Spatial Variation in the Grain Size Characteristics of Sediments in Ramganga River, Ganga Basin, India

2019 ◽  
pp. 2485-2495
Author(s):  
Mohd Yawar Ali Khan
Keyword(s):  
Grain Size ◽  
Spatial Variation ◽  
Ganga Basin ◽  
Ramganga River ◽  
River Ganga

Channel planform dynamics of lower Ramganga River, Ganga basin, GIS and remote sensing analyses

2019 ◽  
Vol 35 (9) ◽  
pp. 934-953 ◽  
Author(s):  
Ashwani Kumar Agnihotri ◽  
Anurag Ohri ◽  
Sachin Mishra
Keyword(s):  
Remote Sensing ◽  
Ganga Basin ◽  
Gis And Remote Sensing ◽  
Ramganga River ◽  
River Ganga ◽  
Channel Planform

FogLearn

2019 ◽  
pp. 1225-1241 ◽  
Author(s):  
Rabindra K. Barik ◽  
Rojalina Priyadarshini ◽  
Harishchandra Dubey ◽  
Vinay Kumar ◽  
Kunal Mankodiya
Keyword(s):  
Big Data ◽  
Fog Computing ◽  
Big Data Analytics ◽  
River Basin Management ◽  
Pathological Feature ◽  
Ganga Basin ◽  
Learning Framework ◽  
Geospatial Database ◽  
Patients With Diabetes ◽  
River Ganga

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.

CloudGanga

2019 ◽  
pp. 278-297 ◽  
Author(s):  
Rabindra K. Barik
Keyword(s):  
Cloud Computing ◽  
Spatial Data ◽  
Learning Approaches ◽  
Geospatial Information ◽  
Ganga Basin ◽  
Hydro Power ◽  
Data Infrastructure ◽  
Irrigation Project ◽  
Well Location ◽  
River Ganga

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.

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