Extreme Learning Machines: A new approach for prediction of reference evapotranspiration

Extreme Learning Machines: A new approach for prediction of reference evapotranspiration

•ELM model is proposed for prediction of ET0 in Iraq.•The ELM is more efficient than FFBP model.•The proposed ELM is efficient in predicting ET0 with complete or incomplete data.•The Rn is the most affecting weather parameter for Mosul station.•The model will solve the problem of shortage of weather...

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Bibliographic Details
Published in:Journal of hydrology (Amsterdam) Vol. 527; pp. 184 - 195
Main Authors: Abdullah, Shafika Sultan, Malek, M.A., Abdullah, Namiq Sultan, Kisi, Ozgur, Yap, Keem Siah
Format: Journal Article
Language:English
Published: Elsevier B.V 01-08-2015
Subjects:
Algorithms
Artificial Neural Networks
Errors
Evapotranspiration
Extreme Learning Machines
Mathematical models
Neural networks
Penman–Monteith equation
Reference evapotranspiration
Relative humidity
Running
Stations
ISW, Iraq
ISW, Iraq, Basrah
Reference evapotranspiration
Extreme Learning Machines
Penman–Monteith equation
Artificial Neural Networks
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Summary:•ELM model is proposed for prediction of ET0 in Iraq.•The ELM is more efficient than FFBP model.•The proposed ELM is efficient in predicting ET0 with complete or incomplete data.•The Rn is the most affecting weather parameter for Mosul station.•The model will solve the problem of shortage of weather data for prediction of ET0. Recognizing the importance of precise determination of reference evapotranspiration (ET0) is a principal step in the attempts to reserve huge quantities of squandered water. This paper investigates the efficiency of Extreme Learning Machines (ELM) algorithm at predicting Penman–Monteith (P–M) ET0 for Mosul, Baghdad, and Basrah meteorological stations, located at the north, mid, and southern part of Iraq. Data of weather parameters containing maximum air temperature (Tmax), minimum air temperature (Tmin), sunshine hours (Rn), relative humidity (Rh), and wind speed (U2) for the period (2000–2013) are used as inputs to the ELM model by using four different input cases including complete and incomplete sets of meteorological data. The performance of ELM model is compared with the empirical P–M equation and with feedforward backpropagation (FFBP) model. The evaluation criteria used for comparison are the root of mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The statistical results of both models are found to be encouraging; particularly results of running the ELM model with incomplete sets of data, noticing that the sensitivity of the proposed model to missing data changes from one location to another, as well as along the year for certain study location. The Rn is found to be the most effective parameter in Mosul Station, while U2 and Rh are found to act almost in parallel with Rn in Baghdad Station, and for conditions of Basrah Station; U2 and Rh prove to be the dominant parameters. The minimum and maximum time intervals required for running ELM model for all stations, and in all applied conditions, are (4.64–6.19) seconds respectively, while the same order of timing required for running the FFBP model is (6.30–27.80) seconds. The maximum R2 recorded for the ELM model is 0.991, while for the FFBP it is 0.985. The ELM proved to be efficient, simple in application, of high speed, and has very good generalization performance; therefore, this algorithm is highly recommended for locations similar to the geographical and meteorological conditions of Iraq that consists of both arid and semiarid regions.
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ISSN:0022-1694
1879-2707
DOI:10.1016/j.jhydrol.2015.04.073