Forecasting sea surface temperature with feed-forward artificial networks in combating the global climate change: The sample of Rize, Türkiye

Tamer Akkan, T. Mutlu, E. Baş
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Abstract

The increase of the world population, especially in the global competition, together with the increasing use of fossil fuel resources to meet energy needs, leads to more greenhouse gases (more than one CO2, methane etc.) emissions and the global climate crisis. In this process, changes in meteorological events such as temperature, precipitation, and wind, attract attention moreover but when considered as a whole, we know that these negative changes in the ecosystem negatively affect many living groups. Sea Surface Temperature (SST) as measured meteorologically is the most important environmental parameter where these changes are monitored and observed. It draws attention to the fact that changes in SST are not limited to living organisms as habitats, but also catalyze many chain reactions, especially socio-economic impacts. Therefore, much of the work is devoted to forecasting studies to adapt to changing habitats and take the necessary precautions against potential risks. Feed-forward artificial neural networks have been commonly used to address these emerging needs. Artificial neural networks, which are a simple imitation of the human neurological system, have been used as an artificial intelligence method in forecasting problems due to their superior performance and not having the limitations of classical time series. In this study, the forecasting of the time series of monthly mean SST temperature obtained from Rize station between the years 2010 and 2020 is performed by using feed-forward artificial neural networks, and the forecasting performance of the corresponding time series is compared with many forecasting methods with different characteristics. The comparison of the methods used the mean square error and mean absolute percentage error criteria, which are commonly used in the forecasting literature. The analysis results showed that the analysis results obtained with the feed-forward artificial neural networks have the best prediction performance. As a result, it can be stated that the sea surface temperature can be forecasted with a very high accuracy using the feed-forward artificial neural networks.
用前馈人工网络预测海面温度在应对全球气候变化中的作用:以Rize, trkiye为例
世界人口的增加,特别是在全球竞争中,加上越来越多地使用化石燃料资源来满足能源需求,导致更多的温室气体(不止一种二氧化碳,甲烷等)排放和全球气候危机。在这一过程中,诸如温度、降水和风等气象事件的变化也引起了人们的注意,但当从整体上考虑时,我们知道这些生态系统的负面变化对许多生物群体产生了负面影响。气象测量的海表温度(SST)是监测和观察这些变化的最重要环境参数。这引起了人们的注意,海温的变化不仅局限于作为栖息地的生物,而且还催化了许多连锁反应,特别是社会经济影响。因此,大部分工作都致力于预测研究,以适应不断变化的栖息地,并采取必要的预防措施,防范潜在的风险。前馈人工神经网络已被广泛用于解决这些新兴需求。人工神经网络是对人类神经系统的简单模仿,由于其优越的性能和不受经典时间序列的限制而被用作预测问题的人工智能方法。本文利用前馈人工神经网络对日则站2010 - 2020年月平均海温时间序列进行了预测,并与多种不同特征的预测方法比较了相应时间序列的预测效果。方法的比较采用了预测文献中常用的均方误差和平均绝对百分比误差标准。分析结果表明,采用前馈人工神经网络得到的分析结果具有最佳的预测性能。结果表明,利用前馈人工神经网络对海表温度预报具有很高的精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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