تخمین تراوش سدخاکی با استفاده از مدل ترکیبی یادگیری عمیق و تبدیل موجک

[1] V. Nourani, E. Sharghi, M.H. Aminfar, Integrated ANN model for earthfill dams seepage analysis: Sattarkhan Dam in Iran, Artif. Intell. Res., 1(2) (2012) 22-37.

[2] A. Pourghasemzadeh, M. Mahdavi Adeli, A comparison of the construction of cut-off wall and grouting curtain in the foundation of Gotvand Aliya Dam from an economic and scheduling perspective, in:  5th international Congress on civil engineering, architecture and urban development, 2017. (in Persian)

[3] B. Nemati, H. Shafiee, Numerical modeling of seepage in earth dams using numerical methods, in:  Fourth Conference on New Research in Science and Technology, 2015. (in Persian)

[4] L.N. Reddi, Seepage in soils: principles and applications, John Wiley & Sons, 2003.

[5] E. Sharghi, V. Nourani, N. Behfar, Evaluation and Application of Ensemble AI-based Models for Estimating Piezometric Heads of Earth Fill Dam, Iran-Water Resources Research, 14(4) (2018) 160-169. (in Persian)

[6] G. Tayfur, D. Swiatek, A. Wita, V.P. Singh, Case study: Finite element method and artificial neural network models for flow through Jeziorsko earthfill dam in Poland, Journal of Hydraulic Engineering, 131(6) (2005) 431-440.

[7] D. Ersayın, Studying seepage in a body of earth-fill dam by (Artifical Neural Networks) ANNs, Izmir Institute of Technology, 2006.

[8] X.Y. Miao, J.K. Chu, J. Qiao, L.H. Zhang, Predicting seepage of earth dams using neural network and genetic algorithm, in:  Advanced Materials Research, Trans Tech Publ, 2012, pp. 3081-3085.

[9] P. Coulibaly, C.K. Baldwin, Nonstationary hydrological time series forecasting using nonlinear dynamic methods, Journal of Hydrology, 307(1-4) (2005) 164-174.

[10] S.J. Yoo, J.B. Park, Y.H. Choi, Stable predictive control of chaotic systems using self-recurrent wavelet neural network, international journal of control, automation, and systems, 3(1) (2005) 43-55.

[11] S. Mirabedini, A review on deep learning, in:  Third National Conference on Electrical and Computer Engineering, 2018. (in Persian)

[12] A.U. Muhammad, X. Li, J. Feng, Using LSTM GRU and hybrid models for streamflow forecasting, in:  International Conference on Machine Learning and Intelligent Communications, Springer, 2019, pp. 510-524.

[13] V. Nourani, N. Behfar, Multi-station runoff-sediment modeling using seasonal LSTM models, Journal of Hydrology, 601 (2021) 126672.

[14] C. Park, I.-M. Chung, Evaluating the groundwater prediction using LSTM model, Journal of Korea Water Resources Association, 53(4) (2020) 273-283.

[15] h. siasar, T. honar, Comparison of Performance of GLM, RF and DL Models in Estimation of Reference Evapotranspiration in Zabol Synoptic Station, journal of watershed management research, 11(22) (2020) 210-219.

[16] M. Ishfaque, Q. Dai, N.u. Haq, K. Jadoon, S.M. Shahzad, H.T. Janjuhah, Use of recurrent neural network with long short-term memory for seepage prediction at Tarbela Dam, KP, Pakistan, Energies, 15(9) (2022) 3123.

[17] N. Farboudfam, V. Nourani, B. Aminnejad, Spatial-temporal disaggregation of rainfall time series using wavelet-artificial neural network hybrid model, Iran-Water Resources Research, 14(5) (2019) 490-495. (in Persian)

[18] D.M. Hawkins, The problem of overfitting, Journal of chemical information and computer sciences, 44(1) (2004) 1-12.

[19] B. Cannas, A. Fanni, G. Sias, S. Tronci, M. Zedda, River flow forecasting using neural networks and wavelet analysis, in:  Geophys. Res. Abstr, 2005, pp. 08651.

[20] V. Nourani, A.H. Baghanam, J. Adamowski, O. Kisi, Applications of hybrid wavelet–artificial intelligence models in hydrology: a review, Journal of Hydrology, 514 (2014) 358-377.

[21] K. Roushangar, V. Nourani, M. Dolatshahi, Investigation and trend identification of groundwater level variations using discrete wavelet transform and non-parametric tests (case study: Azarshahr plain), Iran-Water Resources Research, 16(1) (2020) 102-115. (in Persian)

[22] B. Fatehi-Nobarian, S. Fard Moradinia, Wavelet-ANN hybrid model evaluation in seepage prediction in nonhomogeneous earth dams, Water Practice & Technology,  (2024) wpt2024152.

[23] M.G. Zamani, M.R. Nikoo, F. Niknazar, G. Al-Rawas, M. Al-Wardy, A.H. Gandomi, A multi-model data fusion methodology for reservoir water quality based on machine learning algorithms and bayesian maximum entropy, Journal of Cleaner Production, 416 (2023) 137885.

[24] Behavior monitoring report of the Sattar Khan dam project, in: B.C. Engineers (Ed.), 2003. (in Persian)

[25] M. Ghabaei Sough, A. Mosaedi, M. Hesam, A. Hezarjaribi, Evaluation Effect of Input Parameters Preprocessing in Artificial Neural Networks (Anns) by Using Stepwise Regression and Gamma Test Techniques for Fast Estimation of Daily Evapotranspiration, Water and Soil, 24(3) (2010) -.(in Persian)

[26] V. Nourani, Fundamentals of hydroinformatics, University of Tabriz, 2015. (in Persian)

[27] A. Graves, Supervised sequence labelling, in:  Supervised sequence labelling with recurrent neural networks, Springer, 2012, pp. 5-13.

[28] J. Brownlee, Long short-term memory networks with python: develop sequence prediction models with deep learning, Machine Learning Mastery, 2017.

[29] A. Graves, A.-r. Mohamed, G. Hinton, Speech recognition with deep recurrent neural networks, in:  2013 IEEE international conference on acoustics, speech and signal processing, Ieee, 2013, pp. 6645-6649.

[30] C. Olah, Understanding lstm networks–colah’s blog, Colah. github. io,  (2015).

[31] J. Chung, C. Gulcehre, K. Cho, Y. Bengio, Empirical evaluation of gated recurrent neural networks on sequence modeling, arXiv preprint arXiv:1412.3555,  (2014).

[32] Y. Bengio, P. Simard, P. Frasconi, Learning long-term dependencies with gradient descent is difficult, IEEE transactions on neural networks, 5(2) (1994) 157-166.

[33] S. Hochreiter, J. Schmidhuber, Long short-term memory, Neural computation, 9(8) (1997) 1735-1780.

[34] R. Pascanu, C. Gulcehre, K. Cho, Y. Bengio, How to construct deep recurrent neural networks, arXiv preprint arXiv:1312.6026,  (2013).

[35] S. Hochreiter, Recurrent neural net learning and vanishing gradient, International Journal Of Uncertainity, Fuzziness and Knowledge-Based Systems, 6(2) (1998) 107-116.

[36] M. Hermans, B. Schrauwen, Training and analysing deep recurrent neural networks, Advances in neural information processing systems, 26 (2013) 190-198.

[37] F. Daneshvar Vousoughi, R. Shaker, Assessment of Trend in Groundwater Level using Hybrid Mann-Kendall and Wavelet Transform Method (Case Study: Ardabil Plain), Environment and Water Engineering, 4(3) (2018) 241-251. (in Persian)

[38] J. Adamowski, H.F. Chan, A wavelet neural network conjunction model for groundwater level forecasting, Journal of Hydrology, 407(1-4) (2011) 28-40.

[39] O.S. Jahromi, B.A. Francis, R.H. Kwong, Algebraic theory of optimal filterbanks, IEEE transactions on signal processing, 51(2) (2003) 442-457.