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Hydrologie Data Models
Published in Praveen Kumar, Jay Alameda, Peter Bajcsy, Mike Folk, Momcilo Markus, Hydroinformatics: Data Integrative Approaches in Computation, Analysis, and Modeling, 2005
Benjamin L. Ruddell, Praveen Kumar
With the adoption of standard hydrologie data models, distributed access to data is becoming more streamlined and reliable. Vendors of data are able to store and dispense their data products in a format that everyone can access. For example, the United States Geological Survey (USGS) has adopted the ArcHydro hydrologic data model for its National Hydrographic Dataset (NHD). Government, academic, and industry interests are moving to create a geospatial data infrastructure combining data-modeled databases with distributed Internet-based access. This infrastructure will include web portals (gateways to data) that maintain catalogs of distributed data. Web portals function by taking query-based requests for data, scanning metadata of registered data sources to locate the proper information, and brokering the communication of that data to the client.
An interactive web-based solar energy prediction system using machine learning techniques
Published in Journal of Management Analytics, 2023
Priyanka Chawla, Jerry Zeyu Gao, Teng Gao, Chengchen Luo, Huimin Li, Yiqin We
The proposed work examines the investment potential for each county in California in the previous year from two perspectives: solar radiation (estimated using the suggested model) and profit (derived using solar energy cost and price). Finally, by combining model findings with investment information, this work visualises many perspectives on solar PV investment in each county in California. This research creates a new method for predicting regional solar radiation that could be used to other areas and studies. In addition, an interactive dashboard on a web portal depicts solar energy potential from two perspectives: solar radiation and profit. It assists investors in making more informed regional judgments by utilising a variety of sources. It can also be used to help the California Energy Commission better assess the potential and availability of solar energy production in each area.
Deep E-Learning RecommendNet: An Acute E-Learning Recommendation System with Meta-Heuristic-Based Hybrid Deep Learning Architecture
Published in Cybernetics and Systems, 2022
Pradnya Vaibhav Kulkarni, Rajneeshkaur Sachdeo, Sunil Rai, Rohini Kale
Fung, Lam, and Tam (2014) have attempted to develop an E-learning recommendation system for augmenting web search engines in order to get the personalized recommendations based on E-learning by integrating student’s behaviors and learning competencies over the network. The web interface was the gateway between the organization web portal and the Google search engine. This E-learning recommendation system has been functioned by using the content re-ranking module and the dynamic profiling module of the students. All the records related to the students were recorded in the dynamic profiling module. The re-ranking module has been utilized to prioritize the five most suitable links over the Google search space. The experimental results from this system suggested that the proposed approach has been provided high performance and satisfaction for the students.
Interactive Web Documentaries: A Case Study of Video Viewing Behaviour on iOtok
Published in International Journal of Human–Computer Interaction, 2022
Julie Ducasse, Matjaž Kljun, Nuwan T. Attygalle, Klen Čopič Pucihar
Table 115 shows the number of users and a sum of video views for all videos (total video views) within each user category. Concurrent users account for 89% of all users, which reveals that promotion of the documentary during the first 13 weeks was efficient (Facebook, online news portals, newsletter). Once the active promotion stopped, the number of new users dropped significantly (5411 concurrent users in 3 months vs 679 post-series users in 10 months). Google Analytics data show that 51% of concurrent users accessed the web portal through direct source (e.g., typing the URL, opening a bookmark), compared to 90% for post-series users. In addition, 36% of social media traffic was made by concurrent users compared to 4% by post-series users.