Machine learning in agricultural and applied economics
This review presents machine learning (ML) approaches from an applied economist's
perspective. We first introduce the key ML methods drawing connections to econometric …
perspective. We first introduce the key ML methods drawing connections to econometric …
Seven-layer deep neural network based on sparse autoencoder for voxelwise detection of cerebral microbleed
In order to detect the cerebral microbleed (CMB) voxels within brain, we used susceptibility
weighted imaging to scan the subjects. Then, we used undersampling to solve the accuracy …
weighted imaging to scan the subjects. Then, we used undersampling to solve the accuracy …
Forecasting foreign exchange volatility using deep learning autoencoder‐LSTM techniques
Since the breakdown of the Bretton Woods system in the early 1970s, the foreign exchange
(FX) market has become an important focus of both academic and practical research. There …
(FX) market has become an important focus of both academic and practical research. There …
Forecasting monthly pan evaporation using hybrid additive regression and data-driven models in a semi-arid environment
Exact estimation of evaporation rates is very important in a proper planning and efficient
operation of water resources projects and agricultural activities. Evaporation is affected by …
operation of water resources projects and agricultural activities. Evaporation is affected by …
Prediction of the Indian summer monsoon using a stacked autoencoder and ensemble regression model
The study of climatic variables that govern the Indian summer monsoon has been widely
explored. In this work, we use a non-linear deep learning-based feature reduction scheme …
explored. In this work, we use a non-linear deep learning-based feature reduction scheme …
[HTML][HTML] Long-lead statistical forecasts of the Indian summer monsoon rainfall based on causal precursors
Skillful forecasts of the Indian summer monsoon rainfall (ISMR) at long lead times (4–5
months in advance) pose great challenges due to strong internal variability of the monsoon …
months in advance) pose great challenges due to strong internal variability of the monsoon …
Deep learning based short-range forecasting of Indian summer monsoon rainfall using earth observation and ground station datasets
We develop a deep learning model (DL) for Indian Summer Monsoon (ISM) short-range
precipitation forecasting using a ConvLSTM network. The model is built using daily …
precipitation forecasting using a ConvLSTM network. The model is built using daily …
Prediction of heat waves using meteorological variables in diverse regions of Iran with advanced machine learning models
Climate change has caused a rise in temperature extremes, particularly heatwaves, in
recent decades. Physical-empirical models are developed in this study using two classical …
recent decades. Physical-empirical models are developed in this study using two classical …
[HTML][HTML] A machine learning approach to modeling tropical cyclone wind field uncertainty
T Loridan, RP Crompton… - Monthly Weather …, 2017 - journals.ametsoc.org
Tropical cyclone (TC) risk assessment models and probabilistic forecasting systems rely on
large ensembles to simulate the track trajectories, intensities, and spatial distributions of …
large ensembles to simulate the track trajectories, intensities, and spatial distributions of …
Deep learning for predicting the monsoon over the homogeneous regions of India
Indian monsoon varies in its nature over the geographical regions. Predicting the rainfall not
just at the national level, but at the regional level is an important task. In this article, we used …
just at the national level, but at the regional level is an important task. In this article, we used …