Local‐Scale Rainy Season Onset Detection: A New Approach Based on Principal Component Analysis and its Application to Vietnam

Abstract In agroclimatology, the rainy season onset and cessation dates are often defined from a combination of several empirical rainfall thresholds. For example, the onset may be the first wet day of N consecutive days receiving at least P millimeters without a dry spell lasting n days and receiving less than p millimeters in the following C days. These thresholds are parameterized empirically in order to fit the requirements of a given crop and to account for local-scale climatic conditions. Such local-scale agroclimatic definition is rigid because each threshold may not be necessarily transposable to other crops and other climate environments. A new approach is developed to define onset/cessation dates and monitor their interannual variability at the regional scale. This new approach is less sensitive to parameterization and local-scale contingencies but still has some significance at the local scale. The approach considers multiple combinations of rainfall thresholds in a principal component analysis so that a robust signal across space and parameters is extracted. The regional-scale onset/cessation date is unequally influenced by input rainfall parameters used for the definition of the local rainy season onset. It appears that P is a crucial parameter to define onset, C plays a significant role at most stations, and N seems to be of marginal influence.

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Scaled PCA: A New Approach to Dimension Reduction

Management Science ◽

10.1287/mnsc.2021.4020 ◽

2021 ◽

Author(s):

Dashan Huang ◽

Fuwei Jiang ◽

Kunpeng Li ◽

Guoshi Tong ◽

Guofu Zhou

Keyword(s):

Principal Component Analysis ◽

Supervised Learning ◽

Principal Component ◽

Real Data ◽

Component Analysis ◽

General Factor ◽

Macroeconomic Forecasting ◽

New Approach ◽

Learning Technique ◽

The Common

This paper proposes a novel supervised learning technique for forecasting: scaled principal component analysis (sPCA). The sPCA improves the traditional principal component analysis (PCA) by scaling each predictor with its predictive slope on the target to be forecasted. Unlike the PCA that maximizes the common variation of the predictors, the sPCA assigns more weight to those predictors with stronger forecasting power. In a general factor framework, we show that, under some appropriate conditions on data, the sPCA forecast beats the PCA forecast, and when these conditions break down, extensive simulations indicate that the sPCA still has a large chance to outperform the PCA. A real data example on macroeconomic forecasting shows that the sPCA has better performance in general.

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Evaluation of the quality of superficial sediment using principal components analysis of the physical–chemical attributes of the Mearim River – Brazil

Ciência e Natura ◽

10.5902/2179460x41267 ◽

2020 ◽

Vol 42 ◽

pp. e5

Author(s):

Neuriane Silva Lima ◽

Darlan Ferreira da Silva ◽

Wallace Ribeiro Nunes Neto ◽

Delmo Matos da Silva ◽

Leila Cristina Almeida de Sousa ◽

...

Keyword(s):

Principal Component Analysis ◽

Principal Components ◽

Rainy Season ◽

Principal Component ◽

Component Analysis ◽

Dry Period ◽

Data Set ◽

Inorganic Matter ◽

Sampling Points

The Mearim River is one of the main rivers of Maranhão, which, over the years, has been affected from pollution caused by human activities such as deforestation, disposal of domestic effluents, and agricultural activities, among others. The objective of this research was to evaluate the environmental quality of the Mearim River through the study of the sediment in different periods. In order to investigate this question, four sampling points (P1- Balneário, P2-Cais, P3-Trizidela, and P4-Matadouro) were submitted to particle size analysis (clay, silt, and fine sand) and physico-chemical analyses (pH, organic matter, and inorganic and organic carbon). Two principal components were generated in principal component analysis, explaining 73% of the total variance among the parameters within the studied periods. The overall analysis of the data set by principal component analysis highlighted two clusters: one relating the attributes to three sampling points analyzed in the rainy season and another relating the attributes to four sampling points analyzed in the dry period. Multivariate analysis of the data showed that the organic matter, clay, and pH parameters were directly correlated with the dry period (correlation coefficients 0.41), and inorganic matter (correlation coefficient = | 0,414) was more sensitive in the rainy season.

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