Special Issue "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 1242

Special Issue Editors

Dr. Rabee Rustum
E-Mail Website
Guest Editor
School of Energy, Geoscience, Infrastructure and Society, Heriot-Watt University, Dubai, UAE
Interests: modelling and control of water and environmental engineering systems
Prof. Dr. Adebayo J. Adeloye
E-Mail Website
Guest Editor
School of Energy, Geoscience,‎ Infrastructure and Society,‎ Heriot-Watt University, Edinburgh, UK
Interests: water resources management; climate change; water security; modelling of complex environmental systems

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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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

Published Papers (1 paper)

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Research

Article
Curating 62 Years of Walnut Gulch Experimental Watershed Data: Improving the Quality of Long-Term Rainfall and Runoff Datasets
Water 2022, 14(14), 2198; https://doi.org/10.3390/w14142198 - 12 Jul 2022
Viewed by 822
Abstract
The curation of hydrologic data includes quality control, documentation, database development, and provisions for public access. This article describes the development of new quality control procedures for experimental watersheds like the Walnut Gulch Experimental Watersheds (WGEW). WGEW is a 149 km2 watershed [...] Read more.
The curation of hydrologic data includes quality control, documentation, database development, and provisions for public access. This article describes the development of new quality control procedures for experimental watersheds like the Walnut Gulch Experimental Watersheds (WGEW). WGEW is a 149 km2 watershed outdoor hydrologic laboratory equipped with a dense network of hydro-climatic instruments since the 1950s. To improve data accuracy from the constantly growing instrumentation networks in numerous experimental watersheds, we developed five new QAQC tools based on fundamental hydrologic principles. The tools include visual analysis of interpolated rainfall maps and evaluating temporal, spatial, and quantitative relationships between paired rainfall-runoff events, including runoff lag time, runoff coefficients, multiple regression, and association methods. The methods identified questionable rainfall and runoff observations in the WGEW database that were not usually captured by the existing QAQC procedures. The new tools were evaluated and confirmed using existing metadata, paper charts, and graphical visualization tools. It was found that 13% of the days (n = 780) with rainfall and 7% of the runoff events sampled had errors. Omitting these events improved the quality and reliability of the WGEW dataset for hydrologic modeling and analyses. This indicated the effectiveness of application of conventional hydrologic relations to improve the QAQC strategy for experimental watershed datasets. Full article
(This article belongs to the Special Issue Data Handling and Mining for Water Resources Planning and Management)
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