[1] Bhutada, H., Khurshid, A., Yadav, M., Yadav, N. and Baheti, P., 2022, April. COD prediction in water

using Edge Artificial Intelligence. In 2022 10th International Conference on Emerging Trends in

Engineering and Technology-Signal and Information Processing (ICETET-SIP-22) (pp. 01-05). IEEE.

[2] Liu, P., Wang, J., Sangaiah, A.K., Xie, Y. and Yin, X., 2019. Analysis and prediction of water quality

using LSTM deep neural networks in IoT environment. Sustainability, 11(7), p.2058.

[3] Rashid, M.M., Al-Akhir, N., Simi, S.A., Saha, J., Rahman, M.O. and Kibria, M.G., 2021. IoT based

smart water quality prediction for biofloc aquaculture. International Journal of Advanced Computer

Science and Applications, 12(6).

[4] Barzegar, R., Aalami, M.T. and Adamowski, J., 2020. Short-term water quality variable prediction

using a hybrid CNN–LSTM deep learning model. Stochastic Environmental Research and Risk

Assessment, 34(2), pp.415-433.

[5] Aggarwal, S., Gulati, R. and Bhushan, B., 2019, July. Monitoring of input and output water quality in

treatment of urban waste water using IoT and artificial neural network. In 2019 2nd International

Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT) (Vol. 1,

pp. 897-901). IEEE.

[6] Mustafa, H.M., Mustapha, A., Hayder, G. and Salisu, A., 2021, January. Applications of IoT and

Artificial Intelligence in Water Quality Monitoring and Prediction: A Review. In 2021 6th International

Conference on Inventive Computation Technologies (ICICT) (pp. 968-975). IEEE.

[7] Cheng, L., Tan, X., Yao, D., Xu, W., Wu, H. and Chen, Y., 2021. A fishery water quality monitoring

and prediction evaluation system for floating UAV based on time series. Sensors, 21(13), p.4451.

[8] Yang, Y., Xiong, Q., Wu, C., Zou, Q., Yu, Y., Yi, H. and Gao, M., 2021. A study on water quality

prediction by a hybrid CNN-LSTM model with attention mechanism. Environmental Science and

Pollution Research, 28(39), pp.55129-55139.

[9] Li, Z., Peng, F., Niu, B., Li, G., Wu, J. and Miao, Z., 2018. Water quality prediction model combining

sparse auto-encoder and LSTM network. IFAC-PapersOnLine, 51(17), pp.831-836.

[10] Baek, S.S., Pyo, J. and Chun, J.A., 2020. Prediction of water level and water quality using a CNNLSTM

combined deep learning approach. Water, 12(12), p.3399.

[11] Zou, Q., Xiong, Q., Li, Q., Yi, H., Yu, Y. and Wu, C., 2020. A water quality prediction method based

on the multi-time scale bidirectional long short-term memory network. Environmental Science and

Pollution Research, 27(14), pp.16853-16864.

[12] Yan, J., Liu, J., Yu, Y. and Xu, H., 2021. Water quality prediction in the luan river based on 1-DRCNN

and bigru hybrid neural network model. Water, 13(9), p.1273.

[13] Aldhyani, T.H., Al-Yaari, M., Alkahtani, H. and Maashi, M., 2020. Water quality prediction using

artificial intelligence algorithms. Applied Bionics and Biomechanics, 2020

[14] Lu, H. and Ma, X., 2020. Hybrid decision tree-based machine learning models for short-term water

quality prediction. Chemosphere, 249, p.126169

[15] Yan, J., Gao, Y., Yu, Y., Xu, H. and Xu, Z., 2020. A prediction model based on deep belief network

and least squares SVR applied to cross-section water quality. Water, 12(7), p.1929

[16] Liu, S., Xie, J., Shen, C., Shang, X., Wang, D. and Zhu, Z., 2020. Bearing fault diagnosis based on

improved convolutional deep belief network. Applied Sciences, 10(18), p.6359.

[17] Odili, J.B., Kahar, M.N.M. and Anwar, S., 2015. African buffalo optimization: a swarm-intelligence

technique. Procedia Computer Science, 76, pp.443-448.

[18] Singh, P., Meena, N.K., Slowik, A. and Bishnoi, S.K., 2020. Modified african buffalo optimization for

strategic integration of battery energy storage in distribution networks. IEEE Access, 8, pp.14289-

14301.