TY - GEN
T1 - LSTM-Based Prediction of Water Quality Parameters System in Backwaters
AU - Shreesha, S.
AU - Manohara Pai, M. M.
AU - Pai, Radhika M.
AU - Verma, Ujjwal
N1 - Funding Information:
ACKNOWLEDGMENT We wish to acknowledge Department of Science and Technology (DST), Government of India, for extending the financial support to the project titled Development and deployment of Smart Aquaculture: An IoT enabled System (grant number: DST/SSTP/KARNATAKA/73/2017-18) under which the work has been carried out.
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Aquaculture provides food security, self-employment, and a sustainable source of income. At the same time, risks involved is very high as the fishermen practice traditional approaches to maintain the culture systems. The unforeseen changes in the water quality parameters may cause mass mortality of fishes leading to economic losses. To address this issue, an experimental investigation is carried out by monitoring water quality parameters in an exsisting aquaculture eco-system. LSTM is utilized to predict the water quality parameters 90 minutes in advance, which provides sufficient time window for fishermen to take appropriate precautions. Performance analysis of three such different LSTMs architecture has been conducted. It has been observed that, the Bi-directional LSTM can better model the dynamic nature of the data. Also, the outliers in the predicted values have been identified by employing Gaussian distribution model. From the experiment, it can be seen the performance of developed outlier detection system is acceptable. The decision support system thus developed, supports the culturists for maintaining the aquaculture eco system in a favorable condition.
AB - Aquaculture provides food security, self-employment, and a sustainable source of income. At the same time, risks involved is very high as the fishermen practice traditional approaches to maintain the culture systems. The unforeseen changes in the water quality parameters may cause mass mortality of fishes leading to economic losses. To address this issue, an experimental investigation is carried out by monitoring water quality parameters in an exsisting aquaculture eco-system. LSTM is utilized to predict the water quality parameters 90 minutes in advance, which provides sufficient time window for fishermen to take appropriate precautions. Performance analysis of three such different LSTMs architecture has been conducted. It has been observed that, the Bi-directional LSTM can better model the dynamic nature of the data. Also, the outliers in the predicted values have been identified by employing Gaussian distribution model. From the experiment, it can be seen the performance of developed outlier detection system is acceptable. The decision support system thus developed, supports the culturists for maintaining the aquaculture eco system in a favorable condition.
UR - https://www.scopus.com/pages/publications/85123368947
UR - https://www.scopus.com/inward/citedby.url?scp=85123368947&partnerID=8YFLogxK
U2 - 10.1109/CONECCT52877.2021.9622543
DO - 10.1109/CONECCT52877.2021.9622543
M3 - Conference contribution
AN - SCOPUS:85123368947
T3 - Proceedings of CONECCT 2021: 7th IEEE International Conference on Electronics, Computing and Communication Technologies
BT - Proceedings of CONECCT 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2021
Y2 - 9 July 2021 through 11 July 2021
ER -
