Prof. Xiaohua Xu Profile

Prof. Xiaohua Xu

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Proceedings Article | 9 October 2023 Paper

Analysis and prediction of urban drought characteristics: Nanchang City as an example

Weifeng Yang, Xiaohua Xu, Delong Li, Yewei Liu, Xindong Xie

Proceedings Volume 12791, 127911Z (2023) https://doi.org/10.1117/12.3004692

KEYWORDS: Education and training, Data modeling, Neural networks, Analytical research, Artificial neural networks, Neurons, Temperature metrology, Performance modeling, Excel, Positron emission tomography

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This study was undertaken in Nanchang City with the aim of enhancing resilience against droughts, which have become a growing concern and have inflicted significant harm to both the economy and the quality of life of the city's inhabitants. The research efforts utilized the monthly data of precipitation and average temperature in Nanchang from January 1960 to December 2018 to accomplish the following objectives: assess the trend and abrupt variations of precipitation and temperature via a combination of linear regression, M-K test, and sliding t-test; analyze the drought intensity and frequency through two drought indices, using different time scales; and employ NAR Artificial Neural Network to predict the SPI-1 and SPEI-1 indices, and evaluate their accuracy. The findings indicated that there was a significant increase in annual precipitation and average temperature in Nanchang over a period of 59 years. At the annual scale, droughts were more frequent and light to medium droughts were the most prevalent, whereas heavy and exceptional droughts were less frequent. At the seasonal scale, summer and autumn were more susceptible to droughts than spring and winter. On the whole, the SPEI index was found to be more effective in identifying droughts in Nanchang, being more sensitive to the variations of drought and capable of offering varying assessments of drought intensity at different time scales. The overall performance of the prediction model was deemed satisfactory, with the prediction accuracy of SPI-1 and SPEI-1 indices being 69.4% and 81.5% respectively in the short-term prediction period. This resulted in the conclusion that the model had a superior prediction effect on the SPEI-1 index. The findings of the research could serve as a reference for future predictions of drought trends in Nanchang in the short term.

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