Deep learning modelling techniques: current progress, applications, advantages, and challenges
Deep learning (DL) is revolutionizing evidence-based decision-making techniques that can
be applied across various sectors. Specifically, it possesses the ability to utilize two or more …
be applied across various sectors. Specifically, it possesses the ability to utilize two or more …
Application of artificial intelligence in geotechnical engineering: A state-of-the-art review
Geotechnical engineering deals with soils and rocks and their use in engineering
constructions. By their nature, soils and rocks exhibit complex behaviours and a high level of …
constructions. By their nature, soils and rocks exhibit complex behaviours and a high level of …
Research on particle swarm optimization in LSTM neural networks for rainfall-runoff simulation
Y Xu, C Hu, Q Wu, S Jian, Z Li, Y Chen, G Zhang… - Journal of …, 2022 - Elsevier
Flood forecasting is an essential non-engineering measure for flood prevention and disaster
reduction. Many models have been developed to study the complex and highly random …
reduction. Many models have been developed to study the complex and highly random …
Forecast methods for time series data: a survey
Z Liu, Z Zhu, J Gao, C Xu - Ieee Access, 2021 - ieeexplore.ieee.org
Research on forecasting methods of time series data has become one of the hot spots. More
and more time series data are produced in various fields. It provides data for the research of …
and more time series data are produced in various fields. It provides data for the research of …
Artificial intelligence for climate prediction of extremes: State of the art, challenges, and future perspectives
Extreme events such as heat waves and cold spells, droughts, heavy rain, and storms are
particularly challenging to predict accurately due to their rarity and chaotic nature, and …
particularly challenging to predict accurately due to their rarity and chaotic nature, and …
[HTML][HTML] Using a long short-term memory (LSTM) neural network to boost river streamflow forecasts over the western United States
Accurate river streamflow forecasts are a vital tool in the fields of water security, flood
preparation and agriculture, as well as in industry more generally. Traditional physics-based …
preparation and agriculture, as well as in industry more generally. Traditional physics-based …
A global-scale investigation of stochastic similarities in marginal distribution and dependence structure of key hydrological-cycle processes
To seek stochastic analogies in key processes related to the hydrological cycle, an extended
collection of several billions of data values from hundred thousands of worldwide stations is …
collection of several billions of data values from hundred thousands of worldwide stations is …
Recurrent neural network model for high-speed train vibration prediction from time series
In this article, we want to discuss the use of deep learning model to predict potential
vibrations of high-speed trains. In our research, we have tested and developed deep …
vibrations of high-speed trains. In our research, we have tested and developed deep …
Time series forecasting (tsf) using various deep learning models
Time Series Forecasting (TSF) is used to predict the target variables at a future time point
based on the learning from previous time points. To keep the problem tractable, learning …
based on the learning from previous time points. To keep the problem tractable, learning …
Forecasting of water level in multiple temperate lakes using machine learning models
Due to global climate change and growing population, fresh water resources are becoming
more vulnerable to pollution. Protecting fresh water resources, especially lakes and the …
more vulnerable to pollution. Protecting fresh water resources, especially lakes and the …