Local Precipitation Forecast with LSTM for Greenhouse Environmental Control

Hsing Chuan Hsieh, Yi Wei Chiu, Yong Xiang Lin, Ming Hwi Yao, Yuh Jye Lee

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2020 International Conference on Pervasive Artificial Intelligence, ICPAI 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages175-182
Number of pages8
ISBN (Electronic)9780738142623
DOIs
StatePublished - Dec 2020
Event1st International Conference on Pervasive Artificial Intelligence, ICPAI 2020 - Taipei, Taiwan
Duration: 3 Dec 20205 Dec 2020

Publication series

NameProceedings - 2020 International Conference on Pervasive Artificial Intelligence, ICPAI 2020

Conference

Conference1st International Conference on Pervasive Artificial Intelligence, ICPAI 2020
CountryTaiwan
CityTaipei
Period3/12/205/12/20

Keywords

  • costsensitive learning
  • imbalanced data
  • LSTM
  • missing data imputation
  • oversampling techniques
  • precipitation forecast

Fingerprint Dive into the research topics of 'Local Precipitation Forecast with LSTM for Greenhouse Environmental Control'. Together they form a unique fingerprint.

Cite this