Predicting Future Climate Using Algae Sedimentation

Jasdeep Natt, R. Hashemi, A. Bahrami, M. Bahar, N. Tyler, J. Hodgson
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引用次数: 4

Abstract

Biologists have shown that algae are the first to be implicated in climate changes and vice versa. The goal of this research effort is to predict the future climate using algae species living in a lake in the past. On performing age depth profile analysis on the sediment core obtained from the bottom of Jewel Lake in Alaska, a dataset of 163 records is derived. The 163 records collectively represent 4,308 years interval. Each record is composed of 86 attributes: Year, 84 species of algae, and Climate. The relevancy analysis of the attributes of the dataset identified only four species relevant to climate. An extrapolation system's Architecture with two stages of prediction process fit to meet the goal of this research effort. Three different predictive models of regression analysis, neural network, and ID3 were investigated for possible use in development of the extrapolation system resulting in four possible ways among which the best one uses neural networks in both stages. The best extrapolation system was able to predict climate for a year in future, within the next 278 years, with the average accuracy of 80%.
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利用藻类沉积预测未来气候
生物学家已经证明,藻类是第一个与气候变化有关的物种,反之亦然。这项研究的目的是利用过去生活在湖泊中的藻类物种来预测未来的气候。对阿拉斯加宝石湖湖底沉积物岩心进行了年龄深度剖面分析,得到了163条记录的数据集。163条记录的时间间隔为4308年。每条记录由86个属性组成:年份、84种藻类和气候。数据集属性的相关性分析只确定了四种与气候相关的物种。具有两阶段预测过程的外推系统体系结构适合于本研究的目标。研究了回归分析、神经网络和ID3三种不同的预测模型在外推系统开发中的可能应用,得出了四种可能的方法,其中最好的方法是在两个阶段都使用神经网络。最好的外推系统能够预测未来一年的气候,即未来278年的气候,平均准确率为80%。
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