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A novel method for lake level prediction: deep echo state network

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Abstract

Accurately prediction of lake level fluctuations is essential for water resources planning and management. In the present study, the potential of a novel method, deep echo state network (Deep ESN), is investigated for monthly lake level prediction and its results are compared with three data-driven methods, artificial neural networks (ANNs), extreme learning machine (ELM), and regression tree (Reg. Tree). The methods are validated using root mean square errors (RMSE), determination coefficient (R2), and Nash-Sutcliffe efficiency (NSE) criteria. The investigated method (Deep ESN) outperforms the ELM, ANNs, and Reg. Tree by improving accuracies by 61–62–96%, 10–14–84%, and 8–23–80% in prediction 1 month, 2 months, and 3 months ahead lake level fluctuations in terms of RMSE criteria, respectively. Also, accuracy of ELM, ANNs, and Reg. Tree was significantly increased using Deep ESN model by 1.1–1.1–443%, 1.1–1.6–250%, and 1.6–6.5–184% in terms of NSE indicator for different lead-time horizons. Among the ELM, ANNs, and Reg. Tree, the third method provides the worst predictions while the first method performs superior to the second one in all tree time horizons.

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References

  • Agana NA, Homaifar A (2017) A deep learning based approach for long-term drought prediction. In Southeast Con., IEEE, 1-8

  • Alizamir M, Kisi O, Zounemat-Kermani M (2018) Modelling long-term groundwater fluctuations by extreme learning machine using hydro-climatic data. Hydrol Sci J 63(1):63–73

    Google Scholar 

  • Alizamir M, Kim S et al (2020a) Deep echo state network: a novel machine learning approach to model dew point temperature using meteorological variables. Hydrol Sci J 65(7):1173–1190. https://doi.org/10.1080/02626667.2020.1735639

  • Alizamir M, Kisi O, Ahmed AN, Mert C, Fai CM, Kim S, Kim NW, el-Shafie A (2020b) Advanced machine learning model for better prediction accuracy of soil temperature at different depths. PLoS One 15(4):e0231055

    Google Scholar 

  • Alizamir M, Kim S et al (2020c) A comparative study of several machine learning based non-linear regression methods in estimating solar radiation: Case studies of the USA and Turkey regions. Energy 68:117239. https://doi.org/10.1016/j.energy.2020.117239

    Article  Google Scholar 

  • Alizamir M, Kisi O et al (2020d) Modelling reference evapotranspiration by combining neuro-fuzzy and evolutionary strategies. Acta Geophys:1–14

  • Al-Musaylh MS, Deo RC, Adamowski JF, Li Y (2018) Short-term electricity demand forecasting with MARS, SVR and ARIMA models using aggregated demand data in Queensland, Australia. Adv Eng Inform 35:1–16. https://doi.org/10.1016/j.aei.2017.11.002

    Article  Google Scholar 

  • Al-Musaylh MS, Deo RC, Adamowski JF, Li Y (2019) Short-term electricity demand forecasting using machine learning methods enriched with ground-based climate and ECMWF Reanalysis atmospheric predictors in southeast Queensland, Australia. Renew Sust Energ Rev 113:109293. https://doi.org/10.1016/j.rser.2019.109293

    Article  Google Scholar 

  • Altunkaynak A (2007) Forecasting surface water level fluctuations of Lake Van by artificial neural networks. Water Resour Manag 21(2):399–408

    Google Scholar 

  • Aytek A, Kisi O, Guven A (2014) A genetic programming technique for lake level modeling. Hydrol Res 45(4-5):529–539

    Google Scholar 

  • Bacciu D, Chessa S, Gallicchio C, Micheli A, Pedrelli L, Ferro E, Fortunati L, La Rosa D, Palumbo F, Vozzi F, Parodi O (2017) A learning system for automatic Berg Balance Scale score estimation. Eng Appl Artif Intell 66:60–74. https://doi.org/10.1016/j.engappai.2017.08.018

    Article  Google Scholar 

  • Bai Y, Chen Z, Xie J, Li C (2016) Daily reservoir inflow forecasting using multiscale deep feature learning with hybrid models. J Hydrol 532:193–206

    Google Scholar 

  • Bisoi R, Dash PK, Parida AK (2019) Hybrid variational mode decomposition and evolutionary robust kernel extreme learning machine for stock price and movement prediction on daily basis. Appl Soft Comput J 74:652–678. https://doi.org/10.1016/j.asoc.2018.11.008

    Article  Google Scholar 

  • Brauns M, Garcia XF, Pusch MT (2008) Potential effects of water-level fluctuations on littoral invertebrates in lowland lakes. In: Ecological effects of water-level fluctuations in lakes. Springer, Dordrecht, pp 5–12

    Google Scholar 

  • Breiman L, Friedman J, Stone CJ, Olshen RA (1984) Classification and regression trees. CRC press

  • Breiman L, Friedman JH, Olshen RA, Stone CJ (1998) Classification and regression trees. Chapman and Hall/CRC, Boca Raton

    Google Scholar 

  • Butcher JB, Verstraeten D, Schrauwen B, Day CR, Haycock PW (2013) Reservoir computing and extreme learning machines for non-linear time-series data analysis. Neural Netw 38:76–89. https://doi.org/10.1016/j.neunet.2012.11.011

    Article  Google Scholar 

  • Buyukyildiz M, Tezel G, Yilmaz V (2014) Estimation of the change in lake water level by artificial intelligence methods. Water Resour Manag 28(13):4747–4763

    Google Scholar 

  • Cabral AIR, Vasconcelos MJP, Pereira JMC, Martins E, Bartholomé E (2006) A land cover map of Southern hemisphere Africa based on SPOT-4 Vegetation data. Int J Remote Sens 6(20):1053–1074. https://doi.org/10.1080/01431160500307409

    Article  Google Scholar 

  • Cabral AIR, Silva S, Silva PC, Vanneschi L, Vasconcelos MJ (2018) Burned area estimations derived from Landsat ETM+ and OLI data: comparing genetic programming with maximum likelihood and classification and regression trees. ISPRS J Photogramm Remote Sens 142:94–105. https://doi.org/10.1016/j.isprsjprs.2018.05.007

    Article  Google Scholar 

  • Chen J, Jin Q, Chao J (2012) Design of deep belief networks for short-term prediction of drought index using data in the Huaihe river basin. Math Probl Eng 2012:235929

    Google Scholar 

  • Chong LY, Ong TS, Teoh ABJ (2019) Feature fusions for 2.5D face recognition in Random Maxout Extreme Learning Machine. Appl Soft Comput J 75:358–372. https://doi.org/10.1016/j.asoc.2018.11.024

    Article  Google Scholar 

  • Çimen M, Kisi O (2009) Comparison of two different data-driven techniques in modeling lake level fluctuations in Turkey. J Hydrol 378(3-4):253–262

    Google Scholar 

  • Coe MT, Foley JA (2001) Human and natural impacts on the water resources of the Lake Chad basin. J Geophys Res-Atmos 106(D4):3349–3356

    Google Scholar 

  • Crisostomi E, Gallicchio C, Micheli A, Raugi M, Tucci M (2015) Prediction of the Italian electricity price for smart grid applications. Neurocomputing 170:286–295. https://doi.org/10.1016/j.neucom.2015.02.089

    Article  Google Scholar 

  • Das M, Ghosh SK, Chowdary VM, Saikrishnaveni A, Sharma RK (2016) A probabilistic nonlinear model for forecasting daily water level in reservoir. Water Resour Manag 30(9):3107–3122

    Google Scholar 

  • Deo RC, Şahin M (2017) Forecasting long-term global solar radiation with an ANN algorithm coupled with satellite-derived (MODIS) land surface temperature (LST) for regional locations in Queensland. Renew Sust Energ Rev 72:828–848. https://doi.org/10.1016/j.rser.2017.01.114

    Article  Google Scholar 

  • Deo RC, Şahin M, Adamowski JF, Mi J (2019) Universally deployable extreme learning machines integrated with remotely sensed MODIS satellite predictors over Australia to forecast global solar radiation: a new approach. Renew Sust Energ Rev 104:235–261. https://doi.org/10.1016/j.rser.2019.01.009

    Article  Google Scholar 

  • Gallicchio C, Micheli A (2011) Architectural and markovian factors of echo state networks. Neural Netw 24(5):440–456. https://doi.org/10.1016/j.neunet.2011.02.002

    Article  Google Scholar 

  • Gallicchio C, Micheli A (2013) Tree echo state networks. Neurocomputing 101:319–337. https://doi.org/10.1016/j.neucom.2012.08.017

    Article  Google Scholar 

  • Gallicchio C, Micheli A (2019) Deep reservoir neural networks for trees. Inf Sci 480:174–193. https://doi.org/10.1016/j.ins.2018.12.052

    Article  Google Scholar 

  • Gallicchio C, Micheli A, Pedrelli L (2017) Deep reservoir computing: a critical experimental analysis. Neurocomputing 268:87–99. https://doi.org/10.1016/j.neucom.2016.12.089

    Article  Google Scholar 

  • Gallicchio C, Micheli A, Pedrelli L (2018a) Design of deep echo state networks. Neural Netw 108:33–47. https://doi.org/10.1016/j.neunet.2018.08.002

    Article  Google Scholar 

  • Gallicchio C, Micheli A, Silvestri L (2018b) Local lyapunov exponents of deep echo state networks. Neurocomputing 298:34–45. https://doi.org/10.1016/j.neucom.2017.11.073

    Article  Google Scholar 

  • Ghorbani MA, Deo RC, Karimi V, Yaseen ZM, Terzi O (2018) Implementation of a hybrid MLP-FFA model for water level prediction of Lake Egirdir, Turkey. Stoch Env Res Risk A 32(6):1683–1697

    Google Scholar 

  • Giuliani C, Veisz AC, Piccinno M, Recanatesi F (2019) Estimating vulnerability of water body using Sentinel-2 images and environmental modelling: the study case of Bracciano Lake (Italy). Eur J Remote Sens 52(sup4):64–73

    Google Scholar 

  • Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT press

  • Ha JH, Lee YH, Kim YH (2016) Forecasting the precipitation of the next day using deep learning. J Korean Inst Intell Syst 26(2):93–98

    Google Scholar 

  • Haykin S (1999) Neural networks a comprehensive foundation. Prentice Hall, Upper Saddle River

    Google Scholar 

  • Hornik K (1991) Approximation capabilities of multilayer feedforward networks. Neural Netw 4:251–257. https://doi.org/10.1016/0893-6080(91)90009-T

    Article  Google Scholar 

  • Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2:359–366. https://doi.org/10.1016/0893-6080(89)90020-8

    Article  Google Scholar 

  • Hu W, Zhai S, Zhu Z, Han H (2008) Impacts of the Yangtze River water transfer on the restoration of Lake Taihu. Ecol Eng 34(1):30–49

    Google Scholar 

  • Hu C, Wu Q, Li H, Jian S, Li N, Lou Z (2018) Deep learning with a long short-term memory networks approach for rainfall-runoff simulation. Water 10(11):1543

    Google Scholar 

  • Huang GB, Chen L, Siew CK (2006a) Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans Neural Netw 17(4):879–892. https://doi.org/10.1109/TNN.2006.875977

    Article  Google Scholar 

  • Huang GB, Zhu QY, Siew CK (2006b) Extreme learning machine: theory and applications. Neurocomputing 70(1-3):489–501. https://doi.org/10.1016/j.neucom.2005.12.126

    Article  Google Scholar 

  • Huang GB, Zhou H, Ding X, Zhang R (2011) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern Part B (Cybernetics) 42(2):513–529. https://doi.org/10.1109/TSMCB.2011.2168604

    Article  Google Scholar 

  • Im J, Lu Z, Rhee J, Quackenbush LJ (2012) Impervious surface quantification using a synthesis of artificial immune networks and decision/regression trees from multi-sensor data. Remote Sens Environ 117:102–113. https://doi.org/10.1016/j.rse.2011.06.024

    Article  Google Scholar 

  • Jaeger H (2001) The “echo state” approach to analyzing and training recurrent neural networks. Technical report GMD-German National Research Institute for Computer Science

  • Jaeger H (2002a) Adaptive nonlinear system identification with echo state networks. In: Advances in neural information processing systems: Vol. 15. NIPS. MIT Press, pp 593–600

  • Jaeger H (2002b) Short term memory in echo state networks. GMD-report 152. GMD-German National Research Institute for Computer Science

  • Jaeger H. (2002c) Tutorial on training recurrent neural networks, covering BPTT, RURL, EKF and the Echo State Network Approach. Technical Report GMD Report 159, 2002

  • Jalili S, Hamidi SA, Namdar Ghanbari R (2016) Climate variability and anthropogenic effects on Lake Urmia water level fluctuations, northwestern Iran. Hydrol Sci J 61(10):1759–1769

    Google Scholar 

  • Kakahaji H, Banadaki HD, Kakahaji A, Kakahaji A (2013) Prediction of Urmia Lake water-level fluctuations by using analytical, linear statistic and intelligent methods. Water Resour Manag 27(13):4469–4492

    Google Scholar 

  • Karimi S, Shiri J, Kisi O, Makarynskyy O (2012) Forecasting water level fluctuations of Urmieh Lake using gene expression programming and adaptive neuro-fuzzy inference system. Int J Ocean Clim Syst 3(2):109–125

    Google Scholar 

  • Khatibi R, Ghorbani MA, Naghipour L, Jothiprakash V, Fathima TA, Fazelifard MH (2014) Inter-comparison of time series models of lake levels predicted by several modeling strategies. J Hydrol 511:530–545

    Google Scholar 

  • Kisi O (2009) Neural network and wavelet conjunction model for modelling monthly level fluctuations in Turkey. Hydrol Process Int J 23(14): 2081–2092.

  • Kisi O, Alizamir M (2018) Modelling reference evapotranspiration using a new wavelet conjunction heuristic method: wavelet extreme learning machine vs wavelet neural networks. Agric For Meteorol 263:41–48

    Google Scholar 

  • Kisi O, Shiri J, Nikoofar B (2012) Forecasting daily lake levels using artificial intelligence approaches. Comput Geosci 41:169–180

    Google Scholar 

  • Kisi O, Shiri J, Karimi S, Shamshirband S, Motamedi S, Petković D, Hashim R (2015) A survey of water level fluctuation predicting in Urmia Lake using support vector machine with firefly algorithm. Appl Math Comput 270:731–743

    Google Scholar 

  • Kisi O, Alizamir M, Docheshmeh Gorgij A (2020) Dissolved oxygen prediction using a new ensemble method. Environ Sci Pollut Res 27:9589–9603. https://doi.org/10.1007/s11356-019-07574-w

    Article  Google Scholar 

  • Li B, Yang G, Wan R, Dai X, Zhang Y (2016a) Comparison of random forests and other statistical methods for the prediction of lake water level: a case study of the Poyang Lake in China. Hydrol Res 47(S1):69–83

    Google Scholar 

  • Li C, Bai Y, Zeng B (2016b) Deep feature learning architectures for daily reservoir inflow forecasting. Water Resour Manag 30(14):5145–5161

    Google Scholar 

  • Liu F, Xu F, Yang S (2017) A flood forecasting model based on deep learning algorithm via integrating stacked autoencoders with BP neural network. In 2017 IEEE third International conference on multimedia big data (BigMM), IEEE, 58-61

  • Lv L, Wang W, Zhang Z, Liu X (2020) A novel intrusion detection system based on an optimal hybrid kernel extreme learning machine. Knowl-Based Syst 195:105648. https://doi.org/10.1016/j.knosys.2020.105648

    Article  Google Scholar 

  • Maass W, Natschläger T, Markram H (2002) Real-time computing without stable states: a new framework for neural computation based on perturbations. Neural Comput 14(11):2531–2560. https://doi.org/10.1162/089976602760407955

    Article  Google Scholar 

  • Marçais J, De Dreuzy JR (2017) Prospective interest of deep learning for hydrological inference. Groundwater 55(5):688–692

    Google Scholar 

  • Mora B, Wulder MA, White JC (2010) Segment-constrained regression tree estimation of forest stand height from very high spatial resolution panchromatic imagery over a boreal environment. Remote Sens Environ 114(11):2474–2484. https://doi.org/10.1016/j.rse.2010.05.022

    Article  Google Scholar 

  • Morgan JN, Sonquist JA (1963) Problems in the analysis of survey data, and a proposal. J Am Stat Assoc 58(302):415–434. https://doi.org/10.1080/01621459.1963.10500855

    Article  Google Scholar 

  • Munyati C (2019) Comparative performance of regression tree and parametric classification of savannah woody cover on SPOT 6 NAOMI imagery. Remote Sens Appl Soc Environ 13:171–182. https://doi.org/10.1016/j.rsase.2018.10.015

    Article  Google Scholar 

  • Pierdzioch C, Risse M, Gupta R, Nyakabawo W (2018) On REIT returns and (un-) expected inflation: empirical evidence based on Bayesian additive regression trees. Financ Res Lett 30:160–169. https://doi.org/10.1016/j.frl.2018.09.010

    Article  Google Scholar 

  • Pouteau R, Rambal S, Ratte JP, Gogé F, Joffre R, Winkel T (2011) Downscaling MODIS-derived maps using GIS and boosted regression trees: the case of frost occurrence over the arid Andean highlands of Bolivia. Remote Sens Environ 115(1):117–129. https://doi.org/10.1016/j.rse.2010.08.011

    Article  Google Scholar 

  • Prasad R, Deo RC, Li Y, Maraseni T (2019) Weekly soil moisture forecasting with multivariate sequential, ensemble empirical mode decomposition and Boruta-random forest hybridizer algorithm approach. Catena 177:149–166. https://doi.org/10.1016/j.catena.2019.02.012

    Article  Google Scholar 

  • Prater A (2017) Spatiotemporal signal classification via principal components of reservoir states. Neural Netw 91:66–75. https://doi.org/10.1016/j.neunet.2017.04.008

    Article  Google Scholar 

  • Rajpal A, Mishra A, Bala R (2019) A novel fuzzy frame selection based watermarking scheme for MPEG-4 videos using Bi-directional extreme learning machine. Appl Soft Comput 74:603–620. https://doi.org/10.1016/j.asoc.2018.10.043

    Article  Google Scholar 

  • Sen Z, Kadioglu M, Batur E (2000) Stochastic modelling of the Van Lake monthly level fluctuations in Turkey. Theor Appl Climatol 65:99–110

    Google Scholar 

  • Shafaei M, Kisi O (2016) Lake level forecasting using wavelet-SVR, wavelet-ANFIS and wavelet-ARMA conjunction models. Water Resour Manag 30(1):79–97

    Google Scholar 

  • Shen C (2018) A transdisciplinary review of deep learning research and its relevance for water resources scientists. Water Resour Res 54(11):8558–8593

    Google Scholar 

  • Shiri J, Shamshirband S, Kisi O, Karimi S, Bateni SM, Nezhad SHH, Hashemi A (2016) Prediction of water-level in the Urmia Lake using the extreme learning machine approach. Water Resour Manag 30(14):5217–5229

    Google Scholar 

  • Song Y, He B, Liu P, Yan T (2019) Side scan sonar image segmentation and synthesis based on extreme learning machine. Appl Acoust 146:56–65. https://doi.org/10.1016/j.apacoust.2018.10.031

    Article  Google Scholar 

  • Talebizadeh M, Moridnejad A (2011) Uncertainty analysis for the forecast of lake level fluctuations using ensembles of ANN and ANFIS models. Expert Syst Appl 38(4):4126–4135

    Google Scholar 

  • Tao Y, Gao X, Hsu K, Sorooshian S, Ihler A (2016) A deep neural network modeling framework to reduce bias in satellite precipitation products. J Hydrometeorol 17(3):931–945

    Google Scholar 

  • Teshome FB (2020) Seasonal water quality index and suitability of the water body to designated uses at the eastern catchment of Lake Hawassa. Environ Sci Pollut Res 27(1):279–290

    Google Scholar 

  • Üneş F, Demirci M, Kişi Ö (2015) Prediction of Millers Ferry Dam reservoir level in USA using artificial neural network. Period Polytech Civ Eng 59(3):309–318

    Google Scholar 

  • Vaheddoost B, Aksoy H, Abghari H (2016) Prediction of water level using monthly lagged data in Lake Urmia, Iran. Water Resour Manag 30(13):4951–4967

    Google Scholar 

  • Vasistha P, Ganguly R (2020) Water quality assessment of natural lakes and its importance: An overview. Materials Today: Proceedings, in press

  • Vuglinskiy V (2009) Water Level: water level in lakes and reservoirs, water storage, Assessment of the status of the development of the standards for the terrestrial essential climate variables, Global Terrestrial Observing System (GTOS), Rome, Italy, pp 26

  • Wantzen KM, Rothhaupt KO, Mörtl M, Cantonati M, László G, Fischer P (2008) Ecological effects of water-level fluctuations in lakes: an urgent issue. In: Ecological effects of water-level fluctuations in lakes. Springer, Dordrecht, pp 1–4

    Google Scholar 

  • Wyffels F, Schrauwen B (2010) A comparative study of reservoir computing strategies for monthly time series prediction. Neurocomputing 73(10-12):1958–1964. https://doi.org/10.1016/j.neucom.2010.01.016

    Article  Google Scholar 

  • Yadav B, Eliza K (2017) A hybrid wavelet-support vector machine model for prediction of lake water level fluctuations using hydro-meteorological data. Measurement 103:294–301

    Google Scholar 

  • Yang L, Feng Q, Yin Z, Deo RC, Wen X, Si J, Liu W (2020) Regional hydrology heterogeneity and the response to climate and land surface changes in arid alpine basin, northwest China. Catena 187:104345. https://doi.org/10.1016/j.catena.2019.104345

    Article  Google Scholar 

  • Yarar A, Onucyıldız M, Copty NK (2009) Modelling level change in lakes using neuro-fuzzy and artificial neural networks. J Hydrol 365(3-4):329–334

    Google Scholar 

  • Young CC, Liu WC, Hsieh WL (2015) Predicting the water level fluctuation in an alpine lake using physically based, artificial neural network, and time series forecasting models. Math Probl Eng 2015:708204

    Google Scholar 

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Authors and Affiliations

  1. Department of Civil Engineering, Hamedan Branch, Islamic Azad University, Hamedan, Iran

    Meysam Alizamir

  2. Faculty of Natural Sciences and Engineering, Ilia State University, Tbilisi, Georgia

    Ozgur Kisi

  3. Department of Railroad Construction and Safety Engineering, Dongyang University, Yeongju, 36040, Republic of Korea

    Sungwon Kim

  4. Faculty of Science, Agronomy Department, Hydraulics Division, Laboratory of Research in Biodiversity Interaction Ecosystem and Biotechnology, University 20 Août 1955, Route El Hadaik, 26, Skikda, BP, Algeria

    Salim Heddam

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  1. Meysam Alizamir
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Alizamir, M., Kisi, O., Kim, S. et al. A novel method for lake level prediction: deep echo state network. Arab J Geosci 13, 956 (2020). https://doi.org/10.1007/s12517-020-05965-9

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