A Review on GeoSpatial Artificial Intelligence Techniques to Handle Missing Data

Registration ID: IJNRD_200724

Published ID: IJNRD2307040

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NUKALA NAGA SAI VENKAT , OLIVER ISAIAH , KARTHIK CHITTETI , KOUSHIK L

Geospatial Artificial Intelligence(geoAI), Environ- mental Epidemiology (EE), Geographical Information Systems (GIS), Artificial Intelligence, Machine Learning (ML)

Geospatial Artificial Intelligence (GeoAI) is emerging to the top of its game with its wide range of applications. In this paper, we are going to review the present trends in GeoAI, machine technologies, and the most common challenges that we come across when using temporal data for predicting Environmental Epidemiologies (EE) most precisely in air quality prediction. Geospatial Artificial Intelligence (GeoAI), which is an emerging scientific discipline, combines innovations in spatial science, deep learning, data mining, and temporal data to extract knowledge from spatial big data. Spatial data and spatial science play a huge role in understanding and visualizing real-world phenomena based on their locations. Spatial scientists use Geographical Information Systems (GIS) to achieve patterns in spatial big data. These GeoAI technologies provide an important advantage for exposure modeling in environmental epidemiology.

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NUKALA NAGA SAI VENKAT, OLIVER ISAIAH, KARTHIK CHITTETI, & KOUSHIK L (July-2023). A Review on GeoSpatial Artificial Intelligence Techniques to Handle Missing Data. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(7), a311-a316. https://ijnrd.org/papers/IJNRD2307040.pdf

Volume 8 Issue 7, July-2023

Pages : a311-a316

Paper Reg. ID: IJNRD_200724

Published Paper Id: IJNRD2307040

Downloads: 000121990

Research Area: Engineering

Country: Vijayawada, Andhra Pradesh, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2307040.pdf

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Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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