Data Mining in Environment and Ecology in Coastal Areas

Dear Colleagues,

Coastal areas are one of the developed areas between ocean and land. About half of the world’s population living within 60 km of the coastline are located along the coast. The environmental pollution and ecological disasters of coastal areas have resulted in the most serious of environmental and ecological problems, such as eutrophication, hypoxia, harmful algal blooms (HABs), heavy metal pollution and their biomagnifications, typhoon, and sea level rise. Anthropogenic and natural effects are recognized as growing problems in many estuaries and coastal areas around the world. Hence, sample monitoring, including remote sensing, long-term and in situ data, produce large data systems and are paramount to helping understand the spatial and temporal variations of coastal areas. To extract the latent meaningful information, data mining, chemometrics, multivariate statistical analysis and different biotic indices for biodiversity data are used; these may include factor analysis, cluster analysis, discriminant analysis, self-organizing maps, artificial neural networks, canonical correspondence analysis, redundancy analysis and many biotic indices.

The aim of this Research Topic is to explore the recently used or newly developed methodologies involving data mining, chemometrics, multivariate analysis or biotic indices, with an emphasis on land–ocean interactions in the coastal zone, in order to solve the environmental and ecological problems. The following subtopics will be included but are not limited to:

1. Environment pollution caused by land–ocean interactions under both land–oceans and ocean–land orientations, such as human activities and natural changes.

2. Coastal areas produced by the environment and ecological monitoring networks and chemical parameters.

3. The origin, cause and results of coastal environmental and biogeological changes discussed under a large chemical and biological-based data set.

Prof. Dr. Meilin Wu
Guest Editor

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• water quality
• environment and ecological status
• heavy metal
• POPs
• PAHs
• data mining
• multivariate statistical analysis
• chemometric
• micropollutants
• spatial and temporal variation