A review on ANN based model for solar radiation and wind speed prediction with real-time data
Wind speed and solar radiation are the fundamental inputs used as a renewable energy
source. Both parameters are highly non-linear and environmental dependent. Hence …
source. Both parameters are highly non-linear and environmental dependent. Hence …
[HTML][HTML] Temporal convolutional neural (TCN) network for an effective weather forecasting using time-series data from the local weather station
Non-predictive or inaccurate weather forecasting can severely impact the community of
users such as farmers. Numerical weather prediction models run in major weather …
users such as farmers. Numerical weather prediction models run in major weather …
[HTML][HTML] Crop yield prediction using deep neural networks
Crop yield is a highly complex trait determined by multiple factors such as genotype,
environment, and their interactions. Accurate yield prediction requires fundamental …
environment, and their interactions. Accurate yield prediction requires fundamental …
[HTML][HTML] 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 …
Implementing a novel deep learning technique for rainfall forecasting via climatic variables: An approach via hierarchical clustering analysis
Variations in rainfall negatively affect crop productivity and impose severe climatic
conditions in developing regions. Studies that focus on climatic variations such as variability …
conditions in developing regions. Studies that focus on climatic variations such as variability …
Gated graph recurrent neural networks
Graph processes exhibit a temporal structure determined by the sequence index and and a
spatial structure determined by the graph support. To learn from graph processes, an …
spatial structure determined by the graph support. To learn from graph processes, an …
Weather forecasting model using artificial neural network
K Abhishek, MP Singh, S Ghosh, A Anand - Procedia Technology, 2012 - Elsevier
Weather forecasting has become an important field of research in the last few decades. In
most of the cases the researcher had attempted to establish a linear relationship between …
most of the cases the researcher had attempted to establish a linear relationship between …
An evaluation of CNN and ANN in prediction weather forecasting: A review
Artificial intelligence through deep neural networks is now widely used in a variety of
applications that have profoundly altered human livelihoods in a variety of ways. People's …
applications that have profoundly altered human livelihoods in a variety of ways. People's …
A rainfall prediction model using artificial neural network
The multilayered artificial neural network with learning by back-propagation algorithm
configuration is the most common in use, due to of its ease in training. It is estimated that …
configuration is the most common in use, due to of its ease in training. It is estimated that …
[PDF][PDF] A survey on rainfall prediction using artificial neural network
Rainfall prediction is one of the most important and challenging task in the modern world. In
general, climate and rainfall are highly non-linear and complicated phenomena, which …
general, climate and rainfall are highly non-linear and complicated phenomena, which …