REFERENCES
1. Moftakhari HR, AghaKouchak A, Sanders BF, Allaire M, Matthew RA. What is nuisance flooding? Defining and monitoring an emerging challenge. Water Resour Res 2018;54:4218-27.
2. Zhang M, Conti F, Le Sourne H, et al. A method for the direct assessment of ship collision damage and flooding risk in real conditions. Ocean Eng 2021;237:109605.
3. Luiz-Silva W, Oscar-Júnior AC. Climate extremes related with rainfall in the State of Rio de Janeiro, Brazil: a review of climatological characteristics and recorded trends. Nat Hazards 2022;114:713-32.
4. Bozorg O. Review on IPCC reports. Climate Change in Sustainable Water Resources Management 2022:123.
5. Guo Y, Wu Y, Wen B, et al. Floods in China, COVID-19, and climate change. The Lancet Planet Health 2020;4:e443-44.
6. Yamamoto H, Naka T. Quantitative analysis of the impact of floods on firms' financial conditions. Bank of Japan; 2021.
7. Romero M, Finke J, Rocha C. A top-down supervised learning approach to hierarchical multi-label classification in networks. Appl Netw Sci 2022;7:1-17.
8. Henriksen HJ, Roberts MJ, van der Keur P, et al. Participatory early warning and monitoring systems: a nordic framework for web-based flood risk management. Int J Disast Risk Re 2018;31:1295-306.
9. Ferrans P, Torres MN, Temprano J, Sánchez JPR. Sustainable Urban Drainage System (SUDS) modeling supporting decision-making: a systematic quantitative review. Sci Total Environ 2022;806:150447.
10. Brunner MI, Slater L, Tallaksen LM, Clark M. Challenges in modeling and predicting floods and droughts: a review. Wiley Interdiscip Rev: Water 2021;8:e1520.
11. Ding Y, Zhu Y, Wu Y, Jun F, Cheng Z. Spatio-temporal attention LSTM model for flood forecasting. In: 2019 International Conference on Internet of Things (IThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData). IEEE; 2019. pp. 458-65.
12. De La Cruz R, Olfindo Jr N, Felicen M, et al. Near-realtime Flood Detection From Multi-temporal Sentinel Radar Images Using Artificial Intelligence. ISPRS 2020:43.
13. Belabid N, Zhao F, Brocca L, Huang Y, Tan Y. Near-real-time flood forecasting based on satellite precipitation products. Remote Sens 2019;11:252.
14. Munawar HS, Hammad AW, Waller ST. A review on flood management technologies related to image processing and machine learning. Autom. Constr 2021;132:103916.
15. Bronfman NC, Cisternas PC, Repetto PB, Castañeda JV. Natural disaster preparedness in a multi-hazard environment: characterizing the sociodemographic profile of those better (worse) prepared. PloS one 2019;14:e0214249.
16. Chhajer P, Shah M, Kshirsagar A. The applications of artificial neural networks, support vector machines, and long-short term memory for stock market prediction. Decision Analytics Journal 2022;2:100015.
17. Zhang J, Bargal SA, Lin Z, et al. Top-down neural attention by excitation backprop. Int J Comput Vision 2018;126:1084-102.
18. Greener JG, Kandathil SM, Moffat L, Jones DT. A guide to machine learning for biologists. Nat Rev Mol Cell Bio 2022;23:40-55.
19. Rustam F, Reshi AA, Mehmood A, et al. COVID-19 future forecasting using supervised machine learning models. IEEE access 2020;8:101489-99.
20. El Boujnouni M. A study and identification of COVID-19 viruses using N-grams with Naïve Bayes, K-nearest neighbors, artificial neural networks, decision tree and support vector machine. In: 2022 International Conference on Intelligent Systems and Computer Vision (ISCV). IEEE; 2022. pp. 1-7.
21. Cunningham P, Cord M, Delany SJ. Supervised learning machine learning techniques for multimedia. Springer; 2008.
24. de Bruijn JA, de Moel H, Jongman B, et al. A global database of historic and real-time flood events based on social media. Sci data 2019;6:311.
25. Khan W, Ghazanfar MA, Azam MA, et al. Stock market prediction using machine learning classifiers and social media, news. J Amb Intel Hum Comp 2020:1-24.
27. Ghahramani Z. Unsupervised learning. Advanced Lectures on Machine Learning. LNAI 3176. Springer-Verlag; 2004.
31. Zhu XJ. Semi-supervised learning literature survey 2005.
32. Mey A, Loog M. Improved generalization in semi-supervised learning: a survey of theoretical results. IEEE T Pattern Anal 2022; doi: 10.1109/TPAMI.2022.3198175.
33. Xu W, Tang J, Xia H. A review of semi-supervised learning for industrial process regression modeling. In: 2021 40th Chinese Control Conference (CCC). IEEE; 2021. pp. 1359-64.
34. Yang X, Song Z, King I, Xu Z. A survey on deep semi-supervised learning. IEEE T Knowl Data En 2022:1-20.
35. Poldrack RA, Huckins G, Varoquaux G. Establishment of best practices for evidence for prediction: a review. JAMA Psychiat 2020;77:534-40.
36. Gull T, Khurana S, Kumar M. Semi-supervised labeling: a proposed methodology for labeling the twitter datasets. Multimed Tools Appl 2022;03:81.
37. Giglioni V, García-Macías E, Venanzi I, Ierimonti L, Ubertini F. The use of receiver operating characteristic curves and precision-versus-recall curves as performance metrics in unsupervised structural damage classification under changing environment. Eng Struct 2021;246:113029.
38. Opella JMA, Hernandez AA. Developing a flood risk assessment using support vector machine and convolutional neural network: a conceptual framework. In: 2019 IEEE 15th International Colloquium on Signal Processing & Its Applications (CSPA). IEEE; 2019. pp. 260-65.
39. Sankaranarayanan S, Prabhakar M, Satish S, et al. Flood prediction based on weather parameters using deep learning. J Water Clim Change 2020;11:1766-83.
40. Arzoumanian Z, Baker PT, Blumer H, et al. The NANOGrav 12.5 yr data set: search for an isotropic stochastic gravitational-wave background. The Astrophysical journal letters 2020;905:L34.
41. Benetos E, Dixon S, Duan Z, Ewert S. Automatic music transcription: an overview. IEEE Signal Proc Mag 2018;36:20-30.
42. Zhou T, Thung KH, Zhu X, Shen D. Effective feature learning and fusion of multimodality data using stage-wise deep neural network for dementia diagnosis. Hum Brain Mapp 2019;40:1001-16.
43. Lin J, Li J, Chen J. An analysis of English classroom behavior by intelligent image recognition in IoT. Int J Syst Assur Eng 2021:1-9.
44. Chen S, Yu J, Wang S. One-dimensional convolutional auto-encoder-based feature learning for fault diagnosis of multivariate processes. J Process Contr 2020;87:54-67.
45. Masarczyk W, Głomb P, Grabowski B, Ostaszewski M. Effective training of deep convolutional neural networks for hyperspectral image classification through artificial labeling. Remote Sens 2020;12:2653.
46. Lindsay GW. Convolutional neural networks as a model of the visual system: Past, present, and future. J Cognitive Neurosci 2021;33:2017-31.
47. Zeman AA, Ritchie JB, Bracci S, Op de Beeck H. Orthogonal representations of object shape and category in deep convolutional neural networks and human visual cortex. Sci Rep-Uk 2020;10:2453.
48. Alzubaidi L, Fadhel MA, Oleiwi SR, Al-Shamma O, Zhang J. DFU_QUTNet: diabetic foot ulcer classification using novel deep convolutional neural network. Multimed Tools Appl 2020;79:15655-77.
49. Li Z, Wu Q, Xiao Y, Jin M, Lu H. Deep matching network for handwritten Chinese character recognition. Pattern Recogn 2020;107:107471.
50. Devaraj J, Madurai Elavarasan R, Shafiullah G, Jamal T, Khan I. A holistic review on energy forecasting using big data and deep learning models. Int J Energ Res 2021;45:13489-530.
51. Larochelle H, Erhan D, Courville A, Bergstra J, Bengio Y. An empirical evaluation of deep architectures on problems with many factors of variation. In: Proceedings of the 24th international conference on Machine learning; 2007. pp. 473-80.
52. Imamverdiyev Y, Abdullayeva F. Deep learning method for denial of service attack detection based on restricted boltzmann machine. Big data 2018;6:159-69.
53. Satarzadeh E, Sarraf A, Hajikandi H, Sadeghian MS. Flood hazard mapping in western Iran: assessment of deep learning vis-à-vis machine learning models. Nat Hazards 2022:1-19.
54. Ying C, Li Q, Liu J. A Brief Investigation for Techniques of Deep Learning Model in Smart Grid. In: 2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM). IEEE; 2021. pp. 173-81.
55. Raza K, Singh NK. A tour of unsupervised deep learning for medical image analysis. Curr Med Imaging 2021;17:1059-77.
56. Amin SU, Alsulaiman M, Muhammad G, Mekhtiche MA, Hossain MS. Deep learning for EEG motor imagery classification based on multi-layer CNNs feature fusion. Future Gener Comp Sy 2019;101:542-54.
57. Zhang Y, Wu J, Cai Z, Du B, Philip SY. An unsupervised parameter learning model for RVFL neural network. Neural Networks 2019;112:85-97.
58. Mittal S, Lamb A, Goyal A, et al. Learning to combine top-down and bottom-up signals in recurrent neural networks with attention over modules. In: International Conference on Machine Learning. PMLR; 2020. pp. 6972-86.
59. Forbus KD, Ferguson RW, Lovett A, Gentner D. Extending SME to handle large-scale cognitive modeling. Cognitive Sci 2017;41:1152-201.
60. Mu Y. An evaluation of deep learning models for urban floods forecasting; 2022.
61. Sit M, Demiray BZ, Xiang Z, et al. A comprehensive review of deep learning applications in hydrology and water resources. Water Sci Technol 2020;82:2635-70.
62. Nevo S, Morin E, Gerzi Rosenthal A, et al. Flood forecasting with machine learning models in an operational framework. Hydrol Earth Syst Sc 2022;26:4013-32.
63. Saxena D, Cao J. Generative adversarial networks (GANs) challenges, solutions, and future directions. ACM Computing Surveys (CSUR) 2021;54:1-42.
64. Goodfellow IJ. On distinguishability criteria for estimating generative models. arXiv preprint arXiv: 14126515 2014.
65. Salazar A, Vergara L, Safont G. Generative adversarial networks and markoveandom fields for oversampling very small training sets. Expert Syst Appl 2021;163:113819.
66. Le XH, Ho HV, Lee G, Jung S. Application of long short-term memory (LSTM) neural network for flood forecasting. Water 2019;11:1387.
67. Ren Q, Li M, Song L, Liu H. An optimized combination prediction model for concrete dam deformation considering quantitative evaluation and hysteresis correction. Adv Eng Inform 2020;46:101154.
68. Yang Z, Yu Y, You C, Steinhardt J, Ma Y. Rethinking bias-variance trade-off for generalization of neural networks. In: International Conference on Machine Learning. PMLR; 2020. pp. 10767-77.
69. Wang Q, Ma Y, Zhao K, Tian Y. A comprehensive survey of loss functions in machine learning. Annals of Data Science 2020:1-26.
70. Anisha P, Polati A. A bird eye view on the usage of artificial intelligence. In: Communication Software and Networks: Proceedings of INDIA 2019. Springer; 2021. pp. 61–77.
71. Belkin M, Hsu D, Ma S, Mandal S. Reconciling modern machine-learning practice and the classical bias-variance trade-off. P Natl Acad Sci 2019;116:15849-54.
72. Heinlein A, Klawonn A, Lanser M, Weber J. Combining machine learning and adaptive coarse spaces—a hybrid approach for robust FETI-DP methods in three dimensions. SIAM J Sci Comput 2021;43:S816-38.
73. Jiang Y, Yin S, Dong J, Kaynak O. A review on soft sensors for monitoring, control, and optimization of industrial processes. IEEE Sens J 2020;21:12868-81.
74. Arnott R, Harvey CR, Markowitz H. A backtesting protocol in the era of machine learning. The Journal of Financial Data Science 2019;1:64-74.
75. Sulaiman J, Wahab SH. Heavy rainfall forecasting model using artificial neural network for flood prone area. In: IT Convergence and Security 2017: Volume 1. Springer; 2018. pp. 68-76.
76. Chen D, Liu F, Zhang Z, Lu X, Li Z. Significant wave height prediction based on wavelet graph neural network. In: 2021 IEEE 4th International Conference on Big Data and Artificial Intelligence (BDAI). IEEE; 2021. pp. 80-85.
78. Chen D, Zhou R, Pan Y, Liu F. A simple baseline for adversarial domain adaptation-based unsupervised flood forecasting. arXiv preprint arXiv: 220608105 2022.
79. Li J, Socher R, Hoi SC. Dividemix: Learning with noisy labels as semi-supervised learning. arXiv preprint arXiv: 200207394 2020.
80. Scheinost D, Noble S, Horien C, et al. Ten simple rules for predictive modeling of individual differences in neuroimaging. NeuroImage 2019;193:35-45.
81. Lowrance CJ, Lauf AP. An active and incremental learning framework for the online prediction of link quality in robot networks. Eng Appl Artif Intel 2019;77:197-211.
82. Cai S, Wang Z, Lu L, Zaki TA, Karniadakis GE. DeepM & Mnet: inferring the electroconvection multiphysics fields based on operator approximation by neural networks. J Comput Phys 2021;436:110296.
83. Guo Q, Zhuang F, Qin C, et al. A survey on knowledge graph-based recommender systems. IEEE T Knowl Data En 2020;34:3549.
84. Kang Z, Pan H, Hoi SC, Xu Z. Robust graph learning from noisy data. IEEE T Cybernetics 2019;50:1833-43.
85. Lyu L, Fang M, Wang N, Wu J. Water level prediction model based on GCN and LSTM. In: 2021 7th International Conference on Computer and Communications (ICCC). IEEE; 2021. pp. 1600-1605.
86. Yang W, Chen L, Chen X, Chen H. Sub-daily precipitation-streamflow modelling of the karst-dominated basin using an improved grid-based distributed Xinanjiang hydrological model. J Hydrol-Reg Stud 2022;42:101125.
87. Feng J, Wang Z, Wu Y, Xi Y. Spatial and temporal aware graph convolutional network for flood forecasting. In: 2021 International Joint Conference on Neural Networks (IJCNN). IEEE; 2021. pp. 1-8.
88. Miau S, Hung WH. River flooding forecasting and anomaly detection based on deep learning. IEEE Access 2020;8:198384-402.
89. Taşar B, Kaya YZ, Varçin H, Üneş F, Demirci M. Forecasting of suspended sediment in rivers using artificial neural networks approach. International Journal of Advanced Engineering Research and Science 2017;4:237333.
90. Sahoo A, Samantaray S, Ghose DK. Prediction of flood in Barak River using hybrid machine learning approaches: a case study. J Geol Soc India 2021;97:186-98.
91. Shilton A, Palaniswami M, Ralph D, Tsoi AC. Incremental training of support vector machines. IEEE T Neural Networ 2005;16:114-31.
92. Zanfei A, Brentan BM, Menapace A, Righetti M, Herrera M. Graph convolutional recurrent neural networks for water demand forecasting. Water Resour Res 2022;58:e2022WR032299.
93. Zhang Y, Gu Z, Thé JVG, Yang SX, Gharabaghi B. The discharge forecasting of multiple monitoring station for Humber River by hybrid LSTM models. Water 2022;14:1794.
94. Cho M, Kim C, Jung K, Jung H. Water level prediction model applying a long short-term memory (lstm)–gated recurrent unit (gru) method for flood prediction. Water 2022;14:2221.
95. Ding Y, Zhu Y, Feng J, Zhang P, Cheng Z. Interpretable spatio-temporal attention LSTM model for flood forecasting. Neurocomputing 2020;403:348-59.
96. Dtissibe FY, Ari AAA, Titouna C, Thiare O, Gueroui AM. Flood forecasting based on an artificial neural network scheme. Nat Hazards 2020;104:1211-37.
97. Ahmad M, Al Mehedi MA, Yazdan MMS, Kumar R. Development of machine learning flood model using Artificial Neural Network (ANN) at Var River. Liquids 2022;2:147-60.
98. Hassanpour Kashani M, Montaseri M, Lotfollahi Yaghin MA. Flood estimation at ungauged sites using a new hybrid model. J Appl Sci 2008;8:1744-49.
99. Tabbussum R, Dar AQ. Comparative analysis of neural network training algorithms for the flood forecast modelling of an alluvial Himalayan river. J Flood Risk Manag 2020;13:e12656.
100. Jabbari A, Bae DH. Application of Artificial Neural Networks for accuracy enhancements of real-Time flood forecasting in the Imjin Basin. Water 2018:10.
101. Dong P, Liao X, Chen Z, Chu H. An improved method for predicting CO 2 minimum miscibility pressure based on artificial neural network. Advances in Geo-Energy Research 2019;3:355-64.
102. Kimura N, Yoshinaga I, Sekijima K, Azechi I, Baba D. Convolutional neural network coupled with a transfer-learning approach for time-series flood predictions. Water 2019;12:96.
103. Zhang L, Huang Z, Liu W, Guo Z, Zhang Z. Weather radar echo prediction method based on convolution neural network and long short-term memory networks for sustainable e-agriculture. J Clean Prod 2021;298:126776.
104. Sun W, Wang R. Fully convolutional networks for semantic segmentation of very high resolution remotely sensed images combined with DSM. IEEE Geosci Remote S 2018;15:474-78.
105. Yuan F, Xu Y, Li Q, Mostafavi A. Spatio-temporal graph convolutional networks for road network inundation status prediction during urban flooding. Comput Environ Urban 2022;97:101870.
106. Mehedi MAA, Khosravi M, Yazdan MMS, Shabanian H. Exploring temporal dynamics of river discharge using univariate Long Short-Term Memory (LSTM) Recurrent Neural Network at east branch of Delaware River. Hydrology 2022;9:202.
107. Liu M, Huang Y, Li Z, et al. The applicability of LSTM-KNN model for real-time flood forecasting in different climate zones in China. Water 2020;12:440.
108. Song T, Ding W, Wu J, et al. Flash flood forecasting based on long short-term memory networks. Water 2019;12:109.
109. Li X, Xu W, Ren M, Jiang Y, Fu G. Hybrid CNN-LSTM models for river flow prediction. Water Supply 2022;22:4902.
110. Li P, Zhang J, Krebs P. Prediction of flow based on a CNN-LSTM combined deep learning approach. Water 2022;14:993.
111. Kasiviswanathan KS, He J, Sudheer K, Tay JH. Potential application of wavelet neural network ensemble to forecast streamflow for flood management. J Hydrol 2016;536:161-73.
112. Lin L, Li W, Bi H, Qin L. Vehicle trajectory prediction using LSTMs with spatial-temporal attention mechanisms. IEEE Intel Transp Sy 2021;14:197-208.
113. Noor F, Haq S, Rakib M, et al. Water level forecasting using spatiotemporal attention-based Long Short-Term Memory Network. Water 2022;14:612.
114. Wang Y, Huang Y, Xiao M, et al. Medium-long-term prediction of water level based on an improved spatio-temporal attention mechanism for long short-term memory networks. J Hydrol 2023;618:129163.
115. Chen C, Luan D, Zhao S, et al. Flood discharge prediction based on remote-sensed spatiotemporal features fusion and graph attention. Remote Sens 2021;13:5023.
116. Liu M, Chen L, Du X, Jin L, Shang M. Activated gradients for deep neural networks. IEEE T Neur Net Lear 2021; doi: 10.1109/TNNLS.2021.3106044.
117. Luo Y, Huang Z, Zhang Z, Wang Z, Li J, et al. Curiosity-driven reinforcement learning for diverse visual paragraph generation. In: Proceedings of the 27th ACM International Conference on Multimedia; 2019. pp. 2341-50.
119. Tran DA, Tsujimura M, Ha NT, et al. Evaluating the predictive power of different machine learning algorithms for groundwater salinity prediction of multi-layer coastal aquifers in the Mekong Delta, Vietnam. Ecol Indic 2021;127:107790.
120. Nguyen A, Yosinski J, Clune J. Deep neural networks are easily fooled: high confidence predictions for unrecognizable images. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2015. pp. 427-36.
121. Hendrycks D, Gimpel K. A baseline for detecting misclassified and out-of-distribution examples in neural networks. arXiv preprint arXiv: 161002136 2016.
122. Zhang C, Bengio S, Hardt M, Recht B, Vinyals O. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 2021;64:107-15.
