Determining Climate Extremes of Mosul Weather Using Robust Noise Clustering Strategy.

N, which means that the number of variables is greater than the number of observations. The observations represented the school years; the variables were the summer days for three months (June, July, and August). This arrangement proved suitable for the variable aggregation technique of high-dimensional data. The results showed six groups, five of which were almost homogeneous. The five clusters indicate different patterns of maximum temperature increases during the summer. The first cluster highlights heat waves in mid-summer (July and August), while the second cluster focuses on the hot ends of summer (late June and August). The third cluster refers to early and continuous heat waves in June and July, while the fourth cluster reflects persistent heat in late July and August. The fifth cluster shows a variation in temperatures between the beginning and the end of summer. The excluded noise variables represent inconsistent data or outliers that did not belong to any cluster. This contributes to improving the accuracy of climate models. The results highlight characteristic climatic patterns and provide recommendations for strengthening environmental and agricultural planning.]]>

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Publication Details

Journal
IRAQI JOURNAL OF STATISTICAL SCIENCES
Published
2025-05-01
DOI
https://doi.org/10.33899/iqjoss.2025.187755
Primary Topic
Traffic Prediction and Management Techniques
Type
article
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article

Determining Climate Extremes of Mosul Weather Using Robust Noise Clustering Strategy.

Marwan AL-Hyali, Bashar AL-Talib
IRAQI JOURNAL OF STATISTICAL SCIENCES
Traffic Prediction and Management Techniques
article

Determining Climate Extremes of Mosul Weather Using Robust Noise Clustering Strategy.

Marwan AL-Hyali, Bashar AL-Talib
article en

Abstract

N, which means that the number of variables is greater than the number of observations. The observations represented the school years; the variables were the summer days for three months (June, July, and August). This arrangement proved suitable for the variable aggregation technique of high-dimensional data. The results showed six groups, five of which were almost homogeneous. The five clusters indicate different patterns of maximum temperature increases during the summer. The first cluster highlights heat waves in mid-summer (July and August), while the second cluster focuses on the hot ends of summer (late June and August). The third cluster refers to early and continuous heat waves in June and July, while the fourth cluster reflects persistent heat in late July and August. The fifth cluster shows a variation in temperatures between the beginning and the end of summer. The excluded noise variables represent inconsistent data or outliers that did not belong to any cluster. This contributes to improving the accuracy of climate models. The results highlight characteristic climatic patterns and provide recommendations for strengthening environmental and agricultural planning.]]>

IRAQI JOURNAL OF STATISTICAL SCIENCESVol. 22(1)
University of Mosul (IQ)
Climate action
Openalex Percentile: Top 88%
Traffic Prediction and Management Techniques
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