logic modeling for groundwater level forecasting of west coast region in India(2011) Dandagala, D.; Deka, P.C.Forecasting the groundwater table in unconfined aquifer is essential for efficient planning of conjunctive use in a basin. In this study, fuzzy logic (FL) models have been developed for groundwater level forecasting in west coast humid region of Karnataka state, India. The FL modeling was carried out to forecast the groundwater table by one week lead time at three different sites over the study area. Mamdani fuzzy inference system was adopted in the present study and finally centroid of area defuzzification method has been applied to obtain crisp output. The results concluded that the FL model performed quite satisfactorily as assessed by various performance indices such as Root mean square error, Coefficient of correlation, and Mean absolute error. © 2011 CAFET-INNOVA TECHNICAL SOCIETY. All rights reserved.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 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 Hybrid wavelet neural network model for improving forecasting accuracy of time series significant wave height(2011) Prahlada, R.; Deka, P.C.Forecasting of a time series ocean wave data for various lead times has been attempted using hybrid wavelet-Artificial neural networks (WLNN) approach in this study. To improve the model performance a wavelet transformation is attached prior to a predictor (ANN) and then analysis has been carried out. Here the wavelet transformation is used to decompose the original significant wave height (Hs) data into its sub signals in the form of approximation coefficients and detail coefficients. Further, these coefficients were fed to ANN as inputs and targets and the results obtained from the hybrid model are then reconstructed to obtain the predicted significant wave heights. The predicted results from the proposed model were compared with the single ANN results. From the results, it is concluded that the proposed model is working efficiently for predicting time series data, and also the error observed at the higher lead time was very less as compared to the single ANN. The effect of decomposition level is also analysed in thisstudy and their influence was observed significantly in the higher lead time forecasting. © 2011 CAFET-INNOVA TECHNICAL SOCIETY.Item Reprint of: “An analytical model for spiral wound reverse osmosis membrane modules: Part II — Experimental validation”(Elsevier B.V., 2011) Sundaramoorthy, S.; Srinivasan, G.; Murthy, D.V.R.This paper presents the experimental studies carried out for validation of a new mathematical model [1] developed for predicting the performance of spiral wound RO modules. Experiments were conducted on a laboratory scale spiral wound RO module taking chlorophenol as a model solute. Experiments were carried out by varying feed flow rate, feed concentration and feed pressure and recording the readings of permeate concentration, retentate flow rate, retentate concentration and retentate pressure. A total of 73 experimental readings were recorded. The membrane transport parameters Aw (solvent transport coefficient) and Bs (solute transport coefficient) and the feed channel friction parameter b were estimated by a graphical technique developed in this work. The mass transfer coefficient k, estimated using the experimental data, was found to be strongly influenced by solvent flux and solute concentration apart from the fluid velocity. Taking the effects of solvent flux, solute concentration and fluid velocity, a new mass transfer correlation for Sherwood number is proposed in this work for the estimation of mass transfer coefficient. Comparison of model predictions with experimental observations demonstrated that the model was capable of predicting permeate concentration within 10% error, retentate rate flow within 4% error and rejection coefficient within 5% error. © 2011 Elsevier B.V.Item Genetic algorithm based support vector machine regression in predicting wave transmission of horizontally interlaced multi-layer moored floating pipe breakwater(Elsevier Ltd, 2012) Patil, S.G.; Mandal, S.; Hegde, A.V.Planning and design of coastal protection works like floating pipe breakwater require information about the performance characteristics of the structure in reducing the wave energy. Several researchers have carried out analytical and numerical studies on floating breakwaters in the past but failed to give a simple mathematical model to predict the wave transmission through floating breakwaters by considering all the boundary conditions. Computational intelligence techniques, such as, Artificial Neural Networks (ANN), fuzzy logic, genetic programming and Support Vector Machine (SVM) are successfully used to solve complex problems. In the present paper, a hybrid Genetic Algorithm Tuned Support Vector Machine Regression (GA-SVMR) model is developed to predict wave transmission of horizontally interlaced multilayer moored floating pipe breakwater (HIMMFPB). Furthermore, optimal SVM and kernel parameters of GA-SVMR models are determined by genetic algorithm. The GA-SVMR model is trained on the data set obtained from experimental wave transmission of HIMMFPB using regular wave flume at Marine Structure Laboratory, National Institute of Technology, Karnataka, Surathkal, Mangalore, India. The results are compared with ANN and Adaptive Neuro-Fuzzy Inference System (ANFIS) models in terms of correlation coefficient, root mean square error and scatter index. Performance of GA-SVMR is found to be reliably superior. b-spline kernel function performs better than other kernel functions for the given set of data. © 2011 Elsevier Ltd. All rights reserved.