Convolutional Neural Network Emulators for DGVMs - A Supervised Machine Learning Approach to Big Data Processing
This paper investigates the possibility to train a convolutional neural network (CNN) that, by capturing temporal features in weather data, can estimate the expected amount of wheat produced during any year, at any geographical location. The aim is to establish whether a CNN can be used for emulation of simulated global crop production - as responses to changes in CO2, temperature, water, and nitr