Data Handling and Mining for Water Resources Planning and Management
A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "New Sensors, New Technologies and Machine Learning in Water Sciences".
Deadline for manuscript submissions: closed (10 December 2022) | Viewed by 3295
Special Issue Editors
Interests: modelling and control of water and environmental engineering systems
Interests: water resources planning and management; artificial intelligence modelling of environmental systems; climate change impacts on water resources; groudwater evaluation, modelling and management; statistical analysis of floods and low flows; hydro-meteorological data
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
As water-related data sets have grown in size and complexity, conventional statistical and inference data analysis has increasingly been augmented with automated data processing, employing machine learning techniques such as neural networks, cluster analysis, genetic algorithms, decision trees and decision rules, and support vector machines. Data mining is the process of applying these methods with the intention of uncovering hidden patterns in large data sets. This Special Issue will focus on the data handling and mining of water-related data sets, to extract and discover patterns and knowledge from large water data sets and transform knowledge into comprehensible information for further use. The issue will cover issues such as database and data management aspects, data pre-processing, model and inference considerations, complexity considerations, post-processing of discovered structures, visualisation, as well as application of computer decision support system, including artificial intelligence.
Dr. Rabee Rustum
Prof. Dr. Adebayo J. Adeloye
Guest Editors
Manuscript Submission Information
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Keywords
- artificial intelligence
- classification
- clustering
- computer decision support system
- data management
- data preparation
- data pre-processing
- data understanding
- database systems
- knowledge discovery in databases
- large data sets
- machine learning
- statistical inference
- statistics
- visualisation
- others
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