FACT: Autoencoder and Attention Conv-LSTM-Based Collaborative Framework for Cloud Cover Prediction

Manan Patel, Sudeep Tanwar, Nilesh Kumar Jadav, Rajesh Gupta, Giovanni Pau, Gulshan Sharma, Fayez Alqahtani, Amr Tolba

Research output: Contribution to journalArticlepeer-review

Abstract

Cloud cover forecasting, as an essential paradigm of research, facilitates several applications for various fields, such as weather forecasting, agriculture, aviation, and climate modeling. Nonetheless, despite the accurate predictions, cloud dynamics and its complexity worsen the performance of existing works. Therefore, we propose FACT, a novel framework that utilizes an attention-based convolutional long short-term memory (ConvLSTM) architecture to perform the prediction for the next frame of clouds using a time series dataset of satellite images of cloud cover for a country. Furthermore, an autoencoder model is considered to improve the prediction performance by encoding the frames. The encoding approach helps to reduce the computational complexity of the prediction model, further maintaining enhanced accuracy. Next, we apply post-processing techniques to the acquired prediction result by thresholding the pixel intensity values to produce sharper and clearer cloud images. The proposed model is evaluated and analyzed using various performance assessment metrics, such as Mean Squared Error (MSE), Structural Similarity Index (SSIM), and Peak Signal-to-Noise Ratio (PSNR) metrics. A minimum MSE loss of 0.30 (30%) is achieved. Thus, it shows that the proposed model outperforms existing literature by improving the prediction in the domain of cloud cover forecasting.

Original languageEnglish
Pages (from-to)131488-131504
Number of pages17
JournalIEEE Access
Volume12
DOIs
Publication statusPublished - 2024

Keywords

  • Cloud cover forecasting
  • artificial intelligence: attention-based ConvLSTM
  • autoencoder
  • deep learning
  • satellite image

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering

Fingerprint

Dive into the research topics of 'FACT: Autoencoder and Attention Conv-LSTM-Based Collaborative Framework for Cloud Cover Prediction'. Together they form a unique fingerprint.

Cite this