Faculty Publications
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Item Association of chlorophyll in a multi-parametric shallow marine environment off the Karnataka-Kerala coast(2011) Raghavan, B.R.; Shylini, S.K.; Deepthi, T.; Kumaraswami, M.; Ashwini, S.; Chauhan, P.; Raman, M.; Venkat Reddy, D.Cluster analysis is a useful data analyzing method to obtain detailed information on the state of the shallow marine environment. The objective of this analysis is to appreciate the clustering patterns of the data of case II shallow marine environment of the eastern Arabian Sea. R-mode cluster analysis was resorted to appreciate the relation of the physical, chemical and biological oceanographic parameters represented as dendograms. A set of 14 parameters were retrieved from seven seasons of the coastal waters from Bekal (North Kerala) to Karwar (North Karnataka). This study exhibits diverse clustering patterns reflecting the heterogeneous behavior of the surface waters of the shallow Arabian Sea enforced by the physical, chemical and biological oceanography of the shallow marine environment. © 2011 CAFET-INNOVA technical society. All right reserved.Item Self-optimal clustering technique using optimized threshold function(Institute of Electrical and Electronics Engineers Inc., 2014) Verma, N.K.; Roy, A.This paper presents a self-optimal clustering (SOC) technique which is an advanced version of improved mountain clustering (IMC) technique. The proposed clustering technique is equipped with major changes and modifications in its previous versions of algorithm. SOC is compared with some of the widely used clustering techniques such as K-means, fuzzy C-means, Expectation and Maximization, and K-medoid. Also, the comparison of the proposed technique is shown with IMC and its last updated version. The quantitative and qualitative performances of all these well-known clustering techniques are presented and compared with the aid of case studies and examples on various benchmarked validation indices. SOC has been evaluated via cluster compactness within itself and separation with other clusters. The optimizing factor in the threshold function is computed via interpolation and found to be effective in forming better quality clusters as verified by visual assessment and various standard validation indices like the global silhouette index, partition index, separation index, and Dunn index. © 2007-2012 IEEE.Item An Energy-Efficient Hybrid Clustering Mechanism for Wireless Sensor Network(World Scientific Publishing Co. Pte Ltd wspc@wspc.com.sg, 2015) Muni Venkateswarlu, K.; Kandasamy, A.; Chandrasekaran, K.Valuable energy resources of sensor network should be utilized wisely to prolong network's lifetime. Clustering technique helps wireless sensor network (WSN) to enhance its lifetime by reducing energy consumption on every individual sensor node in the network. In multi-hop data forwarding model, difference in energy consumption among cluster heads (HS) causes hot-spot problem in the network. While data is being transferred, the CH close to base station are burdened with heavy relay traffic from several data routes and tend to die early. Unequal clustering avoids this hot-spot problem by establishing different sized clusters at various levels in the network. Since unequal clustering technique does not control number of CHs it creates, it forms huge number of clusters in the network. This increases hop count between source and destination, and leads to impose more over head on each data forwarding route in the network. Also, rapid variation in cluster size causes imbalance in energy dissipation among clustered nodes in the network. This uneven energy consumption influences network performance and lifetime. In this paper, we present an energy-efficient hybrid clustering mechanism for wireless sensor network using equal and unequal clustering techniques to create limited number of clusters in varied sizes at various level of the network. This avoids hot-spot problem with minimum hop count between the source and destination and achieves uniform energy dissipation between intra-and inter-cluster communication. Simulation results show that the proposed clustering mechanism balances the energy consumption among clusters with its hybrid cluster formation mechanism and elevates sensor network lifetime. © 2015 World Scientific Publishing Company.Item EGA-FMC: Enhanced genetic algorithm-based fuzzy k-modes clustering for categorical data(Inderscience Enterprises Ltd., 2018) Narasimhan, M.; Balasubramanian, B.; Kumar, S.D.; Patil, N.Categorical data clustering is the unsupervised technique of grouping similar objects which have categorical attributes. We propose a genetic algorithm-based fuzzy k-modes categorical data clustering algorithm using multi-objective rank-based selection with enhanced elitism operation. Compactness of the clusters and inter-cluster separation were chosen as objectives to be optimised. During elitism, in every iteration, the best parent chromosomes were identified. The entire population was passed through the selection, crossover and mutation steps. The worst children were then replaced by the best parents. Our method was evaluated on three real-world datasets and resulted in clusters of better quality as compared to current methods with a significant reduction in computation time. Additionally, statistical significance tests were conducted to show the superiority of our approach over other clustering solutions. © © 2018 Inderscience Enterprises Ltd.Item Groundwater quality assessment of urban Bengaluru using multivariate statistical techniques(Springer Verlag, 2018) Gulgundi, M.S.; Shetty, A.Groundwater quality deterioration due to anthropogenic activities has become a subject of prime concern. The objective of the study was to assess the spatial and temporal variations in groundwater quality and to identify the sources in the western half of the Bengaluru city using multivariate statistical techniques. Water quality index rating was calculated for pre and post monsoon seasons to quantify overall water quality for human consumption. The post-monsoon samples show signs of poor quality in drinking purpose compared to pre-monsoon. Cluster analysis (CA), principal component analysis (PCA) and discriminant analysis (DA) were applied to the groundwater quality data measured on 14 parameters from 67 sites distributed across the city. Hierarchical cluster analysis (CA) grouped the 67 sampling stations into two groups, cluster 1 having high pollution and cluster 2 having lesser pollution. Discriminant analysis (DA) was applied to delineate the most meaningful parameters accounting for temporal and spatial variations in groundwater quality of the study area. Temporal DA identified pH as the most important parameter, which discriminates between water quality in the pre-monsoon and post-monsoon seasons and accounts for 72% seasonal assignation of cases. Spatial DA identified Mg, Cl and NO3 as the three most important parameters discriminating between two clusters and accounting for 89% spatial assignation of cases. Principal component analysis was applied to the dataset obtained from the two clusters, which evolved three factors in each cluster, explaining 85.4 and 84% of the total variance, respectively. Varifactors obtained from principal component analysis showed that groundwater quality variation is mainly explained by dissolution of minerals from rock water interactions in the aquifer, effect of anthropogenic activities and ion exchange processes in water. © 2018, The Author(s).Item Hierarchical clustering approaches for flood assessment using multi-sensor satellite images(Taylor and Francis Ltd. michael.wagreich@univie.ac.at, 2019) Senthilnath, J.; Shreyas, P.B.; Rajendra, R.; Sundaram, S.; Kulkarni, S.; Benediktsson, J.A.In this paper, hierarchical clustering methods are used on synthetic aperture radar (SAR) (during the flood) and LISS-III (before the flood) data to analyse damage caused by floods. The flooded and non-flooded regions are extracted from the SAR image while different land cover regions are extracted from the LISS-III image. Initially, the Bayesian information criterion (BIC) is implemented to obtain the constraints for the number of clusters. The optimal cluster centres are then computed using hierarchical clustering approach (i.e. cluster splitting and merging techniques). The cluster splitting techniques such as Iterative Self-Organising Data Technique (ISODATA), Mean Shift Clustering (MSC), Niche Genetic Algorithm (NGA) and Niche Particle Swarm Optimisation (NPSO) were applied on SAR and LISS-III data. The cluster centres obtained from these algorithms are used to group similar data points by using merging method into their respective classes. Further, the results obtained for each method are overlaid to analyse the individual land cover region that is affected by floods. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.Item Trend Analysis of Groundwater Levels and Assessment of Regional Groundwater Drought: Ghataprabha River Basin, India(Springer New York LLC barbara.b.bertram@gsk.com, 2019) Pathak, A.A.; Dodamani, B.M.Groundwater drought is a relatively new concept, particularly in the Indian subcontinent, where groundwater levels are declining rapidly. The present study focuses on understanding the trends in groundwater levels and evaluates regional groundwater drought characteristics in the drought-prone Ghataprabha river basin, India. Cluster analysis was performed on the long-term monthly groundwater levels to classify the wells, and the Mann–Kendall test was accomplished to investigate the annual and seasonal groundwater-level trends. Standardized Groundwater level Index (SGI) was used to evaluate groundwater drought. Significant decreasing trends were observed in more than 61% of the wells in the study area with average decline of 0.21 m. Results of the SGI analysis showed that the wells of clusters 1 and 2 experienced recurrent droughts, which can be attributed to diminishing rainfall and over-exploitation of groundwater resources. The outcome of this study provides valuable information about the long-term behavior of regional groundwater levels which, in turn, helps to establish an operative groundwater management strategy for upcoming droughts. © 2018, International Association for Mathematical Geosciences.Item 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 Connection between Meteorological and Groundwater Drought with Copula-Based Bivariate Frequency Analysis(American Society of Civil Engineers (ASCE), 2021) Pathak, A.A.; Dodamani, B.M.Groundwater is a major resource of freshwater that provides additional resilience to agricultural drought during rainfall deficit and also helps in understanding the nature of the hydrological drought risk of an area. This study investigated the response of groundwater drought to meteorological drought and local aquifer properties by considering monthly groundwater levels of a tropical river basin in India. Further, bivariate frequency analysis was carried out for groundwater drought to develop severity-duration-frequency curves by considering the copula function. Long-term monthly groundwater levels were procured, and cluster analysis was performed on groundwater observations to classify the wells. Standardized Groundwater level Index (SGI) was used to evaluate groundwater drought for each cluster, and the same was compared with the meteorological drought of different association periods. The cluster analysis conveyed that wells can be grouped into three clusters optimally. Based on the comparison of groundwater drought with meteorological drought, it was inferred that SGI is well harmonized with the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) in humid and semiarid regions, respectively. Analysis of hydraulic diffusivity with the autocorrelation structure of SGI emphasizes the crucial role of aquifer characteristics in local groundwater droughts. The results of joint and conditional return periods obtained from bivariate frequency analysis conveyed that high severity and high-duration droughts were more frequent in the well of Clusters 1 as well as Cluster 3 and comparatively less for the well of Cluster 2. The outcome of the study will be helpful to design proactive drought mitigation and preparedness strategies by considering conjunctive use of surface and groundwater. It also provides a framework to evaluate groundwater drought risk in other parts of the world. © 2021 American Society of Civil Engineers.Item Impact of Side Friction on Travel Time Reliability of Urban Public Transit(Springer Science and Business Media Deutschland GmbH, 2021) Harsha, M.M.; Mulangi, R.H.Travel time reliability is the key aspect that indicates the quality of urban public transit service. The studies on travel time reliability of the public transit system in Indian traffic conditions are few. Also, the impact of side friction elements on travel time reliability has not been considered in the previous studies. Hence, the present study aims to quantify the different types of side friction elements and analyse their impact on the travel time reliability of the public bus transit system. The field data consisting of side friction elements, traffic volume, and travel time of public bus transit have been collected and extracted at two different road sections (divided and undivided) in the Mysore city (Karnataka, India) during weekdays and weekends. The data are grouped into static and dynamic side frictions. An approach has been proposed to represent different types of side friction elements with a single index called the Side Friction Index (SFI) using relative weight analysis. Travel time reliability is represented using measures such as Buffer Time Index (BTI), Planning Time Index (PTI), Travel Time Index (TTI) and Reliable Buffer Index (RBI). The impact of side friction on travel time reliability was found to be sensitive to traffic volume, and hence the thresholds for different traffic volume levels have been determined using K-means clustering method. It was observed from relative weight analysis that the static side friction has a higher weightage (0.509 and 0.327 for the undivided road and divided road respectively) than the dynamic side friction elements in describing the variation of travel time. The impact of side friction on reliability measures at different traffic volume levels has been studied and found to have a non-linear (exponential) relationship. The impact of SFI has been observed to be higher on TTI and PTI in comparison with BTI. The study outcomes show that the impact of side friction on TTI and PTI is sensitive to traffic volume, especially at higher traffic volume level and impact of side friction on BTI is less, especially at medium traffic volume level. The inference from the study shows that the impact of side friction elements varies with respect to the type of road (divided and undivided), traffic volume levels, different days of week (weekday and weekend), and different time periods of day. © 2021, Iran University of Science and Technology.