Prediction of hotspots pattern in Kalimantan using copula-based quantile regression and probabilistic model: a study of precipitation and dry spells across varied ENSO conditions

IF 2.4 Q2 GEOSCIENCES, MULTIDISCIPLINARY
Mohamad K. Najib, Sri Nurdiati, Ardhasena Sopaheluwakan
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Abstract

Hotspots in Kalimantan are significantly correlated with local and global climatic conditions. These hotspots have been represented in previous explorations using copula-based mean regression technique. However, this study focused on advancing hotspots model through the use of copula-based quantile regression. Probabilistic method was also introduced to depict the characteristics of hotspots in Kalimantan. To achieve this objective, the technique of the inference of functions for margins was applied. Several copula functions, including Gumbel, Clayton, Frank, Joe, Galambos, BB1, BB6, BB7, and BB8, were meticulously chosen. The selection of the most suitable copula was based on the results of the Anderson-Darling and Cramer-von Mises hypothesis tests. The results showed that the combination of quantile and mean regression yielded satisfactory results. Moreover, an uncertainty range was established by assessing the outermost quantile, which aided the assessment of the reliability of estimated hotspots. Probabilistic model introduced a fresh viewpoint to modeling process. Instead of forecasting an exact value, model estimated the probability of hotspots occurrences based on specific climatic conditions. Among the three scenarios examined, precipitation-based model showed an average accuracy of 89.7%, while dry spells-based outperformed the value with a score of 90.3%. After evaluating the results from both regression and probabilistic model, dry spells-based method outperformed precipitation-based. On the other hand, precipitation-based performed better in capturing certain minor details compared to dry spells-based model.
基于copula分位数回归和概率模型的加里曼丹地区热点模式预测:不同ENSO条件下降水和干旱期的研究
加里曼丹的热点与当地和全球气候条件显著相关。这些热点已经在以前的探索中使用基于copula的均值回归技术来表示。然而,本研究的重点是通过使用基于copula的分位数回归来推进热点模型。采用概率方法描述了加里曼丹地区热点的特征。为了实现这一目标,应用了边界函数推理技术。几个联结函数,包括Gumbel, Clayton, Frank, Joe, Galambos, BB1, BB6, BB7和BB8,都是经过精心挑选的。选择最合适的联结是基于安德森-达林和克莱默-冯·米塞斯假设检验的结果。结果表明,分位数与均值相结合的回归结果令人满意。此外,通过评估最外面的分位数建立了一个不确定范围,这有助于评估估计热点的可靠性。概率模型为建模过程引入了一种新的视角。该模型不是预测一个精确的值,而是根据特定的气候条件估计热点发生的概率。在研究的三种情景中,基于降水的模型的平均准确率为89.7%,而基于干旱期的模型的平均准确率为90.3%。在对回归模型和概率模型的结果进行评估后,基于干旱期的方法优于基于降水的方法。另一方面,与基于干旱的模型相比,基于降水的模型在捕捉某些次要细节方面表现得更好。
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来源期刊
VIETNAM JOURNAL OF EARTH SCIENCES
VIETNAM JOURNAL OF EARTH SCIENCES GEOSCIENCES, MULTIDISCIPLINARY-
CiteScore
3.60
自引率
20.00%
发文量
0
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