Machine Learning Method for Forecasting Weather Needed For Crop Water Demand Estimations in Low-Resource Settings Using A Case Study in Morocco
Name
v03bt03a016-detc2021-70571.pdf
Description
Published version
Size
2.97 MB
Format
Adobe PDF
Checksum (MD5)
6d9323345496aa4485a7faa0e583698e
Author(s)
Sheline, Carolyn
Winter, Amos
Date Issued
August 17, 2021
Journal
Volume 3B: 47th Design Automation Conference (DAC)
Publisher
American Society of Mechanical Engineers
Citation
Sheline, Carolyn and Winter, Amos. 2021. "Machine Learning Method for Forecasting Weather Needed For Crop Water Demand Estimations in Low-Resource Settings Using A Case Study in Morocco." Volume 3B: 47th Design Automation Conference (DAC).
Version
Final published version
Abstract
Low and middle income countries often do not have the infrastructure needed to support weather forecasting models, which are computationally expensive and often require detailed inputs from local weather stations. Local, low-cost weather prediction services are needed to enable optimal irrigation scheduling and increase crop productivity for rural farmers in low-resource settings. This work proposes a machine learning approach to predict the weather inputs needed to calculate crop water demand, namely evapotranspiration and precipitation. The focus of this work is on the accuracy with which Moroccan weather can be predicted with a vector autoregression (VAR) model compared to using typical meteorological year (TMY) weather, and how this accuracy changes as the number of weather parameters is reduced.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1115/detc2021-70571
