The recent occurrence of exotic freshwater fishes in the tributaries of river Ganga basin: abundance, distribution, risk, and conservation issues

2012 ◽  
Vol 32 (4) ◽  
pp. 476-484 ◽  
Author(s):  
Uttam Kumar Sarkar ◽  
Vineet Kumar Dubey ◽  
Atul Kumar Singh ◽  
Brij Kishor Gupta ◽  
Ajay Pandey ◽  
...  
2019 ◽  
pp. 1225-1241 ◽  
Author(s):  
Rabindra K. Barik ◽  
Rojalina Priyadarshini ◽  
Harishchandra Dubey ◽  
Vinay Kumar ◽  
Kunal Mankodiya

Big data analytics with the cloud computing are one of the emerging area for processing and analytics. Fog computing is the paradigm where fog devices help to reduce latency and increase throughput for assisting at the edge of the client. This article discusses the emergence of fog computing for mining analytics in big data from geospatial and medical health applications. This article proposes and develops a fog computing-based framework, i.e. FogLearn. This is for the application of K-means clustering in Ganga River Basin Management and real-world feature data for detecting diabetes patients suffering from diabetes mellitus. The proposed architecture employs machine learning on a deep learning framework for the analysis of pathological feature data that obtained from smart watches worn by the patients with diabetes and geographical parameters of River Ganga basin geospatial database. The results show that fog computing holds an immense promise for the analysis of medical and geospatial big data.


2019 ◽  
pp. 278-297 ◽  
Author(s):  
Rabindra K. Barik

The present research paper proposes and develops a Cloud computing based Spatial Data Infrastructure (SDI) Model named as CloudGanga for sharing, analysis and processing of geospatial data particularly in River Ganga Basin management in India. The main purpose of the CloudGanga is to integrate all the geospatial information such as dam location, well location, irrigation project, hydro power project, canal network and central Water Commission gauge stations locations related to River Ganga. CloudGanga can help the decision maker/ planner or common users to get enough information for their further research and studies. The open source software (Quantum GIS) has been used for the development of geospatial database. QGIS Plugin has been linked with Quantum GIS for invoking cloud computing environment. It has also discussed about the various overlay analysis in CloudGanga environment. In the present research, machine learning approaches are also used in a R tool for well locations which are associated with the basin of River Ganga.


Author(s):  
N. S. Nagpure ◽  
Rashmi Srivastava ◽  
Ravindra Kumar ◽  
Anurag Dabas ◽  
Basdeo Kushwaha ◽  
...  

2021 ◽  
Author(s):  
Laura A. Richards ◽  

<p>In a basin-wide survey of the River Ganga and key tributaries, from the Himalayan source to the Bay of Bengal in India, we aim to improve the conceptual understanding of downstream water quality trends along > 2000 km.  Here we explore the spatial distribution of a suite of inorganic and organic chemicals, nutrients and wastewater indicators to determine the dominant geochemical process controls across the basin.  Sampling was undertaken at 81 sites in the post-monsoon period of 2019.  We use chemical signatures to identify likely sources, characterise potential higher-pollution zones and to determine the relative importance of regional versus localized controls on the observed water quality parameters, including in relation to contaminant type.  The influence from key tributaries is determined.  We seek to unravel the relative importance of mechanisms such as dilution, evaporation, water-rock interactions and anthropogenic inputs in controlling contaminant distribution.  We assess the representativeness of river bank sampling in comparison to cross-river transects in select locations.  We compare our data to historical records across previous annual cycles, noting differences in extent of agreement according to contaminant type.  This coordinated, catchment-wide survey presents a much broader and more comprehensive dataset than typically reported, hence leading to substantially improved process understanding of dominant controls on contaminant distribution across the catchment.  This work may have implications on informing future monitoring efforts and in identifying future remediation priorities.</p><p><strong>Acknowledgements </strong>This research was supported by the NERC-DST Indo-UK Water Quality Programme (NE/R003386/1 and DST/TM/INDO-UK/2K17/55(C) & 55(G) to DP et al; NE/R003106/1 and DST/TM/INDO-UK/2K17/30 to DR et al.), NE/R000131/1 to Jenkins et al. and a Dame Kathleen Ollerenshaw Fellowship (LR).</p>


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