**References**

- J.T. Huang, Research on the Response Mechanism of Evapotranspiration in Semi-arid Area to Groundwater Changes, J. Chang’an University, 2013.
- C.W. Chai, Z.R. Jiang, X.Y. Xu, W.D. Tang, W.W. Chai, L.J. Li, C.X. Li, Determination of land desertification types in the desert oasis transition zone in Minqin County, J. N. Forest. Univ., 6 (2006) 12–16.
- Y. Qiao, X.J. Liang, Y.B. Wang, Application and comparative study of two models in groundwater burial depth prediction, Water Con. Irrig., 3 (2014) 45–47, 53.
- C.H. Xiao, Y.G. Hao, P.Y. Jia, Changes of water factors in Dengkou Oasis in the northeast of Ulan Buhe Desert in recent 52 years, Ari. Land Res. Environ., 6 (2008) 161–165.
- P.P. Adhikary, Ch.J. Dash, Comparison of deterministic and stochastic methods to predict spatial variation of groundwater depth, Appl. Sci., 7 (2017) 339–348.
- K. Al-Mahallawi, J. Mania, A. Hani, I. Shahrour, Using of neural networks for the prediction of nitrate groundwater contamination in rural and agricultural areas, Environ. Earth Sci., 65 (2012) 917–928.
- P. Sucharita, K. Shiv, Y. Kumar, C.S. Harish, Assessment of groundwater utilization status and prediction of water table depth using different heuristic models in an Indian interbasin, J. Soft Comput., 23 (2019) 10261–10285.
- M. Khorasani, M. Ehteshami, H. Ghadimi, M. Salari, Simulation and analysis of temporal changes of groundwater depth using time series modeling, Model. Earth Syst. Environ., 2 (2016) 90, doi: 10.1007/s40808-016-0164-0.
- P.R. Maiti, J. Medha, M.S. Sabita, Comparative analysis of performance of neural network and neuro-fuzzy model in prediction of groundwater table fluctuation, Int. J. Hydrol. Sci. Technol., 2 (2012) 252–269.
- D. Liu, G.X. Li, Q. Fu, M. Li, C.L. Liu, A.F. Muhammad, I.K. Muhammad, T.X. Li, S. Cui, Application of particle swarm optimization and extreme learning machine forecasting models for regional groundwater depth using nonlinear prediction models as preprocessor, J. Hydrol. Eng., 23 (2018), doi: 10.1061/(ASCE)HE.1943-5584.0001711.
- T. Zhou, F.X. Wang, Z. Yang, Comparative analysis of ANN and SVM models combined with wavelet preprocess for groundwater depth prediction, Water, 9 (2017) 781, doi: 10.3390/w9100781.
- G.-C. Shao, K. Zhang, Z.-Y. Wang, X.-J. Lu, Groundwater depth prediction model based on IABC-RBF neural network, J. Zhejiang Univ. (Eng. Sci.), 53 (2019) 1323–1330.
- C.F. Zhang, H.R. Chen, Z.Q. Yue, Groundwater burial depth simulation prediction based on long and short term memory network (LSTM) - an example analysis of Guanzhong Plain, Chin. Rur. Water Con. Hydro., (2020) 127–131+137.
- H.J. Yu, X.H. Wen, Q. Feng, Z.L. Yin, Z.Q. Chang, T.F. Yu, X.Y. Niu, Using wavelet transform and support vector machine coupling model (WA-SVM) to predict groundwater depth in arid areas, Chin. Des., 36 (2016) 1435–1442.
- Q.Z. Liang, L.Q.L.D. Wang, G.G. Li. Regional groundwater burial depth PSO-ELM prediction model based on EEMD, Water Res. Hydrol. Technol., 51 (2020) 45–51.
- J.R. Zhang, H.M. Tang, T. Wen, J.W. Ma, Q.W. Tan, D. Xia, X. Liu, Y.Q. Zhang, A hybrid landslide displacement prediction method based on CEEMD and DTW-ACO-SVR—cases studied in the Three Gorges Reservoir Area, Sensors, 20 (2020) 4287, doi: 10.3390/s20154287.
- Y.T. Sang, X.H. Zhao, X.P. Zhu, D.J. Xi, Monthly runoff prediction of the upper Fen River based on CEEMD- BP model, Yellow River, 41 (2019) 1–5.
- N.E. Huang, Z. Shen, S.R. Long, M.C. Wu, H.H. Shih, Q.N. Zheng, N.-C. Yen, C.C. Tung, H.H. Liu, The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proc. R. Soc. London, Ser. A, 454 (1998) 903–995.
- J.-R. Yeh, J.-S. Shieh, N.-E. Huang, Complementary ensemble empirical mode decomposition: a novel noise enhanced data analysis method, Adv. Adapt. Data Anal., 2 (2010) 135–156.
- J. Wang, W.D. Li, Ultra-short-term wind speed prediction based on CEEMD and GWO, Pow. Sys. Pro. Con., 46 (2018) 69–74.
- X. Chen, X.F. Wang, W.Y. Qi, T. Zhou, Application of BP neural network model based on genetic algorithm in groundwater depth prediction: taking Mengcheng County as an example, Water Res. Hydrol. Technol., 49 (2018) 1–7.
- B. Zhang, J.M. Liu, Groundwater dynamic prediction based on BP neural network, Res. Soil. Water Con., 19 (2012) 235–237.
- L. Xu, P. Li, Aero-engine performance parameter prediction based on dynamic neural network, J. Binzhou Univ., 31 (2015) 23–27.