Open AccessUsing Neural Network Classifier Approach for Statistically Forecasting Extreme Corn Yield Losses in Eastern United States
Author(s) -
Mathieu J. A.,
Aires F.
Publication year - 2018
Publication title -
earth and space science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.843
H-Index - 23
ISSN - 2333-5084
DOI - 10.1029/2017ea000343
Subject(s) - artificial neural network , extreme weather , extreme learning machine , environmental science , extreme heat , extreme value theory , warning system , statistics , yield (engineering) , climatology , meteorology , climate change , computer science , mathematics , machine learning , geography , geology , telecommunications , oceanography , materials science , metallurgy
This paper presents a statistical method for forecasting extreme corn yield losses caused by weather extremes. A neural network classifier approach is tested over the Eastern United States (time series of 35 years) to detect extreme yield losses for corn from weather‐related information. We first developed a methodology to rank a series of climate‐based predictors according to the accuracy with which they classify extreme from nonextreme yield losses. The classification methodology is adapted in order to be trained with a limited number of extreme cases. Using four weather predictors—the average temperature in July and August, and the SPEI (Standardized Precipitation‐Evapotranspiration Index) in June and July—71% of the extreme cases are well classified by this statistical model. Furthermore, the neural network output represents a good yield severity index and can provide an early quantitative warning for extreme yield anomalies.