Abstract
Floods are significant global hazards that generally lead to loss of lives and billions worth of properties, especially in flood-prone regions. Various excellent artificial intelligence algorithms that are aimed at predicting streamflow to minimize the effects of floods are proposed by researchers. Most of these models depend on the assumption that both training and testing data set are similar and sufficient. However, in reality, many of these data set varies along with time and are insufficient in some new basins. Motivated by the success of transfer learning in natural language processing, image processing and time series forecasting. In this paper, we proposed two hybrid transfer learning models for streamflow forecasting. The proposed models, which integrate Gated Recurrent Unit (GRU) with transfer learning and integration of Long Short Term Memory (LSTM) with transfer learning, are compared to our reference model. The proposed coupled Transfer Learning with the GRU (TL+GRU) model outperforms the baseline models, i.e., the transfer learning model and the coupled Transfer Learning with LSTM model (TL+LSTM) for most of the basins when streamflow and precipitation data set from Model Parameter Estimation Experiment (MOPEX) basins in the United States of America is used. As a result, we can finally conclude that, with Artificial Neural Networks’ (ANN) integration to transfer learning, more enhanced performance are obtained.
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Data Availability
The data that support the findings of this study are openly available at https://hydrology.nws.noaa.gov/pub/gcip/mopex/US_Data/
References
Apaydin H, Feizi H, Sattari MT et al (2020) Comparative analysis of recurrent neural network architectures for reservoir inflow forecasting. Water 12(5):1500. https://doi.org/10.3390/w12051500
Chen J, Jing H, Chang Y et al (2019) Gated recurrent unit based recurrent neural network for remaining useful life prediction of nonlinear deterioration process. Reliab Eng Syst Saf 185:372–382. https://doi.org/10.1016/j.ress.2019.01.006
Chen Y, Liu G, Huang X et al (2020) Development of a surrogate method of groundwater modeling using gated recurrent unit to improve the efficiency of parameter auto-calibration and global sensitivity analysis. J Hydrol:125726. https://doi.org/10.1016/j.jhydrol.2020.125726
Chung J, Gulcehre C, Cho K et al (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv:14123555, https://doi.org/10.48550/arXiv.1412.3555
Dhulekar N, Nambirajan S, Oztan B et al (2015) Seizure prediction by graph mining, transfer learning, and transformation learning. In: International workshop on machine learning and data mining in pattern recognition. https://doi.org/10.1007/978-3-319-21024-7_3. Springer, pp 32–52
Dong L, Fang D, Wang X et al (2020) Prediction of streamflow based on dynamic sliding window LSTM. Water 12(11):3032. https://doi.org/10.3390/w12113032
Feng D, Fang K, Shen C (2020) Enhancing streamflow forecast and extracting insights using long-short term memory networks with data integration at continental scales. Water Resour Res 56(9):e2019WR026,793. https://doi.org/10.1029/2019WR026793
Garcke J, Vanck T (2014) Importance weighted inductive transfer learning for regression. In: Joint European conference on machine learning and knowledge discovery in databases. https://doi.org/10.1007/978-3-662-44848-9_30. Springer, pp 466–481
Guenzi D, Fratianni S, Boraso R et al (2017) CondMerg: an open source implementation in R language of conditional merging for weather radars and rain gauges observations. Earth Science Inform 10(1):127–135
Gunduz S, Ugurlu U, Oksuz I (2020) Transfer learning for electricity price forecasting. arXiv:200703762, https://doi.org/10.48550/arXiv.2007.03762
Hu Q, Zhang R, Zhou Y (2016) Transfer learning for short-term wind speed prediction with deep neural networks. Renew Energy 85:83–95. https://doi.org/10.1016/j.renene.2015.06.034
Jiang S, Zheng Y, Babovic V et al (2018) A computer vision-based approach to fusing spatiotemporal data for hydrological modeling. J Hydrol 567:25–40. https://doi.org/10.1016/j.jhydrol.2018.09.064
Jin XB, Yang NX, Wang XY et al (2020) Hybrid deep learning predictor for smart agriculture sensing based on empirical mode decomposition and gated recurrent unit group model. Sensors 20(5):1334. https://doi.org/10.3390/s20051334
Khan NM, Abraham N, Hon M (2019) Transfer learning with intelligent training data selection for prediction of Alzheimer’s disease. IEEE Access 7:72,726–72,735. https://doi.org/10.1109/ACCESS.2019.2920448
Kohansarbaz A, Kohansarbaz A, Yaghoubi B et al (2021) An integration of adaptive neuro-fuzzy inference system and firefly algorithm for scour estimation near bridge piers. Earth Sci Inform 14(3):1399–1411
Li W, Wu H, Zhu N, et al (2020) Prediction of dissolved oxygen in a fishery pond based on gated recurrent unit (GRU). Information Processing in Agriculture https://doi.org/10.1016/j.inpa.2020.02.002
Li X, Xie H, Lau RY et al (2018) Stock prediction via sentimental transfer learning. IEEE Access 6:73,110–73,118. https://doi.org/10.1109/ACCESS.2018.2881689
Majumdar K, Jayachandran S (2018) A geometric analysis of time series leading to information encoding and a new entropy measure. J Comput Appl Math 328:469–484. https://doi.org/10.1016/j.cam.2017.07.006
Mocanu E, Nguyen PH, Kling WL et al (2016) Unsupervised energy prediction in a smart grid context using reinforcement cross-building transfer learning. Energy Build 116:646–655. https://doi.org/10.1016/j.enbuild.2016.01.030
Muhammad AU, Li X, Feng J (2019a) Artificial intelligence approaches for urban water demand forecasting: A review. In: International conference on machine learning and intelligent communications. https://doi.org/10.1007/978-3-030-32388-2_51. Springer, pp 595–622
Muhammad AU, Li X, Feng J (2019b) Using LSTM GRU and hybrid models for streamflow forecasting. In: International conference on machine learning and intelligent communications. https://doi.org/10.1007/978-3-030-32388-2_44. Springer, pp 510–524
Muhammad AU, Yahaya AS, Kamal SM et al (2020) A hybrid deep stacked LSTM and GRU for water price prediction. In: 2020 2nd international conference on computer and information sciences (ICCIS). https://doi.org/10.1109/ICCIS49240.2020.9257651. IEEE, pp 1–6
Nguyen TT, Yoon S (2019) A novel approach to short-term stock price movement prediction using transfer learning. Appl Sci 9(22):4745. https://doi.org/10.3390/app9224745
Ni L, Wang D, Singh VP et al (2020) Streamflow and rainfall forecasting by two long short-term memory-based models. J Hydrol 583:124,296. https://doi.org/10.1016/j.jhydrol.2019.124296
Pan SJ, Yang Q (2009) A survey on transfer learning. IEEE Trans Knowl Data Eng 22 (10):1345–1359. https://doi.org/10.1109/TKDE.2009.191
Pan W, Liu NN, Xiang EW et al (2011) Transfer learning to predict missing ratings via heterogeneous user feedbacks. In: Proceedings of the 22th international joint conference on artificial intelligence, Barcelona, Catalonia, Spain, p 2318
Qiu S, Liang R, Wang Y et al (2022) Comparative analysis of machine learning algorithms and statistical models for predicting crown width of Larix olgensis. Earth Sci Inform 15(4):2415–2429
Qureshi AS, Khan A, Zameer A et al (2017) Wind power prediction using deep neural network based meta regression and transfer learning. Appl Soft Comput 58:742–755. https://doi.org/10.1016/j.asoc.2017.05.031
Raina R, Battle A, Lee H et al (2007) Self-taught learning: transfer learning from unlabeled data. In: Proceedings of the 24th international conference on Machine learning, pp 759–766. https://doi.org/10.1145/1273496.1273592
Razavi T, Coulibaly P (2017) An evaluation of regionalization and watershed classification schemes for continuous daily streamflow prediction in ungauged watersheds. Can Water Resour J/Revue Can Ressourc Hydriques 42(1):2–20. https://doi.org/10.1080/07011784.2016.1184590
Sahoo BB, Jha R, Singh A et al (2019) Long short-term memory (LSTM) recurrent neural network for low-flow hydrological time series forecasting. Acta Geophys 67(5):1471–1481. https://doi.org/10.1007/s11600-019-00330-1
Studholme C, Hill DL, Hawkes DJ (1999) An overlap invariant entropy measure of 3D medical image alignment. Pattern Recogn 32(1):71–86. https://doi.org/10.1016/S0031-3203(98)00091-0
Wang S, Bonomi L, Dai W et al (2016) Big data privacy in biomedical research. IEEE Trans Big Data 6(2):296–308. https://doi.org/10.1109/TBDATA.2016.2608848
Zelelew MB, Alfredsen K (2014) Transferability of hydrological model parameter spaces in the estimation of runoff in ungauged catchments. Hydrol Sci J 59(8):1470–1490. https://doi.org/10.1080/02626667.2013.838003
Zhou Y (2020) Real-time probabilistic forecasting of river water quality under data missing situation: Deep learning plus post-processing techniques. J Hydrol 589:125,164. https://doi.org/10.1016/j.jhydrol.2020.125164
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AUM: Methodology, Writing-original draft, Formal analysis, Data curation. SIA: Conceptualization, Methodology, Writing-review & editing, Supervision.
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Communicated by: H. Babaie
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Muhammad, A.U., Abba, S.I. Transfer learning for streamflow forecasting using unguaged MOPEX basins data set. Earth Sci Inform 16, 1241–1264 (2023). https://doi.org/10.1007/s12145-023-00952-6
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DOI: https://doi.org/10.1007/s12145-023-00952-6