Accuracy of Water Quality Prediction Using Random Forest Regression and Artificial Neural Network-Reference-Cited by-同舟云学术

Accuracy of Water Quality Prediction Using Random Forest Regression and Artificial Neural Network

Published:2023 Issue: Volume: Page:373-381
ISSN:2367-3370
Container-title:Lecture Notes in Networks and Systems
language:
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Author:

Hai Tao^ORCID,Zhou Jincheng^ORCID,Uzochukwu Chijioke Victor,Ayandiran Doyinsola,Liu Yi,Iwendi Celestine

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-37164-6_28

Reference13 articles.

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3. Hu, Z., Zhang, Y., Zhao, Y., Xie, M., Zhong, J., Tu, Z., Liu, J.: A water quality prediction method based on the deep LSTM network considering correlation in smart mariculture. Sensors 19(6), 1420 (2019)

4. Yu, Y., Qu, Y., Zhang, H., Jiang, L., Shao, M., Wei, D., Zhang, D.: Indicator Analysis of Lake Water Quality Based on Fuzzy Neural Network and Spectrophotometry. In: 2022 IEEE 10th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), vol. 10, pp. 1543–1546. IEEE (2022)

5. Ragi, N.M., Holla, R., Manju, G.: Predicting Water Quality Parameters Using Machine Learning. In: 2019 4th International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT), pp. 1109–1112. IEEE (2019)