Multivariate analysis in water management refers to
the use of statistical techniques to analyse and interpret multiple variables
or factors that affect water resources. It involves studying the relationships
and interactions between various factors such as rainfall, temperature,
groundwater levels, water quality parameters, land use patterns, and human
activities.
Multivariate
statistical techniques have been utilized to organize and simplify information
as well as characterize the quality of freshwater, seawater, and sediment. In
recent years, multivariate statistical techniques have been frequently used to analyse
water quality.
Some common
multivariate analysis techniques used in water management include:
1. Principal
Component Analysis (PCA): PCA is used to identify the underlying patterns and
relationships among a large set of variables. It helps in reducing the
dimensionality of the data and identifying the most important variables that
contribute to variations in water resources. Principal Component Analysis (PCA)
is a technique used to statistically extract linear relationships from a group
of variables. Principal component 1 (PC1) explains the most variance in the
original data, according to the principal components produced during the
analysis. The factor loadings describe the strength of dataset such as
"strong," "moderate," and "weak," with absolute
loading values of 0.75, 0.75-0.50, and 0.50-0.30, respectively. However,
loading does not indicate the significance of the component itself; rather, it
reflects the relative relevance of a variable inside the component.
Kaiser-Meyer-Olkin (KMO) and Bartlett's tests were used to determine
whether the datasets were adequate for PCA. The least KMO value that is deemed
acceptable is 0.5, and high values close to 1 signify the efficacy of PCA in
the investigation.
2. Cluster
Analysis: Cluster analysis is used to group similar water resource
characteristics or locations based on multiple variables. It helps in
identifying distinct water resource zones or clusters with similar
characteristics, which can aid in targeted management strategies. Cluster
analysis was used to divide a big data collection into groups based on a
predefined set of characteristics. Cluster analysis uses similarities and
dissimilarities to identify generally homogeneous groups or clusters of
sampling sites.
3.
Regression Analysis: Regression analysis is used to model and understand the relationships
between dependent and independent variables. In water management, it can be
used to analyze the relationship between rainfall and groundwater levels, or
between land use patterns and water quality parameters.
4.
Discriminant Analysis: Discriminant analysis is used to classify or
discriminate between different water resource conditions based on multiple
variables. It helps in identifying factors that differentiate between different
water resource categories, such as polluted vs. non-polluted water bodies.
5. Factor
Analysis: Factor analysis is used to identify underlying factors or dimensions
that explain the correlations among a set of observed variables. In water
management, it can be used to identify common factors influencing water quality
or availability.
By using
multivariate analysis techniques, water managers can gain a deeper
understanding of the complex relationships and dynamics within water systems.
This knowledge can then be used to develop effective strategies for water
allocation, conservation, pollution control, and sustainable water resource
management.