Yannic Lops

发表

A real-time hourly ozone prediction system using deep convolutional neural network

Yunsoo Choi, Ebrahim Eslami, Yannic Lops, 2019, Neural Computing and Applications.

A Novel CMAQ-CNN Hybrid Model to Forecast Hourly Surface-Ozone Concentrations Fourteen Days in Advance pdf

Ebrahim Eslami, Yannic Lops, Alqamah Sayeed, 2020, ArXiv.

Real-time 7-day forecast of pollen counts using a deep convolutional neural network

Ebrahim Eslami, Yannic Lops, Alqamah Sayeed, 2019, Neural Computing and Applications.

Using wavelet transform and dynamic time warping to identify the limitations of the CNN model as an air quality forecasting system

Yunsoo Choi, Ebrahim Eslami, Yannic Lops, 2020, Geoscientific Model Development.

CAMQ-AI: A computationally efficient deep learning model to improve CMAQ performance over the United States

Yunsoo Choi, Ebrahim Eslami, Yannic Lops, 2019 .

Using a deep convolutional neural network to predict 2017 ozone concentrations, 24 hours in advance

Ebrahim Eslami, Yannic Lops, Alqamah Sayeed, 2020, Neural Networks.

A data ensemble approach for real-time air quality forecasting using extremely randomized trees and deep neural networks

Yunsoo Choi, Ebrahim Eslami, Yannic Lops, 2019, Neural Computing and Applications.

Efficient PM2.5 forecasting using geographical correlation based on integrated deep learning algorithms

Yannic Lops, Alqamah Sayeed, Inchoon Yeo, 2021, Neural Computing and Applications.

A Deep Convolutional Neural Network Model for Improving WRF Simulations

Ebrahim Eslami, Yannic Lops, Alqamah Sayeed, 2020, IEEE Transactions on Neural Networks and Learning Systems.

A novel CMAQ-CNN hybrid model to forecast hourly surface-ozone concentrations 14 days in advance

Ebrahim Eslami, Yannic Lops, Alqamah Sayeed, 2020, Scientific Reports.