Evolving connectionist systems (ECoSs): a new approach for modeling daily reference evapotranspiration (ET0)

Heddam, Salim; Watts, Michael J.; Houichi, Larbi; Djemili, Lakhdar; Sebbar, Abderrazek

doi:10.1007/s10661-018-6903-0

Published: 14 August 2018

Evolving connectionist systems (ECoSs): a new approach for modeling daily reference evapotranspiration (ET₀)

Salim Heddam ORCID: orcid.org/0000-0002-8055-8463¹,
Michael J. Watts²,
Larbi Houichi³,
Lakhdar Djemili⁴ &
Abderrazek Sebbar⁴

Environmental Monitoring and Assessment volume 190, Article number: 516 (2018) Cite this article

156 Accesses
8 Citations
1 Altmetric
Metrics details

Abstract

Over the last few years, the uses of artificial intelligence techniques (AI) for modeling daily reference evapotranspiration (ET₀) have become more popular and a considerable amount of models were successfully applied to the problem. Therefore, in the present paper, we propose a new evolving connectionist (ECoS) approaches for modeling daily reference evapotranspiration (ET₀) in the Mediterranean region of Algeria. Three ECoS models, namely, (i) the off-line dynamic evolving neural-fuzzy inference system called DEFNIS_OF, (ii) the on-line dynamic evolving neural-fuzzy inference system called DEFNIS_ON, and (iii) the evolving fuzzy neural network called (EFuNN), were statistically compared using the root mean square error (RMSE), the mean absolute error (MAE), the coefficient of correlation (R), and the Nash-Sutcliffe efficiency (NSE) indexes. The proposed approaches were applied for modeling daily ET₀ using climatic variables from two weather stations: Algiers and Skikda, Algeria. Five well-known climatic variables were selected as inputs: daily maximum and minimum air temperatures (T_max and T_min), daily wind speed (W_S), daily relative humidity (R_H), and daily sunshine hours (SH). The effect of combining several climatic variables as inputs was evaluated, and at least six scenarios were developed and compared. The proposed ECoS models were compared against the reference Penman-Monteith model referred as “FAO-56 PM”. According to the results obtained, the DEFNIS_OF1 model having T_max, T_min, W_S, RH, and SH as inputs, is the best model, followed by the DEFNIS_ON1, and the EFuNN1 is the worst model. The R and NSE value calculated for the testing dataset for the Algiers and Skikda stations were (0.954, 0.910) and (0.954, 0.905), respectively. While both DEFNIS_OF1 and DEFNIS_ON1 showed good accuracy and high performances, the EFuNN1 was less accurate.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

43,34 €

Price includes VAT (Finland)
Tax calculation will be finalised during checkout.

References

Abdullah, S. S., Malek, M. A., Abdullah, N. S., Kisi, O., & Yap, K. S. (2015). Extreme learning machines: a new approach for prediction of reference evapotranspiration. Journal of Hydrology, 527, 184–195. https://doi.org/10.1016/j.jhydrol.2015.04.073.
Article Google Scholar
Abraham, A., Steinberg, D., & Philip, N. S. (2001). Rainfall forecasting using soft computing models and multivariate adaptive regression splines. IEEE SMC Transactions, Special Issue on Fusion of Soft Computing and Hard Computing in Industrial Applications, 1(xx), 1–6.
Google Scholar
Adamala, S., Raghuwanshi, N. S., Mishra, A., & Tiwari, M. (2014a). Evapotranspiration modeling using second-order neural networks. ASCE Journal of Hydrologic Engineering, 19(6), 1131–1140. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000887.
Article Google Scholar
Adamala, S., Raghuwanshi, N. S., Mishra, A., & Tiwari, M. (2014b). Development of generalized higher-order synaptic neural-based ET₀ models for different agroecological regions in India. ASCE Journal of Irrigation and Drainage Engineering, 140(12). https://doi.org/10.1061/(ASCE)IR.1943-4774.0000784.
Article Google Scholar
Adamala, S., Raghuwanshi, N. S., & Mishra, A. (2015). Generalized quadratic synaptic neural networks for ET₀ modeling. Environmental Processes, 2, 309–329. https://doi.org/10.1007/s40710-015-0066-6.
Article Google Scholar
Alavi, S. A., & Rahimikhoob, A. (2016). A simple model for determining reference evapotranspiration using NOAA satellite data: a case study. Environmental Processes, 3, 479–493. https://doi.org/10.1007/s40710-016-0141-7.
Article Google Scholar
Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop evapotranspiration: guidelines for computing crop water requirements-drainage paper 56 (Vol. 300, p. 6541). Rome: FAO.
Google Scholar
Ashrafi, M., Hock Chye Chua, L., Quek, C., & Qin, X. (2016). A fully-online neuro-fuzzy model for flow forecasting in basins with limited data. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2016.11.057.
Article Google Scholar
Aytek, A. (2009). Co-active neurofuzzy inference system for evapotranspiration modeling. Soft Computing, 13, 691–700. https://doi.org/10.1007/s00500-008-0342-8.
Article Google Scholar
Ballesteros, R., Ortega, J. F., & Moreno, M. A. (2016). FORET₀: new software for reference evapotranspiration forecasting. Journal of Arid Environments, 124, 128–141. https://doi.org/10.1016/j.jaridenv.2015.08.006.
Article Google Scholar
Cobaner, M. (2011). Evapotranspiration estimation by two different neurofuzzy inference systems. Journal of Hydrology, 398(2011), 292–302. https://doi.org/10.1016/j.jhydrol.2010.12.030.
Article Google Scholar
Debnath, S., Adamala, S., & Raghuwanshi, N. S. (2015). Sensitivity analysis of FAO-56 Penman-Monteith method for different agro-ecological regions of India. Environmental Processes, 2, 689–704. https://doi.org/10.1007/s40710-015-0107-1.
Article Google Scholar
Dovžan, D., & Škrjanc, I. (2011). Recursive fuzzy c-means clustering for recursive fuzzy identification of time-varying processes. ISA Transactions, 50, 159–169. https://doi.org/10.1016/j.isatra.2011.01.004.
Article Google Scholar
Falamarzi, Y., Palizdan, N., Huang, Y. F., & Lee, T. S. (2014). Estimating evapotranspiration from temperature and wind speed data using artificial and wavelet neural networks (WNNs). Agricultural Water Management, 140, 26–36. https://doi.org/10.1016/j.agwat.2014.03.014.
Article Google Scholar
Feng, Y., Gong, D., Mei, X., & Cui, N. (2016a). Estimation of maize evapotranspiration using extreme learning machine and generalized regression neural network on the China loess plateau. Hydrology Research. https://doi.org/10.2166/nh.2016.099.
Article Google Scholar
Feng, Y., Cui, N., Zhao, L., Hu, X. T., & Gong, D. (2016b). Comparison of ELM, GANN, WNN and empirical models for estimating reference evapotranspiration in humid region of Southwest China. Journal of Hydrology, 536, 376–383. https://doi.org/10.1016/j.jhydrol.2016.02.053.
Article Google Scholar
Feng, Y., Cui, N., Gong, D., Zhang, Q., & Zhao, L. (2017a). Evaluation of random forests and generalized regression neural networks for daily reference evapotranspiration modelling. Agricultural Water Management, 193, 163–173. https://doi.org/10.1016/j.agwat.2017.08.003.
Article Google Scholar
Feng, Y., Peng, Y., Cui, N., Gong, D., & Zhang, K. (2017b). Modeling reference evapotranspiration using extreme learning machine and generalized regression neural network only with temperature data. Computers and Electronics in Agriculture, 136, 71–78. https://doi.org/10.1016/j.compag.2017.01.027.
Article Google Scholar
Gocić, M., Motamedi, S., Shamshirband, S., Petković, D., Sudheer, C., Hashim, R., & Arif, M. (2015). Soft computing approaches for forecasting reference evapotranspiration. Computers and Electronics in Agriculture, 113, 164–173. https://doi.org/10.1016/j.compag.2015.02.010.
Article Google Scholar
Gocić, M., Petković, D., Shamshirband, S., & Kamsin, A. (2016). Comparative analysis of reference evapotranspiration equations modelling by extreme learning machine. Computers and Electronics in Agriculture, 127, 56–63. https://doi.org/10.1016/j.compag.2016.05.017.
Article Google Scholar
Guo, D., Westra, S., & Maier, H. R. (2016). An R package for modelling actual, potential and reference evapotranspiration. Environmental Modelling & Software, 78, 216–224. https://doi.org/10.1016/j.envsoft.2015.12.019.
Article Google Scholar
Guven, A., Aytek, A., Yuce, M. I., & Aksoy, H. (2008). Genetic programming based empirical model for daily reference evapotranspiration estimation. Clean: Soil, Air, Water, 36(10–11), 905–912. https://doi.org/10.1002/clen.200800009.
CAS Article Google Scholar
Heddam, S. (2014). Modelling hourly dissolved oxygen concentration (DO) using dynamic evolving neural-fuzzy inference system (DENFIS) based approach: case study of Klamath River at Miller Island boat ramp, Oregon, USA. Environmental Science and Pollution Research, 21, 9212–9227. https://doi.org/10.1007/s11356-014-2842-7.
CAS Article Google Scholar
Heddam, S. (2016). Fuzzy neural network (EFuNN) for modelling dissolved oxygen concentration (DO). In C. Kahraman & I. U. Sari (Eds.), Intelligence Systems in Environmental Management: Theory and Applications, Intelligent Systems Reference Library 113. https://doi.org/10.1007/978-3-319-42993-9_11.
Chapter Google Scholar
Heddam, S., & Dechemi, N. (2015). A new approach based on the dynamic evolving neural-fuzzy inference system (DENFIS) for modelling coagulant dosage: case study of water treatment plant of Algeria country. Desalination and Water Treatment, Taylor & Francis, 53-4, 1045–1053. https://doi.org/10.1080/19443994.2013.878669.
CAS Article Google Scholar
Heydari, G., Vali, M. A., & Gharaveisi, A. A. (2016). Chaotic time series prediction via artificial neural square fuzzy inference system. Expert Systems with Applications, 55, 461–468. https://doi.org/10.1016/j.eswa.2016.02.031.
Article Google Scholar
Huang, H., Pasquier, M., & Quek, C. (2009). Financial market trading system with a hierarchical coevolutionary fuzzy predictive model. IEEE Transactions on Evolutionary Computation, 13(1), 56–70. https://doi.org/10.1109/TEVC.2008.911682.
Article Google Scholar
Hwang, Y.C., Song, Q. (2009). Dynamic neural fuzzy inference system. Proceedings of the international conference on Advances in neuroinformation processing ICONIP. Lecture Notes in Computer Science, 5506/2009, 1245–1250. Berlin: Springer. https://doi.org/10.1007/978-3-642-02490-0-151.
Karimi, S., Kisi, O., Kim, S., Nazemi, A. H., & Shiri, J. (2016). Modelling daily reference evapotranspiration in humid locations of South Korea using local and cross-station data management scenarios. International Journal of Climatology. https://doi.org/10.1002/joc.4911.
Article Google Scholar
Kasabov, N. (2001). Evolving fuzzy neural networks for online supervised/unsupervised, knowledge-based learning. IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, 31(6), 902–918. https://doi.org/10.1109/3477.969494.
CAS Article Google Scholar
Kasabov, N. (2007). Evolving connectionist systems: the knowledge engineering approach (p. 465. ISBN 978-1-84628-345-1). New York: Springer. https://doi.org/10.1007/978-1-84628-347-5.
Book Google Scholar
Kasabov, N. (2015). Evolving connectionist systems for adaptive learning and knowledge discovery: trends and directions. Knowledge-Based Systems, 80, 24–33. https://doi.org/10.1016/j.knosys.2014.12.032.
Article Google Scholar
Kasabov, N., & Song, Q. (2002). DENFIS: dynamic, evolving neural-fuzzy inference systems and its application for time-series prediction. IEEE Transactions on Fuzzy Systems, 10, 144–154. https://doi.org/10.1109/91.995117.
Article Google Scholar
Kasabov, N., Song, Q., & Tian, M. M. (2008). Fuzzy-neuro systems for local and personalized modelling. Forging new frontiers: fuzzy pioneers II. Studies in Fuzziness and Soft Computing, 218, 175–197. https://doi.org/10.1007/978-3-540-73185-6-8.
Article Google Scholar
Kisi, O. (2011). Modeling reference evapotranspiration using evolutionary neural networks. ASCE Journal of Irrigation and Drainage Engineering, 137(10), 636–643. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000333.
Article Google Scholar
Kisi, O. (2013). Applicability of Mamdani and Sugeno fuzzy genetic approaches for modeling reference evapotranspiration. Journal of Hydrology, 504, 160–170. https://doi.org/10.1016/j.jhydrol.2013.09.043.
Article Google Scholar
Kisi, O. (2016). Modeling reference evapotranspiration using three different heuristic regression approaches. Agricultural Water Management, 169, 162–172. https://doi.org/10.1016/j.agwat.2016.02.026.
Article Google Scholar
Kisi, O., & Guven, A. (2010). Evapotranspiration modeling using linear genetic programming technique. ASCE Journal of Irrigation and Drainage Engineering, 136(10), 715–723. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000244.
Article Google Scholar
Kisi, O., Sanikhani, H., Zounemat-Kermani, M., & Niazi, F. (2015). Long-term monthly evapotranspiration modeling by several data-driven methods without climatic data. Computers and Electronics in Agriculture, 115, 66–77. https://doi.org/10.1016/j.compag.2015.04.015.
Article Google Scholar
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. Meteorology and Atmospheric Physics, 118(3–4), 163–178. https://doi.org/10.1007/s00703-012-0205-9.
Article Google Scholar
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. Arabian Journal for Science and Engineering, 39, 5959–5969. https://doi.org/10.1007/s13369-014-1151-2.
Article Google Scholar
Laha, D., Ren, Y., & Suganthan, P. N. (2015). Modeling of steelmaking process with effective machine learning techniques. Expert Systems with Applications, 42, 4687–4696. https://doi.org/10.1016/j.eswa.2015.01.030.
Article Google Scholar
Landeras, G., Ortiz-Barredo, A., & López, J. J. (2009). Forecasting weekly evapotranspiration with ARIMA and artificial neural network models. ASCE Journal of Irrigation and Drainage Engineering, 135(3), 323–334. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000008.
Article Google Scholar
Lemos A, Caminhas W, Gomide F (2013) Evolving intelligent systems: methods, algorithms and applications. Chapter in Emerging Paradigms in Machine Learning, volume 13 of the series Smart Innovation, Systems and Technologies pp 117-159. https://doi.org/10.1007/978-3-642-28699-5_6.
Chapter Google Scholar
Liao, Y., Rao Vemuri, V., & Pasos, A. (2007). Adaptive anomaly detection with evolving connectionist systems. Journal of Network and Computer Applications, 30, 60–80. https://doi.org/10.1016/j.jnca.2005.08.005.
Article Google Scholar
Malcangi, M. (2015). Applying evolutionary methods for early prediction of sleep onset. Neural Computing and Applications. https://doi.org/10.1007/s00521-015-1928-6.
Article Google Scholar
Ng, G. S., Liu, F., Loh, T. F., & Quek, C. (2012). A novel brain-inspired neuro-fuzzy hybrid system for artificial ventilation modeling. Expert Systems with Applications, 39, 11808–11817. https://doi.org/10.1016/j.eswa.2012.01.028.
Article Google Scholar
Olden, J. D., & Jackson, D. A. (2002). Illuminating the “black box”: understanding variable contributions in artificial neural networks. Ecological Modelling, 154, 135–150. https://doi.org/10.1016/S0304-3800(02)00064-9.
Article Google Scholar
Opresnik, D., Fiasché, M., Taisch, M., & Hirsch, M. (2015). An evolving fuzzy inference system for extraction of rule set for planning a product-service strategy. Information Technology and Management. https://doi.org/10.1007/s10799-015-0242-4.
Article Google Scholar
Ozkan, C., Kisi, O., & Akay, B. (2011). Neural networks with artificial bee colony algorithm for modeling daily reference evapotranspiration. Irrigation Science, 29, 431–441. https://doi.org/10.1007/s00271-010-0254-0.
Article Google Scholar
Partal, T. (2016). Comparison of wavelet based hybrid models for daily evapotranspiration estimation using meteorological data. KSCE Journal of Civil Engineering, 20(5), 2050–2058. https://doi.org/10.1007/s12205-015-0556-0.
Article Google Scholar
Raj Kiran, N., & Ravi, V. (2007). Software reliability prediction by soft computing techniques. Journal of Systems and Software, 81(4), 576–583. https://doi.org/10.1016/j.jss.2007.05.005.
Article Google Scholar
Ramírez-Cuesta, J. M., Cruz-Blanco, M., Santos, C., & Lorite, I. J. (2017). Assessing reference evapotranspiration at regional scale based on remote sensing, weather forecast and GIS. International Journal of Applied Earth Observation and Geoinformation, 55, 32–42. https://doi.org/10.1016/j.jag.2016.10.004.
Article Google Scholar
Talei, A., Chua, L. H., Quek, C., & Jansson, P. (2013). Runoff forecasting using a Takagi-Sugeno neuro-fuzzy model with online learning. Journal of Hydrology, 488, 17–32. https://doi.org/10.1016/j.jhydrol.2013.02.022.
Article Google Scholar
Traore, S., & Guven, A. (2012). Regional-specific numerical models of evapotranspiration using gene-expression programming interface in Sahel. Water Resources Management, 26, 4367–4380. https://doi.org/10.1007/s11269-012-0149-3.
Article Google Scholar
Vinay Kumar, K., Ravi, V., Carr, M., & Raj Kiran, N. (2008). Software development cost estimation using wavelet neural networks. Journal of Systems and Software, 81, 1853–1867. https://doi.org/10.1016/j.jss.2007.12.793.
Article Google Scholar
Wahab, A., Quek, C., Tan, C. K., & Takeda, K. (2009). Driving profile modeling and recognition based on soft computing approach. IEEE Transactions on Neural Networks, 20(4), 563–582. https://doi.org/10.1109/TNN.2008.2007906.
Article Google Scholar
Watts, M. (2009). A decade of Kasabov’s evolving connectionist systems: a review. IEEE Transactions on Systems, Man, and Cybernetics Part C: Applications and Reviews, 39(3), 253–269. https://doi.org/10.1109/TSMCC.2008.2012254.
Article Google Scholar
Xing, X., Liu, Y., Zhao, W., Kang, D., Yu, M., & Ma, X. (2016). Determination of dominant weather parameters on reference evapotranspiration by path analysis theory. Computers and Electronics in Agriculture, 120, 10–16. https://doi.org/10.1016/j.compag.2015.11.001.
Article Google Scholar
Yurdakul, M., Gopalakrishnan, K., & Akdas, H. (2014). Prediction of specific cutting energy in natural stone cutting processes using the neuro-fuzzy methodology. International Journal of Rock Mechanics and Mining Sciences, 67, 127–135. https://doi.org/10.1016/j.ijrmms.2014.01.015.
Article Google Scholar

Download references

Author information

Affiliations

Faculty of Science, Agronomy Department, Hydraulics Division University 20 Août 1955, Route EL HADAIK BP 26, SKIKDA, Algeria
Salim Heddam
Information Technology Programme, Auckland Institute of Studies, PO Box 2995, Auckland, New Zealand
Michael J. Watts
Department of Hydraulic, University of Batna 2, Algeria BP 45/B Tamachit, Batna, Algeria
Larbi Houichi
Soil and Hydraulics Laboratory, Faculty of Engineering Sciences, Hydraulics Department, University BADJI-MOKHTAR ANNABA, Annaba, Algeria
Lakhdar Djemili & Abderrazek Sebbar

Authors

Salim Heddam
View author publications
You can also search for this author in PubMed Google Scholar
Michael J. Watts
View author publications
You can also search for this author in PubMed Google Scholar
Larbi Houichi
View author publications
You can also search for this author in PubMed Google Scholar
Lakhdar Djemili
View author publications
You can also search for this author in PubMed Google Scholar
Abderrazek Sebbar
View author publications
You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Salim Heddam.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Heddam, S., Watts, M.J., Houichi, L. et al. Evolving connectionist systems (ECoSs): a new approach for modeling daily reference evapotranspiration (ET₀). Environ Monit Assess 190, 516 (2018). https://doi.org/10.1007/s10661-018-6903-0

Download citation

Received: 19 November 2017
Accepted: 07 August 2018
Published: 14 August 2018
DOI: https://doi.org/10.1007/s10661-018-6903-0

Keywords

Modeling
ET₀
Climatic variables
DENFIS
EFuNN
ECoS