A transdisciplinary review of deep learning research and its relevance for water resources scientists
C Shen - Water Resources Research, 2018 - Wiley Online Library
Deep learning (DL), a new generation of artificial neural network research, has transformed
industries, daily lives, and various scientific disciplines in recent years. DL represents …
industries, daily lives, and various scientific disciplines in recent years. DL represents …
A review of deep learning models for time series prediction
Z Han, J Zhao, H Leung, KF Ma… - IEEE Sensors Journal, 2019 - ieeexplore.ieee.org
In order to approximate the underlying process of temporal data, time series prediction has
been a hot research topic for decades. Develo** predictive models plays an important role …
been a hot research topic for decades. Develo** predictive models plays an important role …
Survey of computational intelligence as basis to big flood management: Challenges, research directions and future work
Flooding produces debris and waste including liquids, dead animal bodies and hazardous
materials such as hospital waste. Debris causes serious threats to people's health and can …
materials such as hospital waste. Debris causes serious threats to people's health and can …
Improving artificial intelligence models accuracy for monthly streamflow forecasting using grey Wolf optimization (GWO) algorithm
Monthly streamflow forecasting is required for short-and long-term water resources
management especially in extreme events such as flood and drought. Therefore, there is …
management especially in extreme events such as flood and drought. Therefore, there is …
Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibratory signals
Fault diagnosis is an effective tool to guarantee safe operations in gearboxes. Acoustic and
vibratory measurements in such mechanical devices are all sensitive to the existence of …
vibratory measurements in such mechanical devices are all sensitive to the existence of …
GeoAI for large-scale image analysis and machine vision: recent progress of artificial intelligence in geography
GeoAI, or geospatial artificial intelligence, has become a trending topic and the frontier for
spatial analytics in Geography. Although much progress has been made in exploring the …
spatial analytics in Geography. Although much progress has been made in exploring the …
Deep learning convolutional neural network in rainfall–runoff modelling
Rainfall–runoff modelling is complicated due to numerous complex interactions and
feedback in the water cycle among precipitation and evapotranspiration processes, and also …
feedback in the water cycle among precipitation and evapotranspiration processes, and also …
Decomposition ensemble model based on variational mode decomposition and long short-term memory for streamflow forecasting
G Zuo, J Luo, N Wang, Y Lian, X He - Journal of Hydrology, 2020 - Elsevier
Reliable and accurate streamflow forecasting is vital for water resource management. Many
streamflow prediction studies have demonstrated the excellent prediction ability of …
streamflow prediction studies have demonstrated the excellent prediction ability of …
Prolongation of SMAP to spatiotemporally seamless coverage of continental US using a deep learning neural network
Abstract The Soil Moisture Active Passive (SMAP) mission has delivered valuable sensing of
surface soil moisture since 2015. However, it has a short time span and irregular revisit …
surface soil moisture since 2015. However, it has a short time span and irregular revisit …
A new-structure grey Verhulst model for China's tight gas production forecasting
Tight gas, shale gas and coalbed gas are recognized as the three sources of
unconventional natural gas in the world. Currently, China's tight gas production is at an …
unconventional natural gas in the world. Currently, China's tight gas production is at an …