Trend analysis of temperature data using innovative polygon trend analysis and modeling by gene expression programming

Abstract

Presenting temperature data using recently introduced innovative polygon trend analysis (IPTA) can improve our understanding of the effects of climate change. This method was applied to analyze temperature trends at six stations in Turkey: Istanbul (17,064), Ankara (17,131), Bursa (17,116), Iznik (17,661), Gemilik (17,663), and Sakarya (17,069). At station 17,064, there was an increasing trend in temperature data for seven months, while only one month showed a decreasing trend, and the remainder presented no trend. For station 17,131, there was a decreasing trend for two months, an increasing trend for five months, and no trend for the remaining months. At station 17,116, an increasing trend was present for nine months, with a decreasing trend for two months and only one month indicating no trend. An increasing trend over seven months was noted at station 17,661, while two and three months showed a decreasing and no trend, respectively. For station 17,663, there was an increasing trend for nine months, one month showed no trend, and two months presented a decreasing trend. At station 17,069, five, four, and three months showed increasing, decreasing, and no trends, respectively. The gene expression programming (GEP) model was tested to predict the short-term monthly average temperature for this dataset. The proposed GEP model presented good prediction results for all selected stations by tracing the relationship with a coefficient of determination (R-Sq) >= 0.90. Trend analysis by IPTA can help understand temperature trends better, aiding future decision-making, and the GEP model can effectively predict short-term values.