Weather Forecasting Accuracy Enhancement Using Random Forests Algorithm

Title

Weather Forecasting Accuracy Enhancement Using Random Forests Algorithm

Creator

Jahnavi A.; Tanni N.; Jayapandian N.

Description

In today's world, weather forecasting is essential for decision-making in a variety of fields, including agriculture, transportation, and disaster preparedness. It's not simple to make weather predictions. Today, both in business and academia, data analytics is growing in importance as a tool for decision-making. The adoption of data-driven concepts is for our graduates, enhancing their marketability. Data Analytics us a study belonging to science that analyses gathered raw data, which makes conclusions about the particular information. Data analytics has been used by many sectors recently, such as hospitality, where this industry can collect data, find out where the problem is, and manage to fix the problem. Nominal, ordinal, interval, and ratio data levels are the four types of data measurement. Applications of data analytics can be found in many industries, including shipping and logistics, manufacturing, security, education, healthcare, and web development. Any business that wants to succeed in the modern digital economy should make analytics a core focus. To make such data meaningful, a transformation engine was used with types from several sources. Ironically, this has made analytics harder for businesses. As businesses employ more platforms and applications, the amount of data available has grown tremendously. This article focuses on different applications of data analytics in the modern world. Weather forecasting is a highly intricate and multifaceted process that draws upon data from various sources. It relies on a combination of scientific studies and sophisticated weather models to decipher the vast amount of information available. 2023 IEEE.

Source

International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023 - Proceedings, pp. 765-770.

Date

2023-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Data Analytics; Deep Learning; Intelligence; Machine Learning; Random Forest; Regression; Weather Forecasting

Coverage

Jahnavi A., Christ (Deemed to Be University), Department of Cse, India; Tanni N., Christ (Deemed to Be University), Department of Cse, India; Jayapandian N., Christ (Deemed to Be University), Department of Cse, India

Rights

Restricted Access

Relation

ISBN: 979-835030085-7

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICSSAS57918.2023.10331690

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85181162298&doi=10.1109%2fICSSAS57918.2023.10331690&partnerID=40&md5=3c07568540f0b9585c6164b1a469e9f7