Artificial neural network models for prediction of net radiation over a tropical region
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ORIGINAL ARTICLE
Artificial neural network models for prediction of net radiation over a tropical region Olusola Samuel Ojo1
•
Babatunde Adeyemi1 • Daniel Oluwagbenga Oluleye1
Received: 20 August 2020 / Accepted: 26 October 2020 Ó Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract In this study, net radiation and temperature data were collected from the Nigeria Meteorological Agency, Lagos, covering a period of 1983–2013. The study uses a multiple layer perceptron (MLP) model of artificial neural network (ANN) with three algorithms namely the Gradient Descent, Conjugate Gradient, and Broyden–Fletcher–Goldfarb–Shanno for the prediction of net radiation using the temperature series as input variables in place of commonly used empirical statistical methods. The investigation is conducted in a tropical region where 16 meteorological stations spatially distributed across the four climatic regions of Nigeria viz semi-arid (SAR), sub-humid dry (SHD), sub-humid humid (SHH), and humid (HUM) regions were used as a case study. Analyses showed that minimum temperature has a greater positive impact on net radiation than maximum temperature. This has been attributed among other factors to have contributed to higher magnitudes of net radiation in humid regions than those of arid regions. Meanwhile, the performance assessment of the ANN models using the refined index (dr) metric showed that among the three models proposed, the MLP-BFGS model performed best having maximum values of 0.91 in the SAR, 0.65 in the SHD, 0.80 in the SHH, and 0.88 in the HUM regions. It also has the lowest error analysis with magnitudes of root mean square error values of 7.92 W/m2 in the SAR, 9.95 W/m2 in the SHD, 12.91 W/m2 in the SHH, and 8.73 W/m2 in the HUM regions. Finally, it can be inferred from the results that MLP neural networks with the BFGS algorithm can predict net radiation accurately better than any empirical statistical methods over Nigeria. Keywords Net radiation Prediction Neural network algorithms Performance assessment Tropical region
1 Introduction Net radiation is the major source of energy that is available on the earth’s surface as a driving force for physical and biological processes useful for different applications, such as climate monitoring, weather forecast, and agricultural meteorology. It aids evaporation, soil heating, and other physical phenomena at the earth’s surface that drive the interaction between the earth’s surface and atmosphere [1, 2]. It plays an essential role in determining the thermal conditions of the Earth’s surface, and it is an important parameter for the study of land-surface processes and global climate change [3, 4]. It is important particularly in & Olusola Samuel Ojo [email protected] 1
crop farming as it is responsible for the germination of seeds, driving photosynthesis, determining the growing degree days of a crop, dictating soil moisture content, and soil temperatures [5]. In ecosystems, net radiation is divided into
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