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An incorporative statistic and neural approach for crop yield modelling and forecasting

journal contribution
posted on 06.12.2017, 00:00 authored by Wanwu GuoWanwu Guo, H Xue
An incorporative framework is proposed in this study for crop yield modelling and forecasting. It is a complementary approach to traditional time series analysis on modelling and forecasting by treating crop yield and associated factors as a non-temporal collection. Statistics are used to identify the highly related factor(s) among many associates to crop yield, and then play a key role in data cleaning and a supporting role in data expansion, if necessary, for neural network training and testing. Wheat yield and associated plantation area, rainfall, and temperature in Queensland of Australia over 100 years are used to test this incorporative approach. The results show that well-trained multilayer perceptron models can simulate the wheat production through given plantation areas with a mean absolute error (MAE) of ~2%, whereas the third-order polynomial correlation returns an MAE of ~20%. However, statistical analysis plays a key role in identifying the most related factor, detecting outliers, determining the general trend of wheat yield with respect to plantation area, and supporting data expansion for neural network training and testing. The combination of these two methods provides both meaningful qualitative and accurate quantitative data analysis and forecasting. This incorporative approach can also be useful in data modelling and forecasting in other applications due to its generic nature.

History

Volume

21

Issue

1

Start Page

109

End Page

117

Number of Pages

9

eISSN

1433-3058

ISSN

0941-0643

Location

United Kingdom

Publisher

Springer

Language

en-aus

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Faculty of Arts, Business, Informatics and Education; Inner Mongolia Agricultural University; Institute for Resource Industries and Sustainability (IRIS);

Era Eligible

Yes

Journal

Neural computing & applications.