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Advances in Environmental Research Volume 6, Number 4, December 2017 , pages 265-279 DOI: https://doi.org/10.12989/aer.2017.6.4.265 |
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Modelling land surface temperature using gamma test coupled wavelet neural network |
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Thendiyath Roshni, Nandini Kumari, Remesan Renji and Jayakumar Drisya
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| Abstract | ||
| The climate change has made adverse effects on land surface temperature for many regions of the world. Several climatic studies focused on different downscaling techniques for climatological parameters of different regions. For statistical downscaling of any hydrological parameters, conventional Neural Network Models were used in common. However, it seems that in any modeling study, uncertainty is a vital aspect when making any predictions about the performance. In this paper, Gamma Test is performed to determine the data length selection for training to minimize the uncertainty in model development. Another measure to improve the data quality and model development are wavelet transforms. Hence, Gamma Test with Wavelet decomposed Feedforward Neural Network (GT-WNN) model is developed and tested for downscaled land surface temperature of Patna Urban, Bihar. The results of GT-WNN model are compared with GT-FFNN and conventional Feedforward Neural Network (FFNN) model. The effectiveness of the developed models is illustrated by Root Mean Square Error and Coefficient of Correlation. Results showed that GT-WNN outperformed the GT-FFNN and conventional FFNN in downscaling the land surface temperature. The land surface temperature is forecasted for a period of 2015-2044 with GT-WNN model for Patna Urban in Bihar. In addition, the significance of the probable changes in the land surface temperature is also found through Mann-Kendall (M-K) Test for Summer, Winter, Monsoon and Post Monsoon seasons. Results showed an increasing surface temperature trend for summer and winter seasons and no significant trend for monsoon and post monsoon season over the study area for the period between 2015 and 2044. Overall, the M-K test analysis for the annual data shows an increasing trend in the land surface temperature of Patna Urban. | ||
| Key Words | ||
| gamma test; downscaling; wavelet decomposition; neural network; temperature | ||
| Address | ||
| Thendiyath Roshni and Nandini Kumari: Department of Civil Engineering, National Institute of Technology Patna, Patna, India Remesan Renji: School of Water Resources,Indian Institute of Technology Kharagpur, Kharagpur, India Jayakumar Drisya: Department of Civil Engineering, National Institute of Technology Calicut, Calicut, India | ||
