A review on emerging artificial intelligence (AI) techniques for air pollution forecasting: Fundamentals, application and performance

A Masood, K Ahmad - Journal of Cleaner Production, 2021 - Elsevier
Accurate air quality forecasting is critical for systematic pollution control as well as public
health and wellness. Most of the traditional forecasting techniques have shown inconsistent …

Modeling and optimization of environment in agricultural greenhouses for improving cleaner and sustainable crop production

Y Guo, H Zhao, S Zhang, Y Wang, D Chow - Journal of Cleaner Production, 2021 - Elsevier
Resource-use efficiency and crop yield are significant factors in the management of
agricultural greenhouse. Appropriate modeling methods effectively improve the control …

Predicting energy consumption using LSTM, multi-layer GRU and drop-GRU neural networks

S Mahjoub, L Chrifi-Alaoui, B Marhic, L Delahoche - Sensors, 2022 - mdpi.com
With the steep rise in the development of smart grids and the current advancement in
developing measuring infrastructure, short term power consumption forecasting has recently …

A combined architecture of multivariate LSTM with Mahalanobis and Z-Score transformations for oil price forecasting

S Urolagin, N Sharma, TK Datta - Energy, 2021 - Elsevier
Oil price plays a vital role in a country's economy. Oil price forecasting helps in making better
economic planning and decisions. The fluctuation in the oil price occurs due to several …

Control and implementation of an energy management strategy for a PV–wind–battery microgrid based on an intelligent prediction algorithm of energy production

S Mahjoub, L Chrifi-Alaoui, S Drid, N Derbel - Energies, 2023 - mdpi.com
This paper describes an energy management strategy for a DC microgrid that utilizes a
hybrid renewable energy system (HRES) composed of a photovoltaic (PV) module, a wind …

A hybrid CNN-Transformer model for ozone concentration prediction

Y Chen, X Chen, A Xu, Q Sun, X Peng - Air Quality, Atmosphere & Health, 2022 - Springer
Ozone concentration has come to the fore as an important air quality indicator. However,
ozone concentrations vary with meteorological conditions and the presence of other …

Comparative study of a mathematical epidemic model, statistical modeling, and deep learning for COVID-19 forecasting and management

M Masum, MA Masud, MI Adnan, H Shahriar… - Socio-Economic …, 2022 - Elsevier
The COVID-19 pandemic has caused a global crisis with 47,209,305 confirmed cases and
1,209,505 confirmed deaths worldwide as of November 2, 2020. Forecasting confirmed …

Forecasting daily temperatures with different time interval data using deep neural networks

S Lee, YS Lee, Y Son - Applied Sciences, 2020 - mdpi.com
Temperature forecasting has been a consistent research topic owing to its significant effect
on daily lives and various industries. However, it is an ever-challenging task because …

Long short term memory (LSTM) recurrent neural network (RNN) for discharge level prediction and forecast in Cimandiri river, Indonesia

Y Sudriani, I Ridwansyah… - IOP Conference series …, 2019 - iopscience.iop.org
Cimandiri watershed in Sukabumi prefecture of West Java, Indonesia, has been used for
profitable activities such as power plant, rafting tourism, drinking water, and municipal …

A new insight for real-time wastewater quality prediction using hybridized kernel-based extreme learning machines with advanced optimization algorithms

J Alavi, AA Ewees, S Ansari, S Shahid… - … Science and Pollution …, 2022 - Springer
Accurate prediction of inlet chemical oxygen demand (COD) is vital for better planning and
management of wastewater treatment plants. The COD values at the inlet follow a complex …