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Data, Modeling, Remote Sensing, and Machine Learning-Driven Research on Water...
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Data, Modeling, Remote Sensing, and Machine Learning-Driven Research on Water and Watersheds

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 (31 May 2023) | Viewed by 2987

Special Issue Editors

Dr. Adnan Rajib

E-Mail Website
Guest Editor
Department of Environmental Engineering, Texas A&M University, Kingsville, TX, USA
Interests: ecosystem modeling; climate risks; earth observations; environmental informatics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Venkatesh Merwade

E-Mail Website
Guest Editor
Lyles School of Civil Engineering, Purdue University, West Lafayette, IN, USA
Interests: surface water hydrology; flood modeling and mapping; cyberinfrastructure for hydrology
Special Issues, Collections and Topics in MDPI journals
Dr. Zhu Liu

E-Mail Website
Guest Editor
College of Hydrology and Water Resource, Hohai University, Nanjing, China
Interests: climate change; flood modeling; watershed hydrology; uncertainty quantification; bayesian analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The increasing availability of open access data, models, remote sensing, and machine learning techniques is taking the science of water and watersheds to a new direction. The goal of this Special Issue is to aggregate research contributions along this new direction. We welcome high-quality articles (original research, technical notes, and reviews) on the use of open access data, modeling, remote sensing, and machine learning techniques to assess floods, droughts, and water quality as well as their interactions with climatic, anthropogenic, and ecological drivers. Depending on the topic and authors’ interests, the authors may submit their articles either to the Remote Sensing or the Water journal.

Potential topics include but are not limited to the following:

  • Flood and drought hazard forecasting, mapping, and management.
  • Water quality predictions in lakes, rivers, and estuaries.
  • Water use and irrigation efficiency in agricultural landscapes.
  • Improved land use/land cover mapping and change detection.
  • Improved process representation in hydrologic models via assimilation of remotely sensed Earth observations.
  • Climate change impacts on water availability and water extremes.
  • Next-generation remote sensing techniques (e.g., unmanned aerial vehicles) for improved representation of landscape features.
  • Deeping learning techniques in surface and subsurface hydrology.
  • Tools, workflows, and web-based decision-support frameworks for watershed management. 

You may choose our Joint Special Issue in Remote Sensing

Dr. Adnan Rajib
Prof. Dr. Venkatesh Merwade
Dr. Zhu Liu
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 2600 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

  • hydrology
  • water quality
  • floods
  • droughts
  • earth observations
  • data assimilation
  • GIS
  • climate change
  • land use change

Published Papers (1 paper)

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Research

21 pages, 1691 KiB  
Article
Comparing Single and Multiple Imputation Approaches for Missing Values in Univariate and Multivariate Water Level Data
by Nura Umar and Alison Gray
Water 2023, 15(8), 1519; https://doi.org/10.3390/w15081519 - 13 Apr 2023
Cited by 4 | Viewed by 2696
Abstract
Missing values in water level data is a persistent problem in data modelling and especially common in developing countries. Data imputation has received considerable research attention, to raise the quality of data in the study of extreme events such as flooding and droughts. [...] Read more.
Missing values in water level data is a persistent problem in data modelling and especially common in developing countries. Data imputation has received considerable research attention, to raise the quality of data in the study of extreme events such as flooding and droughts. This article evaluates single and multiple imputation methods used on monthly univariate and multivariate water level data from four water stations on the rivers Benue and Niger in Nigeria. The missing completely at random, missing at random and missing not at random data mechanisms were each considered. The best imputation method is identified using two error metrics: root mean square error and mean absolute percentage error. For the univariate case, the seasonal decomposition method is best for imputing missing values at various missingness levels for all three missing mechanisms, followed by Kalman smoothing, while random imputation is much poorer. For instance, for 5% missing data for the Kainji water station, missing completely at random, the Kalman smoothing, random and seasonal decomposition methods had average root mean square errors of 13.61, 102.60 and 10.46, respectively. For the multivariate case, missForest is best, closely followed by k nearest neighbour for the missing completely at random and missing at random mechanisms, and k nearest neighbour is best, followed by missForest, for the missing not at random mechanism. The random forest and predictive mean matching methods perform poorly in terms of the two metrics considered. For example, for 10% missing data missing completely at random for the Ibi water station, the average root mean square errors for random forest, k nearest neighbour, missForest and predictive mean matching were 22.51, 17.17, 14.60 and 25.98, respectively. The results indicate that the seasonal decomposition method, and missForest or k nearest neighbour methods, can impute univariate and multivariate water level missing data, respectively, with higher accuracy than the other methods considered. Full article
(This article belongs to the Special Issue Data, Modeling, Remote Sensing, and Machine Learning-Driven Research on Water and Watersheds)
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