Deep learning for processing and analysis of remote sensing big data: A technical review
In recent years, the rapid development of Earth observation technology has produced an
increasing growth in remote sensing big data, posing serious challenges for effective and …
increasing growth in remote sensing big data, posing serious challenges for effective and …
[HTML][HTML] A review on deep sequential models for forecasting time series data
Deep sequential (DS) models are extensively employed for forecasting time series data
since the dawn of the deep learning era, and they provide forecasts for the values required …
since the dawn of the deep learning era, and they provide forecasts for the values required …
Deep learning-based effective fine-grained weather forecasting model
It is well-known that numerical weather prediction (NWP) models require considerable
computer power to solve complex mathematical equations to obtain a forecast based on …
computer power to solve complex mathematical equations to obtain a forecast based on …
LoRa based intelligent soil and weather condition monitoring with internet of things for precision agriculture in smart cities
Urbanization is expected to hold about 50% of the world population by 2050 and there will
be stress on available resources including food and freshwater. Further, inefficient utilization …
be stress on available resources including food and freshwater. Further, inefficient utilization …
Metaheuristic evolutionary deep learning model based on temporal convolutional network, improved aquila optimizer and random forest for rainfall-runoff simulation …
X Qiao, T Peng, N Sun, C Zhang, Q Liu, Y Zhang… - Expert Systems with …, 2023 - Elsevier
Accurate and reliable runoff prediction is of great significance to water resources
management, disaster monitoring and rational development and utilization of water …
management, disaster monitoring and rational development and utilization of water …
IncepTCN: A new deep temporal convolutional network combined with dictionary learning for strong cultural noise elimination of controlled-source electromagnetic …
When the controlled-source electromagnetic (CSEM) data are contaminated by intense
cultural noise and the signal-to-noise ratio (S/N) is lower than 0 dB, the existing denoising …
cultural noise and the signal-to-noise ratio (S/N) is lower than 0 dB, the existing denoising …
DeepSTF: A deep spatial–temporal forecast model of taxi flow
Taxi flow forecast is significant for planning transportation and allocating basic transportation
resources. The flow forecast in the urban adjacent area is different from the fixed-point flow …
resources. The flow forecast in the urban adjacent area is different from the fixed-point flow …
An improved temporal convolutional network with attention mechanism for photovoltaic generation forecasting
Z Zhang, J Wang, D Wei, Y Xia - Engineering Applications of Artificial …, 2023 - Elsevier
Forecasting renewable energy generation is challenging due to non-stationary and intricate
stochastic properties, with a significant impact on grid operation. Although various methods …
stochastic properties, with a significant impact on grid operation. Although various methods …
Traffic flow forecasting in the covid-19: A deep spatial-temporal model based on discrete wavelet transformation
Traffic flow prediction has always been the focus of research in the field of Intelligent
Transportation Systems, which is conducive to the more reasonable allocation of basic …
Transportation Systems, which is conducive to the more reasonable allocation of basic …
Dual memory scale network for multi-step time series forecasting in thermal environment of aquaculture facility: A case study of recirculating aquaculture water …
Multi-step time series forecasting is essential in engineering. However, effective time series
prediction of the agricultural environment is still a challenge due to the disturbance of …
prediction of the agricultural environment is still a challenge due to the disturbance of …