Weather Prediction and Classification Using Neural Networks and k-Nearest Neighbors

Autor:	Jhanavi Chaudhary, Kishore Bingi, Harshita Puri, Kulkarni Rakshit Raghavendra, Rhea Mantri
Rok vydání:	2021
Předmět:	Artificial neural network Meteorology Weather condition Weather prediction Weather forecasting Training (meteorology) Humidity computer.software_genre Hybrid model computer Physics::Atmospheric and Oceanic Physics Mathematics k-nearest neighbors algorithm
Zdroj:	2021 8th International Conference on Smart Computing and Communications (ICSCC).
DOI:	10.1109/icscc51209.2021.9528115
Popis:	This paper focuses on developing a weather prediction model to predict temperature and humidity. Further, a classification model is also extended to predict the weather condition using the expected model’s output. The proposed hybrid model can predict the temperature and humidity and forecast future weather conditions. The prediction and classification models are created using neural networks and k-nearest neighbors, respectively. The prediction model’s results have shown the best ability for both the output variables (temperature and humidity) with R2 values close to one and MSE values close to zero. Further, the classification model’s results also showed better execution in classifying the weather conditions with the highest accuracy values.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::f16fbf3c3e258f9462c8195408f4efd3 https://doi.org/10.1109/icscc51209.2021.9528115 Zobrazit plný text záznamu