Estimation of monthly reference evapotranspiration using novel hybrid machine learning approaches

ABSTRACT In this research, five hybrid novel machine learning approaches, artificial neural network (ANN)-embedded grey wolf optimizer (ANN-GWO), multi-verse optimizer (ANN-MVO), particle swarm optimizer (ANN-PSO), whale optimization algorithm (ANN-WOA) and ant lion optimizer (ANN-ALO), were applied for modelling monthly reference evapotranspiration (ETo) at Ranichauri (India) and Dar El Beida (Algeria) stations. The estimates yielded by hybrid machine learning models were compared against three models, Valiantzas-1, 2 and 3 based on root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), Pearson correlation coefficient (PCC) and Willmott index (WI). The results of comparison show that the ANN-GWO-1 model with five input variables (Tmin, Tmax, RH, Us, Rs) provides better estimates at both study stations (RMSE = 0.0592/0.0808, NSE = 0.9972/0.9956, PCC = 0.9986/0.9978, and WI = 0.9993/0.9989). Also, the adopted modelling strategy can build a truthful expert intelligent system for estimating the monthly ETo at study stations.

[1]  Paresh Chandra Deka,et al.  Performance evaluation of hybrid Wavelet-ANN and Wavelet-ANFIS models for estimating evapotranspiration in arid regions of India , 2015, Neural Computing and Applications.

[2]  John D. Valiantzas,et al.  Simplified Reference Evapotranspiration Formula Using an Empirical Impact Factor for Penman’s Aerodynamic Term , 2013 .

[3]  Lucas Borges Ferreira,et al.  Estimation of reference evapotranspiration in Brazil with limited meteorological data using ANN and SVM – A new approach , 2019, Journal of Hydrology.

[4]  Shahaboddin Shamshirband,et al.  Coupling a firefly algorithm with support vector regression to predict evaporation in northern Iran , 2018 .

[5]  Francesco Granata,et al.  Evapotranspiration evaluation models based on machine learning algorithms—A comparative study , 2019, Agricultural Water Management.

[6]  John D. Valiantzas,et al.  Simple ET 0 Forms of Penman's Equation without Wind and/or Humidity Data. I: Theoretical Development , 2013 .

[7]  Yashar Falamarzi,et al.  Estimating evapotranspiration from temperature and wind speed data using artificial and wavelet neural networks (WNNs) , 2014 .

[8]  Ozgur Kisi,et al.  Comparison of Two Different Adaptive Neuro-Fuzzy Inference Systems in Modelling Daily Reference Evapotranspiration , 2014, Water Resources Management.

[9]  Ozgur Kisi,et al.  Modelling reference evapotranspiration using a new wavelet conjunction heuristic method: Wavelet extreme learning machine vs wavelet neural networks , 2018, Agricultural and Forest Meteorology.

[10]  Masoud Karbasi.  Forecasting of Multi-Step Ahead Reference Evapotranspiration Using Wavelet- Gaussian Process Regression Model , 2017, Water Resources Management.

[11]  Anil Kumar,et al.  Daily suspended sediment concentration simulation using hydrological data of Pranhita River Basin, India , 2017, Comput. Electron. Agric..

[12]  Seyed Mohammad Mirjalili,et al.  Multi-Verse Optimizer: a nature-inspired algorithm for global optimization , 2015, Neural Computing and Applications.

[13]  Daran R. Rudnick,et al.  Evaluation of Valiantzas' simplified forms of the FAO-56 Penman-Monteith reference evapotranspiration model in a humid climate. , 2017 .

[14]  R. López-Urrea,et al.  Testing evapotranspiration equations using lysimeter observations in a semiarid climate , 2006 .

[15]  Ozgur Kisi,et al.  Polynomial chaos expansion and response surface method for nonlinear modelling of reference evapotranspiration , 2019, Hydrological Sciences Journal.

[16]  Andrew Lewis,et al.  Grey Wolf Optimizer , 2014, Adv. Eng. Softw..

[17]  Ozgur Kisi,et al.  Evapotranspiration Estimation using Six Different Multi-layer PerceptronAlgorithms , 2016 .

[18]  Ozgur Kisi,et al.  Evaluation of several soft computing methods in monthly evapotranspiration modelling , 2018 .

[19]  Özgür Kisi,et al.  Long-term monthly evapotranspiration modeling by several data-driven methods without climatic data , 2015, Comput. Electron. Agric..

[20]  M. Katz.  Validation of models , 2006 .

[21]  W. Zeng,et al.  Evaluation of CatBoost method for prediction of reference evapotranspiration in humid regions , 2019, Journal of Hydrology.

[22]  Ozgur Kisi,et al.  Comparison of SVM, ANFIS and GEP in modeling monthly potential evapotranspiration in an arid region (Case study: Sistan and Baluchestan Province, Iran) , 2019 .

[23]  A. A. Alazba,et al.  Artificial neural networks versus gene expression programming for estimating reference evapotranspiration in arid climate , 2016 .

[24]  Seyed Mohammad Mirjalili.  How effective is the Grey Wolf optimizer in training multi-layer perceptrons , 2014, Applied Intelligence.

[25]  C. L. Wu,et al.  Rainfall–runoff modeling using artificial neural network coupled with singular spectrum analysis , 2011 .

[26]  L. S. Pereira,et al.  Crop evapotranspiration : guidelines for computing crop water requirements , 1998 .

[27]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[28]  James L. McClelland,et al.  Explorations in parallel distributed processing: a handbook of models, programs, and exercises , 1988 .

[29]  James L. McClelland,et al.  Explorations in Parallel Distributed Processing - Macintosh version: A Handbook of Models, Programs, and Exercises , 1989 .

[30]  Hossein Tabari,et al.  Multilayer perceptron for reference evapotranspiration estimation in a semiarid region , 2012, Neural Computing and Applications.

[31]  Seyed Mohammad Mirjalili,et al.  The Ant Lion Optimizer , 2015, Adv. Eng. Softw..

[32]  Ozgur Kisi,et al.  Global cross-station assessment of neuro-fuzzy models for estimating daily reference evapotranspiration , 2013 .

[33]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[34]  Hossam Faris,et al.  Training feedforward neural networks using multi-verse optimizer for binary classification problems , 2016, Applied Intelligence.

[35]  Mohamed Menaa,et al.  Optimal Tuning for Load Frequency Control using Ant Lion Algorithm in Multi-Area Interconnected Power System , 2018 .

[36]  Zaher Mundher Yaseen,et al.  Temperature-based modeling of reference evapotranspiration using several artificial intelligence models: application of different modeling scenarios , 2018, Theoretical and Applied Climatology.

[37]  Sushma Jain,et al.  Reference evapotranspiration estimation and modeling of the Punjab Northern India using deep learning , 2019, Comput. Electron. Agric..

[38]  Shuanghe Shen,et al.  Reference Evapotranspiration Variation Analysis and Its Approaches Evaluation of 13 Empirical Models in Sub-Humid and Humid Regions: A Case Study of the Huai River Basin, Eastern China , 2018 .

[39]  Mohammad Valipour,et al.  Investigation of Valiantzas’ evapotranspiration equation in Iran , 2015, Theoretical and Applied Climatology.

[40]  K. Deep,et al.  Accelerated Opposition-Based Antlion Optimizer with Application to Order Reduction of Linear Time-Invariant Systems , 2018, Arabian Journal for Science and Engineering.

[41]  P. T. Ghazvinei,et al.  Forecasting pan evaporation with an integrated artificial neural network quantum-behaved particle swarm optimization model: a case study in Talesh, Northern Iran , 2018 .

[42]  O. Kisi,et al.  Daily Pan Evaporation Estimation Using Heuristic Methods with Gamma Test , 2018, Journal of Irrigation and Drainage Engineering.

[43]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[44]  Jalal Shiri,et al.  Data splitting strategies for improving data driven models for reference evapotranspiration estimation among similar stations , 2019, Comput. Electron. Agric..

[45]  Terry J. Gillespie,et al.  Evaluation of FAO Penman-Monteith and alternative methods for estimating reference evapotranspiration with missing data in Southern Ontario, Canada , 2010 .

[46]  Jan Adamowski,et al.  Estimating Evapotranspiration Using an Extreme Learning Machine Model: Case Study in North Bihar, India , 2016 .

[47]  O. Kisi,et al.  Rainfall-runoff modeling in hilly watershed using heuristic approaches with gamma test , 2018, Arabian Journal of Geosciences.

[48]  Ali Rahimikhoob,et al.  Estimation of evapotranspiration based on only air temperature data using artificial neural networks for a subtropical climate in Iran , 2010 .

[49]  A. A. Alazba,et al.  GEP and MLR approaches for the prediction of reference evapotranspiration , 2018, Neural Computing and Applications.

[50]  Junzeng Xu,et al.  Proper methods and its calibration for estimating reference evapotranspiration using limited climatic data in Southwestern China , 2015 .

[51]  Chuntian Cheng,et al.  Three-person multi-objective conflict decision in reservoir flood control , 2002, Eur. J. Oper. Res..

[52]  Jalal Shiri,et al.  Modeling reference evapotranspiration in island environments: Assessing the practical implications , 2019, Journal of Hydrology.

[53]  Michael J. Watts,et al.  Evolving connectionist systems (ECoSs): a new approach for modeling daily reference evapotranspiration (ET0) , 2018, Environmental Monitoring and Assessment.

[54]  Ling Tong,et al.  Evaluating eddy covariance method by large-scale weighing lysimeter in a maize field of northwest China , 2010 .

[55]  S. Shukla,et al.  Support vector machine based modeling of evapotranspiration using hydro-climatic variables in a sub-tropical environment , 2015 .

[56]  Mohamed A. Mattar,et al.  Using gene expression programming in monthly reference evapotranspiration modeling: A case study in Egypt , 2018 .

[57]  John D. Valiantzas,et al.  Simplified versions for the Penman evaporation equation using routine weather data , 2006 .

[58]  Kwok-wing Chau,et al.  Use of Meta-Heuristic Techniques in Rainfall-Runoff Modelling , 2017 .

[59]  Zaher Mundher Yaseen,et al.  An enhanced extreme learning machine model for river flow forecasting: State-of-the-art, practical applications in water resource engineering area and future research direction , 2019, Journal of Hydrology.

[60]  K. Taylor.  Summarizing multiple aspects of model performance in a single diagram , 2001 .

[61]  Andrew Lewis,et al.  The Whale Optimization Algorithm , 2016, Adv. Eng. Softw..

[62]  Utku Kose,et al.  An Ant-Lion Optimizer-Trained Artificial Neural Network System for Chaotic Electroencephalogram (EEG) Prediction , 2018, Applied Sciences.

[63]  Bilal Cemek,et al.  Estimation of daily reference evapotranspiration by neuro computing techniques using limited data in a semi-arid environment , 2018 .