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Veg-W2TCN: A parallel hybrid forecasting framework for non-stationary time series using wavelet and temporal convolution network model
Long-term vegetation time series (TS) forecasting based on climatic data is one of the most
challenging topics, capable of assisting in advanced estimation and management for …
challenging topics, capable of assisting in advanced estimation and management for …
Intelligent fault diagnosis of rolling bearingbased on deep transfer learning using time-frequency representation
With the expansion of deep learning (DL) and machine learning (ML) methods, fault
diagnosis based on data-driven models has recently become controversial. However, due to …
diagnosis based on data-driven models has recently become controversial. However, due to …
Analyzing Poverty through Intra-Annual Time-Series: A Wavelet Transform Approach
Reducing global poverty is a key objective of the Sustainable Development Goals (SDGs).
Achieving this requires high-frequency, granular data to capture neighborhood-level …
Achieving this requires high-frequency, granular data to capture neighborhood-level …
Desertification detection based on landsat time-series images and variational auto-encoder: application in Jeffera, Tunisia
Desertification detection is a challenging problem because of the dynamic climate change
and human activity. This paper proposes a methodology to detect regions with …
and human activity. This paper proposes a methodology to detect regions with …
Data-Driven Forecasting of Climate Change Impacts on Vegetation for Sustainable Agriculture
Accurate forecasting of climate and vegetation changes through SDG 13 initiatives is critical
for adapting agricultural practices, as it allows farmers to anticipate changes in growing …
for adapting agricultural practices, as it allows farmers to anticipate changes in growing …