Spatio temporal hydrological extreme forecasting framework using LSTM deep learning model

A Anshuka, R Chandra, AJV Buzacott… - … research and risk …, 2022 - Springer
Hydrological extremes occupy a large spatial extent, with a temporal sequence, both of
which can be influenced by a range of climatological and geographical phenomena …

Stacked transfer learning for tropical cyclone intensity prediction

RV Deo, R Chandra, A Sharma - arXiv preprint arXiv:1708.06539, 2017 - arxiv.org
Tropical cyclone wind-intensity prediction is a challenging task considering drastic changes
climate patterns over the last few decades. In order to develop robust prediction models, one …

Multi-step-ahead cyclone intensity prediction with Bayesian neural networks

R Deo, R Chandra - PRICAI 2019: Trends in Artificial Intelligence: 16th …, 2019 - Springer
The chaotic nature of cyclones makes track and wind-intensity prediction a challenging task.
The complexity in attaining robust and accurate prediction increases with an increase of the …

Consistent robust and recursive estimation of atmospheric motion vectors from satellite images

K Mounika, G Kutty, SSRK Gorthi - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
Atmospheric motion vectors (AMVs) estimation helps in better understanding of atmospheric
dynamics and also plays a key role in weather forecasting. It has been a challenging task …

Development of a Co-evolutionary Radial Basis Function Neural Classifier by a k-Random Opponents Topology

BY Hiew, SC Tan, WS Lim - Emerging Trends in Neuro Engineering and …, 2017 - Springer
The interest of the research in this paper is to introduce a novel competitive co-evolutionary
(ComCoE) radial basis function artificial neural network (RBFANN) for data classification …