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Smart Structures and Systems Volume 32, Number 1, July 2023 , pages 37-47 DOI: https://doi.org/10.12989/sss.2023.32.1.037 |
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Teaching-learning-based strategy to retrofit neural computing toward pan evaporation analysis |
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Rana Muhammad Adnan Ikram, Imran Khan, Hossein Moayedi, Loke Kok Foong and Binh Nguyen Le
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| Abstract | ||
| Indirect determination of pan evaporation (PE) has been highly regarded, due to the advantages of intelligent models employed for this objective. This work pursues improving the reliability of a popular intelligent model, namely multi-layer perceptron (MLP) through surmounting its computational knots. Available climatic data of Fresno weather station (California, USA) is used for this study. In the first step, testing several most common trainers of the MLP revealed the superiority of the Levenberg-Marquardt (LM) algorithm. It, therefore, is considered as the classical training approach. Next, the optimum configurations of two metaheuristic algorithms, namely cuttlefish optimization algorithm (CFOA) and teaching-learning-based optimization (TLBO) are incorporated to optimally train the MLP. In these two models, the LM is replaced with metaheuristic strategies. Overall, the results demonstrated the high competency of the MLP (correlations above 0.997) in the presence of all three strategies. It was also observed that the TLBO enhances the learning and prediction accuracy of the classical MLP (by nearly 7.7% and 9.2%, respectively), while the CFOA performed weaker than LM. Moreover, a comparison between the efficiency of the used metaheuristic optimizers showed that the TLBO is a more time-effective technique for predicting the PE. Hence, it can serve as a promising approach for indirect PE analysis. | ||
| Key Words | ||
| environmental management; multi-layer perceptron; pan evaporation; teaching-learning-based optimization | ||
| Address | ||
| "(1) Rana Muhammad Adnan Ikram: School of Economics and Statistics, Guangzhou University, Guangzhou 510006, China; (2) Imran Khan: Department of Economics, The University of Haripur, Pakistan; (3) Hossein Moayedi, Loke Kok Foong, Binh Nguyen Le: Institute of Research and Development, Duy Tan University, Da Nang, Vietnam; (4) Hossein Moayedi, Loke Kok Foong, Binh Nguyen Le: School of Engineering & Technology, Duy Tan University, Da Nang, Vietnam." | ||