Special Issue "The Application of Artificial Intelligent in Hydrology"

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrology and Hydrogeology".

Deadline for manuscript submissions: 15 November 2020.

Special Issue Editor

Dr. Gonzalo Astray
Website
Guest Editor
Physical Chemistry Department, Universidade de Vigo, Vigo, Spain
Interests: machine learning; physical chemistry; hydrology; food technology; palynology; solar radiation
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Over the last few decades, the use of artificial intelligence (AI) has experienced a high increase in a wide variety of research fields. This kind of models are characterized as powerful tools to obtain information which would otherwise be very complicated or impossible to get. AI models, together with the large amount of hydrologycal data currently available, provide the ideal conditions to create tools aimed at managing water supply, predicting flood and drought, monitoring water quality, optimizing irrigation schemes, managing dams, determining carbonate saturation, evaluating the sedimentation process, and modeling the contaminant transport, among others. All the AI models, from the simplest to the most complex, such as random forest or neural networks, therefore allow expanding the existing knowledge about the complex water system.

The aim of this Special Issue on “The Application of Artificial Intelligent in Hydrology” is to present the state-of-the-art related (but not limited) to the study of movements, distribution, and management of water in nature.

We invite authors to submit research articles, reviews, communications, and concept papers that demonstrate the high potential of artificial intelligence in the hydrological field.

Dr. Gonzalo Astray
Guest Editor

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 papers will be 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 monthly 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 1800 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
  • Machine learning
  • Big data/Cloud computing
  • Monitoring/Modelling/Prediction/Optimization
  • Flow prediction
  • Water quality
  • Water supply
  • Management
  • Risk assessment
  • Multidisciplinary research

Published Papers (1 paper)

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Research

Open AccessArticle
Utility of Artificial Neural Networks in Modeling Pan Evaporation in Hyper-Arid Climates
Water 2020, 12(5), 1508; https://doi.org/10.3390/w12051508 - 25 May 2020
Abstract
Evaporation is the major water-loss component of the hydrologic cycle and thus requires efficient management. This study aims to model daily pan evaporation rates in hyper-arid climates using artificial neural networks (ANNs). Hyper-arid climates are characterized by harsh environmental conditions where annual precipitation [...] Read more.
Evaporation is the major water-loss component of the hydrologic cycle and thus requires efficient management. This study aims to model daily pan evaporation rates in hyper-arid climates using artificial neural networks (ANNs). Hyper-arid climates are characterized by harsh environmental conditions where annual precipitation rates do not exceed 3% of annual evaporation rates. For the first time, ANNs were applied to model such climatic conditions in the State of Kuwait. Pan evaporation data from 1993–2015 were normalized to a 0–1 range to boost ANN performance and the ANN structure was optimized by testing various meteorological input combinations. Levenberg–Marquardt algorithms were used to train the ANN models. The proposed ANN was satisfactorily efficient in modeling pan evaporation in these hyper-arid climatic conditions. The Nash–Sutcliffe coefficients ranged from 0.405 to 0.755 over the validation period. Mean air temperatures and average wind speeds were identified as meteorological variables that most influenced the ANN performance. A sensitivity analysis showed that the number of hidden layers did not significantly impact the ANN performance. The ANN models demonstrated considerable bias in predicting high pan evaporation rates (>25 mm/day). The proposed modeling method may assist water managers in Kuwait and other hyper-arid regions in establishing resilient water-management plans. Full article
(This article belongs to the Special Issue The Application of Artificial Intelligent in Hydrology)
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