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2020 | OriginalPaper | Buchkapitel

New Formulation for Predicting Daily Reference Evapotranspiration (ET₀) in the Mediterranean Region of Algeria Country: Optimally Pruned Extreme Learning Machine (OPELM) Versus Online Sequential Extreme Learning Machine (OSELM)

verfasst von : Salim Heddam, Ozgur Kisi, Abderrazek Sebbar, Larbi Houichi, Lakhdar Djemili

Erschienen in: Water Resources in Algeria - Part I

Verlag: Springer International Publishing

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Abstract

This chapter aims to investigate the capabilities and usefulness of two new data-driven techniques: optimally pruned extreme learning machine (OPELM) and online sequential extreme learning machine (OSELM) newly applied and compared for predicting daily reference evapotranspiration (ET₀) in the Mediterranean region of Algeria. Using large data sets from east to west regions of Algeria, the models were developed using several well-known climatic variables as inputs: daily maximum and minimum air temperatures, wind speed, and relative humidity. The proposed models were compared using several well-known statistical indexes: root mean square error (RMSE), mean absolute error (MAE), and coefficient of correlation (R). The obtained results have shown that all the proposed models present high prediction accuracy and the OPELM models provide better overall performances compared to the OSELM models

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Vorheriges Kapitel Comparison of Evolving Connectionist Systems (ECoS) and Neural Networks for Modelling Daily Pan Evaporation from Algerian Dam Reservoirs

Nächstes Kapitel Water Resources in Coastal Aquifers of Algeria Face Climate Variability: Case of Alluvial Aquifer of Mitidja in Algeria

Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration-guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper 56FAO. Food and Agriculture Organization of the United Nations, Rome, Italy

Xing W, Wang W, Shao Q, Peng S, Yu Z, Yong B, Taylor J (2014) Changes of reference evapotranspiration in the Haihe River Basin: present observations and future projection from climatic variables through multi-model ensemble. Glob Planet Change 115:1–15. https://doi.org/10.1016/j.gloplacha.2014.01.004CrossRef

Feng Y, Jia Y, Cui N, Zhao L, Li C, Gong D (2017) Calibration of Hargreaves model for reference evapotranspiration estimation in Sichuan basin of southwest China. Agric Water Manag 181:1–9. https://doi.org/10.1016/j.agwat.2016.11.010CrossRef

Keshtegar B, Kisi O, Arab HG, Zounemat-Kermani M (2018) Subset modeling basis ANFIS for prediction of the reference evapotranspiration. Water Resour Manag 32:1101–1116CrossRef

Karbasi M (2018) Forecasting of multi-step ahead reference evapotranspiration using wavelet-Gaussian process regression model. Water Resour Manag 32:1035–1052CrossRef

Mattar MA (2018) Using gene expression programming in monthly reference evapotranspiration modeling: a case study in Egypt. Agric Water Manag 198:28–38. https://doi.org/10.1016/j.agwat.2017.12.017CrossRef

Yin Z, Feng Q, Yang L, Deo RC, Wen X, Si J, Xiao S (2017) Future projection with an extreme-learning machine and support vector regression of reference evapotranspiration in a mountainous inland watershed in north-west China. Water 9(11):880. https://doi.org/10.3390/w9110880CrossRef

Landeras G, Bekoe E, Ampofo J, Logah F, Diop M, Cisse M, Shiri J (2018) New alternatives for reference evapotranspiration estimation in West Africa using limited weather data and ancillary data supply strategies. Theor Appl Climatol 132:701–716. https://doi.org/10.1007/s00704-017-2120-yCrossRef

Sanikhani H, Kisi O, Maroufpoor E, Yaseen ZM (2018) Temperature-based modeling of reference evapotranspiration using several artificial intelligence models: application of different modeling scenarios. Theor Appl Climatol 135:449–462CrossRef

10.

Shiri J (2017) Evaluation of FAO56-PM, empirical, semi-empirical and gene expression programming approaches for estimating daily reference evapotranspiration in hyper-arid regions of Iran. Agric Water Manag 188:101–114CrossRef

11.

Yin Z, Wen X, Feng Q, He Z, Zou S, Yang L (2017) Integrating genetic algorithm and support vector machine for modeling daily reference evapotranspiration in a semi-arid mountain area. Hydrol Res 48(5):1171–1191CrossRef

12.

Feng Y, Peng Y, Cui N, Gong D, Zhang K (2017) Modeling reference evapotranspiration using extreme learning machine and generalized regression neural network only with temperature data. Comput Electron Agric 136:71–78CrossRef

13.

Feng Y, Cui N, Gong D, Zhang Q, Zhao L (2017) Evaluation of random forests and generalized regression neural networks for daily reference evapotranspiration modelling. Agric Water Manag 193:163–173CrossRef

14.

Mehdizadeh S, Behmanesh J, Khalili K (2017) Using MARS, SVM, GEP and empirical equations for estimation of monthly mean reference evapotranspiration. Comput Electron Agric 139:103–114. https://doi.org/10.1016/j.compag.2017.05.002CrossRef

15.

Feng Y, Cui N, Zhao L, Hud X, Gong D (2016) Comparison of ELM, GANN, WNN and empirical models for estimating reference evapotranspiration in humid region of Southwest China. J Hydrol 536:376–383CrossRef

16.

Ladlani I, Houichi L, Djemili L, Heddam S, Belouz K (2012) Modeling daily reference evapotranspiration (ET₀) in the North of Algeria using generalized regression neural networks (GRNN) and radial basis function neural networks (RBFNN): a comparative study. Meteorol Atmos Phys 118:163–178. https://doi.org/10.1007/s00703-012-0205-9CrossRef

17.

Ladlani I, Houichi L, Djemili L, Heddam S, Belouz K (2014) Estimation of daily reference evapotranspiration (ET₀) in the North of Algeria using adaptive neuro-fuzzy inference system (ANFIS) and multiple linear regression (MLR) models: a comparative study. Arab J Sci Eng 39:5959–5969. https://doi.org/10.1007/s13369-014-1151-2CrossRef

18.

Laaboudi A, Mouhouche B, Draoui B (2012) Neural network approach to reference evapotranspiration modeling from limited climatic data in arid regions. Int J Biometeorol 56:831–841. https://doi.org/10.1007/s00484-011-0485-7CrossRef

19.

Wu L, Fan J (2019) Comparison of neuron-based, kernel-based, tree-based and curve-based machine learning models for predicting daily reference evapotranspiration. PloS one 14(5):e0217520CrossRef

20.

Huang G, Wu L, Ma X, Zhang W, Fan J, Yu X, Zeng W, Zhou H (2019) Evaluation of CatBoost method for prediction of reference evapotranspiration in humid regions. J Hydrol 574:1029–1041CrossRef

21.

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)90020-8CrossRef

22.

Huang GB, Chen L, Siew CK (2006) 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.2006.875977CrossRef

23.

Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501CrossRef

24.

Miche Y, Sorjamaa A, Lendasse A (2008) OP-ELM: theory, experiments and a toolbox. In: Proceedings of the international conference on artificial neural networks. Lecture notes in computer science, vol 5163. Springer, Prague, pp 145–154

25.

Miche Y, Bas P, Jutten C, Simula O, Lendasse A (2008) A methodology for building regression models using extreme learning machine: OP-ELM. In: ESANN 2008, European symposium on artificial neural networks, Apr 23–25, Bruges, Belgium

26.

Miche Y, Sorjamaa A, Bas P, Simula O, Jutten C, Lendasse A (2010) OP-ELM: optimally pruned extreme learning machine. IEEE Trans Neural Netw 21(1):158–162CrossRef

27.

Liang NY, Huang GB, Saratchandran P, Sundararajan N (2006) A fast and accurate online sequential learning algorithm for feedforward networks. IEEE Trans. Neural Netw 17:1411–1423. https://doi.org/10.1109/TNN.2006.880583CrossRef

28.

Huang GB, Wang DH, Lan Y (2011) Extreme learning machines: a survey. Int J Mach Learn Cybern 2:107–122. https://doi.org/10.1007/s13042-011-0019-yCrossRef

29.

Similä T, Tikka J (2005) Multiresponse sparse regression with application to multidimensional scaling. In: Artificial neural networks: formal models and their applications-ICANN, vol 3697, Springer, Berlin, pp 97–102

30.

Efron B, Hastie T, Johnstone I, Tibshirani R (2004) Least angle regression. Ann Stat 32:407–499. https://doi.org/10.1214/009053604000000067CrossRef

31.

Rezaie-Balf M, Kisi O (2018) New formulation for forecasting streamflow: evolutionary polynomial regression vs. extreme learning machine. Hydrol Res 49(3):939–953. https://doi.org/10.2166/nh.2017.283CrossRef

32.

Heddam S (2016) Use of optimally pruned extreme learning machine (OP-ELM) in forecasting dissolved oxygen concentration (DO) several hours in advance: a case study from the Klamath River, Oregon, USA. Environ Process 3:909–937CrossRef

33.

Shihabudheen KV, Mahesh M, Pillai GN (2018) Particle swarm optimization based extreme learning neuro-fuzzy system for regression and classification. Expert Syst Appl 92:474–484. https://doi.org/10.1016/j.eswa.2017.09.037CrossRef

34.

Alencar AS, Neto ARR, Gomes JPP (2016) A new pruning method for extreme learning machines via genetic algorithms. Appl Soft Comput 44:101–107CrossRef

35.

Guo J, He B, Sha Q (2018) Shallow-sea application of an intelligent fusion module for low-cost sensors in AUV. Ocean Eng 148:386–400CrossRef

36.

Guo J, He B, Duan H (2018) Intelligent assistance positioning methodology based on modified iSAM for AUV using low-cost sensors. Ocean Eng 152:36–46CrossRef

37.

Heddam S, Kisi O (2017) Extreme learning machines: a new approach for modeling dissolved oxygen (DO) concentration with and without water quality variables as predictors. Environ Sci Pollut Res 24(20):16702–16724. https://doi.org/10.1007/s11356-017-9283-zCrossRef

38.

Xu S, Wang J (2016) A fast incremental extreme learning machine algorithm for data streams classification. Expert Syst Appl 65:332–344CrossRef

39.

Xue J, Zhou S, Liu Q, Liu X, Yin J (2018) Financial time series prediction using ℓ_{2, 1} RF-ELM. Neurocomputing 277:176–186. https://doi.org/10.1016/j.neucom.2017.04.076CrossRef

40.

Houichi L, Dechemi N, Heddam S, Achour B (2013) An evaluation of ANN methods for estimating the lengths of hydraulic jumps in U-shaped channel. J Hydro Informatics 15(1):147–154

Titel: New Formulation for Predicting Daily Reference Evapotranspiration (ET0) in the Mediterranean Region of Algeria Country: Optimally Pruned Extreme Learning Machine (OPELM) Versus Online Sequential Extreme Learning Machine (OSELM)
verfasst von: Salim Heddam
Ozgur Kisi
Abderrazek Sebbar
Larbi Houichi
Lakhdar Djemili
Verlag: Springer International Publishing
Buch: Water Resources in Algeria - Part I
Print ISBN: 978-3-030-57894-7

Electronic ISBN: 978-3-030-57895-4

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/698_2020_528