Local Precipitation Forecast with LSTM for Greenhouse Environmental Control

With the rise of AI technology, it can be applied to smart greenhouse. In our research, we design a prevention mechanism against instant heavy rainfall using Long Short-Term Memory (LSTM) networks to forecast the local precipitation at the next hour near the greenhouse. Besides, missing data and imb...

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Bibliographic Details
Published in2020 International Conference on Pervasive Artificial Intelligence (ICPAI) pp. 175 - 182
Main Authors Hsieh, Hsing-Chuan, Chiu, Yi-Wei, Lin, Yong-Xiang, Yao, Ming-Hwi, Lee, Yuh-Jye
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2020
Subjects
AI technology
Air pollution
cost-sensitive learning methods
costsensitive learning
data handling
data issues
Data models
Green products
greenhouse environmental control
greenhouse farmers
greenhouses
imbalanced data
imbalanced data handling
imbalanced training data
instant heavy rainfall
interpolation
learning (artificial intelligence)
linear interpolation
local precipitation forecast
Logic gates
Long Short-Term Memory networks
LSTM
Meteorology
missing data imputation
oversampling methods
oversampling techniques
pattern classification
precipitation forecast
Predictive models
prevention mechanism
rain
seasonal LSTM models
smart greenhouse
sporadic missing data
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Summary:With the rise of AI technology, it can be applied to smart greenhouse. In our research, we design a prevention mechanism against instant heavy rainfall using Long Short-Term Memory (LSTM) networks to forecast the local precipitation at the next hour near the greenhouse. Besides, missing data and imbalanced data issues are also tackled. Our experiments show that linear interpolation is enough to deal with sporadic missing data. Moreover, two approaches of imbalanced data handling can also enhance the performance of our proposed seasonal LSTM models, including oversampling methods which manipulate the imbalanced training data, as well as cost-sensitive learning methods which modify the loss function in some way. Finally, we also provide the reference result for the greenhouse farmers, so as to decide how much trade-off between Recall and Accuracy they can bear. This is done by tuning parameters related to imbalanced data handling techniques.
DOI:10.1109/ICPAI51961.2020.00040