[HTML][HTML] Artificial intelligence technologies for forecasting air pollution and human health: a narrative review

S Subramaniam, N Raju, A Ganesan, N Rajavel… - Sustainability, 2022 - mdpi.com
Air pollution is a major issue all over the world because of its impacts on the environment
and human beings. The present review discussed the sources and impacts of pollutants on …

A review of modern machine learning techniques in the prediction of remaining useful life of lithium-ion batteries

P Sharma, BJ Bora - Batteries, 2022 - mdpi.com
The intense increase in air pollution caused by vehicular emissions is one of the main
causes of changing weather patterns and deteriorating health conditions. Furthermore …

[HTML][HTML] Compressive strength prediction of fly ash-based geopolymer concrete via advanced machine learning techniques

A Ahmad, W Ahmad, F Aslam, P Joyklad - Case Studies in Construction …, 2022 - Elsevier
Concrete is a widely used construction material, and cement is its main constituent.
Production and utilization of cement severely affect the environment due to the emission of …

Using soil library hyperspectral reflectance and machine learning to predict soil organic carbon: Assessing potential of airborne and spaceborne optical soil sensing

S Wang, K Guan, C Zhang, DK Lee… - Remote Sensing of …, 2022 - Elsevier
Soil organic carbon (SOC) is a key variable to determine soil functioning, ecosystem
services, and global carbon cycles. Spectroscopy, particularly optical hyperspectral …

[HTML][HTML] An LSTM neural network for improving wheat yield estimates by integrating remote sensing data and meteorological data in the Guanzhong Plain, PR China

H Tian, P Wang, K Tansey, J Zhang, S Zhang… - Agricultural and Forest …, 2021 - Elsevier
Crop growth condition and production play an important role in food management and
economic development. Therefore, estimating yield accurately and timely is of vital …

A novel model to predict significant wave height based on long short-term memory network

S Fan, N **ao, S Dong - Ocean Engineering, 2020 - Elsevier
A long short-term memory (LSTM) network is proposed for the quick prediction of significant
wave height with higher accuracy than conventional neural network. The LSTM network is …

Prediction of hourly air temperature based on CNN–LSTM

J Hou, Y Wang, J Zhou, Q Tian - Geomatics, Natural Hazards and …, 2022 - Taylor & Francis
The prediction accuracy of hourly air temperature is generally poor because of random
changes, long time series, and the nonlinear relationship between temperature and other …

Prediction and reconstruction of ocean wave heights based on bathymetric data using LSTM neural networks

C Jörges, C Berkenbrink, B Stumpe - Ocean Engineering, 2021 - Elsevier
Since climate change impacts threaten the coastal regions of the North Sea, consistent sea
state time series are essential for building coastal protection or offshore structures. Vast …

Artificial neural networks in drought prediction in the 21st century–A scientometric analysis

A Dikshit, B Pradhan, M Santosh - Applied Soft Computing, 2022 - Elsevier
Droughts are the most spatially complex geohazard, which often lasts for years, thereby
severely impacting socio-economic sectors. One of the critical aspects of drought studies is …

An improved SPEI drought forecasting approach using the long short-term memory neural network

A Dikshit, B Pradhan, A Huete - Journal of environmental management, 2021 - Elsevier
Droughts are slow-moving natural hazards that gradually spread over large areas and
capable of extending to continental scales, leading to severe socio-economic damage. A …