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Research Article

A new strategy for prediction of water qualitative and quantitative parameters by deep learning-based models with determination of modelling uncertainties

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Pages 207-225 | Received 19 Apr 2023, Accepted 17 Nov 2023, Published online: 26 Jan 2024

References

  • Baek, S.S., Pyo, J., and Chun, J.A., 2020. Prediction of water level and water quality using a cnn-lstm combined deep learning approach. Water (Switzerland), 12 (12), 3399.
  • Bai, T. and Tahmasebi, P., 2021. Efficient and data-driven prediction of water breakthrough in subsurface systems using deep long short-term memory machine learning. Computational Geosciences, 25 (1), 285–297. doi:10.1007/s10596-020-10005-2
  • 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), 415–433.
  • Cheng, F. and Zhao, J., 2019. A novel process monitoring approach based on feature points distance dynamic autoencoder. Computer Aided Chemical Engineering, 46, 757–762.
  • Cui, X., Wang, Z., and Pei, R., 2023. A VMD-MSMA-LSTM-ARIMA model for precipitation prediction. 68 (6), 810–839. doi:10.1080/02626667.2023.2190896
  • Dong, L. and Zhang, J., 2021. Predicting polycyclic aromatic hydrocarbons in surface water by a multiscale feature extraction-based deep learning approach. Science of the Total Environment, 799, 149509. doi:10.1016/j.scitotenv.2021.149509
  • Dwivedi, D.N. and Gupta, A., 2022. Artificial intelligence-driven power demand estimation and short-, medium-, and long-term forecasting. In: Artificial intelligence for renewable energy systems, 231–242. doi:10.1016/B978-0-323-90396-7.00013-4.
  • Faisal Mehmood Butt, Lal Hussain, Syed Hassan Mujtaba Jafri, Haya Mesfer Alshahrani, Fahd N Al-Wesabi, Kashif Javed Lone, Elsayed M. Tag El Din & Mesfer Al Duhayyim, 2022. Intelligence based Accurate Medium and Long Term Load Forecasting System, Applied Artificial Intelligence, 36, 1. doi:10.1080/08839514.2022.2088452
  • Gorugantula, S.S. and Kambhammettu, B.V.N.P., 2022. Sequential downscaling of GRACE products to map groundwater level changes in Krishna River basin. Journal of Forensic Sciences, 67 (12), 1846–1859. doi:10.1080/02626667.2022.2106142
  • Gudaparthi, H., et al., 2020. Deep learning for smart sewer systems: assessing nonfunctional requirements. In: Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering: Software Engineering in Society, 35–38. doi:10.1145/3377815.3381379.
  • Herbert, Z.C., Asghar, Z., and Oroza, C., 2021. Long-term reservoir inflow forecasts: enhanced water supply and inflow volume accuracy using deep learning. Journal of Hydrology, 601, 126676. doi:10.1016/j.jhydrol.2021.126676
  • Hochreiter, S. and Schmidhuber, J., 1997. Long short-term memory. Neural Computation, 9 (8), 1735–1780. doi:10.1162/neco.1997.9.8.1735
  • Janbain, I., et al., 2023. Use of long short-term memory network (LSTM) in the reconstruction of missing water level data in the River Seine. Hydrological Sciences Journal, 68, 1372–1390. doi:10.1080/02626667.2023.2221791
  • Jiang, Y., et al., 2021. A deep learning algorithm for multi-source data fusion to predict water quality of urban sewer networks. Journal of Cleaner Production, 318, 128533. doi:10.1016/j.jclepro.2021.128533
  • Khatun, A., et al., 2023. A novel smoothing-based long short-term memory framework for short-to medium-range flood forecasting. 68 (3), 488–506. doi:10.1080/02626667.2023.2173012
  • Kisi, O., et al., 2022. Comparative evaluation of deep learning and machine learning in modelling pan evaporation using limited inputs. 67 (9), 1309–1327. doi:10.1080/02626667.2022.2063724
  • Li, S., et al., 2018. Independently Recurrent Neural Network (IndRNN): building a longer and deeper RNN. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 5457–5466. doi:10.1109/CVPR.2018.00572
  • Liao, Z., et al., 2021. Deep learning identifies leak in water pipeline system using transient frequency response. Process Safety and Environmental Protection, 155, 355–365. doi:10.1016/j.psep.2021.09.033
  • Li, J., Li, Y., and Yu, T., 2021. Distributed deep reinforcement learning-based multi-objective integrated heat management method for water-cooling proton exchange membrane fuel cell. Case Studies in Thermal Engineering, 27, 101284. doi:10.1016/j.csite.2021.101284
  • Mayer, T., et al., 2021. Deep learning approach for Sentinel-1 surface water mapping leveraging Google earth engine. ISPRS Open Journal of Photogrammetry and Remote Sensing, 2, 100005. doi:10.1016/j.ophoto.2021.100005
  • Nagappan, M., Gopalakrishnan, V., and Alagappan, M., 2020. Prediction of reference evapotranspiration for irrigation scheduling using machine learning. Hydrological Sciences Journal, 65 (16), 2669–2677. doi:10.1080/02626667.2020.1830996
  • Poursaeid, M., et al., 2020. Estimation of quantity and quality parameters of groundwater using numerical models (Case study: Mighan Desert Basin, Arak). Iranian Journal of Soil and Water Research, 51 (1), 201–216.
  • Poursaeid, M., 2023. An optimized extreme learning machine by evolutionary computation for river flow prediction and simulation of water pollution in Colorado River Basin, USA. In: Expert systems with applications, 120998. doi:10.1016/j.eswa.2023.120998
  • Poursaeid, M., Poursaeed, A.H., and Shabanlou, S., 2022a. Study of water resources parameters using artificial intelligence techniques and learning algorithms: a survey. Applied Water Science, 12 (7), 1–15. doi:10.1007/s13201-022-01675-7
  • Poursaeid, M., Poursaeid, A., and Shabanlou, S., 2022b. Hydraulic modeling of the water resources using learning techniques. Iranian Journal of Soil and Water Research, 52 (11), 2739–2750.
  • Poursaeid, M., Poursaeid, A., and Shabanlou, S., 2023. Simulation using machine learning and multiple linear regression in hydraulic engineering. Water and Soil Science, 33 (4), 19–32. doi:10.22034/ws.2021.48553.2445
  • Sabanci, D., et al., 2023. Predicting reference evapotranspiration based on hydro-climatic variables: comparison of different machine learning models. Survey of Ophthalmology, 68 (7), 1050–1063. doi:10.1080/02626667.2023.2203824
  • Shen, C., 2018. A transdisciplinary review of deep learning research and its relevance for water resources scientists. Water Resources Research, 54 (11), 8558–8593. doi:10.1029/2018WR022643
  • Singh, A., et al., 2022a. Evaluation of precipitation forecasts for five-day streamflow forecasting in Narmada River basin. Hydrological Sciences Journal, 68 (1), 161–179. doi:10.1080/02626667.2022.2151913
  • Singh, E., Kuzhagaliyeva, N., and Sarathy, S.M., 2022b. Using deep learning to diagnose preignition in turbocharged spark-ignited engines. In: Artificial intelligence and data driven optimization of internal combustion engines, 213–237. doi:10.1016/B978-0-323-88457-0.00005-9
  • Tiyasha, T.T.M. and Yaseen, Z.M., 2021. Deep learning for prediction of water quality index classification: tropical catchment environmental assessment. Natural Resources Research, 30 (6), 4235–4254.
  • Wu, Z.Y., El-Maghraby, M., and Pathak, S., 2015. Applications of deep learning for smart water networks. Procedia Engineering, 119 (1), 479–485. doi:10.1016/j.proeng.2015.08.870
  • Wu, Q. and Lin, H., 2019. Daily urban air quality index forecasting based on variational mode decomposition, sample entropy and LSTM neural network. Sustainable Cities and Society, 50, 101657. doi:10.1016/j.scs.2019.101657
  • Yulita, I.N., Fanany, M.I., and Arymuthy, A.M., 2017. Bi-directional long short-term memory using quantized data of deep belief networks for sleep stage classification. Procedia Computer Science, 116, 530–538. doi:10.1016/j.procs.2017.10.042
  • Zaremba, W., et al., 2014. Recurrent neural network regularization.

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