0% Complete
Home
/
14th International Conference on Computer and Knowledge Engineering
Forecasting El Niño Six Months in Advance Utilizing Augmented Convolutional Neural Network
Authors :
Mohammad Naisipour
1
Iraj Saeedpanah
2
Arash Adib
3
Mohammad Hossein Neisi Pour
4
1- Department of Civil Engineering, Faculty of Engineering, University of Zanjan, Iran.
2- Department of Civil Engineering, Faculty of Engineering, University of Zanjan, Iran.
3- Department of Civil Engineering Civil Engineering and Architecture Faculty Shahid Chamran University of Ahvaz, Iran
4- Department of Computer Engineering. Sharif University of Technology. Tehran, Iran
Keywords :
ACNN،El Niño،Forecast،SST،Augmentation
Abstract :
The ability of predicting climate phenomena enables international organization and governments to manage natural disasters such as droughts. El Niño Sothern Oscillation (ENSO) is one the most influential and crucial phenomenon follows with large scale climatic events and can be used for predicting droughts and floods in different parts of the earth. Due to such a great importance, a new Convolutional Neural Network method based on augmented data (ACNN) for predicting ENSO on a relatively long period is developed in this research. The method is developed based on CNN to forecast ENSO six month earlier. Sea Surface Temperature (SST) anomaly maps are given to the model as the predictors and Niño 3.4 Index is the predictand. The method applies convolutional tensors to extract features from the maps, and delivers them to a fully connected neural network to discover connections between Niño Index and the features. A tricky augmentation process is used to increase the number of input data to compensate lack of observations. The model represents reliable prediction as it compared with observations for a long period to ensure the validity and reliability of the method. The relatively low computation cost of the method makes it a great tool for predicting ENSO and its following consequences even for related institutions in low income countries.
Papers List
List of archived papers
Multi-Layer Collaborative Graph with BPR Similarity Embedding for Recommender System
Mostafa Ghorbani - Azadeh Mansouri
Extracting Major Topics of COVID-19 Related Tweets
Faezeh Azizi - Hamed Vahdat-Nejad - Hamideh Hajiabadi - Mohammad Hossein Khosravi
Deep Learning-Based Malaysian Sign Language (MSL) Recognition: Exploring the Impact of Color Spaces
Ervin Gubin Moung - Precilla Fiona Suwek - Maisarah Mohd Sufian - Valentino Liaw - Ali Farzamnia - Wei Leong Khong
Area-Efficient VLSI Implementation of Bit-Serial Multiplier Using Polynomial Basis over GF(2m)
Saeideh Nabipour - Javad Javidan - Gholamreza Zare Fatin
Diagnosis of Depression Based on New Features Extractive from the Frequency Space of the EEG
Melika Changizi - Saeid Rashidi
Classification of COVID-19 and Nodule in CT Images using Deep Convolutional Neural Network
Amirhossein Ghaemi - Seyyed Amir Mousavi mobarakeh - Habibollah Danyali - Kamran Kazemi
Artificial Intelligence applications addressing different aspects of the Covid-19 crisis and key technological solutions for future epidemics control
Nadia Khalili - Hojatollah Hamidi
Exploring 3D Transfer Learning CNN Models for Alzheimer’s Disease Diagnosis from MRI Images
Fatemehsadat Ghanadi Ladani - Hamidreza Baradaran Kashani
Parallel Local Feature Selection For High-dimensional Data
Zhaleh Manbari - Chiman Salavati - Fardin AkhlaghianTab - Barzan Saeedpoor - Himan Delbina - Mahmud Abdulla Mohammad
Sotfware defined content popularity estimation for wireless D2D caching networks
Maede Rezaei - AhmadReza Montazerolghaem
more
Samin Hamayesh - Version 42.4.1