Data-Driven Weather Forecast Using Deep Convolution Neural Network

Priya Sharma, Ashish Patel, Pratik Shah, Soma Senroy

2023

Abstract

Weather forecasting is an important task for the meteorological department as it has a direct impact on the day-to-day lives of people and the economy of a country. India is a diverse country in terms of geographical conditions like rivers, terrains, forests, and deserts. For the weather forecasting problem, we have taken the state of Madhya Pradesh as a case study. The current state of the art for weather forecasting is numerical weather prediction (NWP), which takes a long time and a lot of computing power to make predictions. In this paper, we have introduced a data-driven model based on a deep convolutional neural network, i.e., U-Net. The model takes weather features as input and nowcasts those features. The climate parameters considered for weather forecasting are 2m-Temperature, mean sea level pressure, surface pressure, wind velocity, model terrain height, intensity of solar radiation, and relative humidity. The model can predict weather parameters for the next 6 hours. The results are encouraging and satisfactory, given the acceptable tolerances in prediction.

Paper Citation

in Harvard Style

Sharma P., Patel A., Shah P. and Senroy S. (2023). Data-Driven Weather Forecast Using Deep Convolution Neural Network. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-623-1, pages 853-860. DOI: 10.5220/0011785200003393

in Bibtex Style

@conference{icaart23,
author={Priya Sharma and Ashish Patel and Pratik Shah and Soma Senroy},
title={Data-Driven Weather Forecast Using Deep Convolution Neural Network},
booktitle={Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2023},
pages={853-860},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011785200003393},
isbn={978-989-758-623-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - Data-Driven Weather Forecast Using Deep Convolution Neural Network
SN - 978-989-758-623-1
AU - Sharma P.
AU - Patel A.
AU - Shah P.
AU - Senroy S.
PY - 2023
SP - 853
EP - 860
DO - 10.5220/0011785200003393