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The Application of Artificial Intelligence in Hydrology, Volume II
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The Application of Artificial Intelligence in Hydrology, Volume II

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

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 18074

Special Issue Editor

Dr. Gonzalo Astray

E-Mail Website
Guest Editor
Department of Physical Chemistry, Faculty of Sciences, University of Vigo, 32004 Ourense, Spain
Interests: artificial intelligence (neural networks, fuzzy logic, expert systems, etc.); physical chemistry; water management; hydrology; food technology; bioinformatics; palynology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the last few decades, the use of artificial intelligence (AI) has undergone a significant increase in a wide variety of research fields. It could be said that currently, AI provides systems that are capable of learning and making decisions aimed to solve/optimize problems that, otherwise, would be very complicated.

Artificial intelligence, together with a large amount of hydrological data currently available, provide the ideal conditions to create AI tools aimed at managing water supply, flood, and drought risk assessment, monitoring water quality, modeling groundwater level, predicting suspended sediment load, managing dams, modeling rainfall–runoff processes or modeling contaminant transport, among others. Due to this, AI techniques, from the simplest to the most complex, allow us to expand our knowledge of the hydrology field.

The aim of this Special Issue on “The Application of Artificial Intelligence in Hydrology, Volume II” 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 huge 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 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

  • artificial intelligence
  • machine learning
  • big data/cloud computing
  • monitoring/modeling/prediction/optimization
  • flow prediction
  • water quality
  • water supply
  • management
  • risk assessment
  • multidisciplinary research

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Published Papers (6 papers)

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Research

21 pages, 6714 KiB  
Article
Assessment of Different Machine Learning Methods for Reservoir Outflow Forecasting
by Anton Soria-Lopez, Carlos Sobrido-Pouso, Juan C. Mejuto and Gonzalo Astray
Water 2023, 15(19), 3380; https://doi.org/10.3390/w15193380 - 27 Sep 2023
Cited by 1 | Viewed by 1987
Abstract
Reservoirs play an important function in human society due to their ability to hold and regulate the flow. This will play a key role in the future decades due to climate change. Therefore, having reliable predictions of the outflow from a reservoir is [...] Read more.
Reservoirs play an important function in human society due to their ability to hold and regulate the flow. This will play a key role in the future decades due to climate change. Therefore, having reliable predictions of the outflow from a reservoir is necessary for early warning systems and adequate water management. In this sense, this study uses three approaches machine learning (ML)-based techniques—Random Forest (RF), Support Vector Machine (SVM) and artificial neural network (ANN)—to predict outflow one day ahead of eight different dams belonging to the Miño-Sil Hydrographic Confederation (Galicia, Spain), using three input variables of the current day. Mostly, the results obtained showed that the suggested models work correctly in predicting reservoir outflow in normal conditions. Among the different ML approaches analyzed, ANN was the most appropriate technique since it was the one that provided the best model in five reservoirs. Full article
(This article belongs to the Special Issue The Application of Artificial Intelligence in Hydrology, Volume II)
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20 pages, 6261 KiB  
Article
Explainable Artificial Intelligence in Hydrology: Interpreting Black-Box Snowmelt-Driven Streamflow Predictions in an Arid Andean Basin of North-Central Chile
by Jorge Núñez, Catalina B. Cortés and Marjorie A. Yáñez
Water 2023, 15(19), 3369; https://doi.org/10.3390/w15193369 - 26 Sep 2023
Cited by 3 | Viewed by 1857
Abstract
In recent years, a new discipline known as Explainable Artificial Intelligence (XAI) has emerged, which has followed the growing trend experienced by Artificial Intelligence over the last decades. There are, however, important gaps in the adoption of XAI in hydrology research, in terms [...] Read more.
In recent years, a new discipline known as Explainable Artificial Intelligence (XAI) has emerged, which has followed the growing trend experienced by Artificial Intelligence over the last decades. There are, however, important gaps in the adoption of XAI in hydrology research, in terms of application studies in the southern hemisphere, or in studies associated with snowmelt-driven streamflow prediction in arid regions, to mention a few. This paper seeks to contribute to filling these knowledge gaps through the application of XAI techniques in snowmelt-driven streamflow prediction in a basin located in the arid region of north-central Chile in South America. For this, two prediction models were built using the Random Forest algorithm, for one and four months in advance. The models show good prediction performance in the training set for one (RMSE:1.33, R2: 0.94, MAE:0.55) and four (RMSE: 5.67, R2:0.94, MAE: 1.51) months in advance. The selected interpretation techniques (importance of the variable, partial dependence plot, accumulated local effects plot, Shapley values and local interpretable model-agnostic explanations) show that hydrometeorological variables in the vicinity of the basin are more important than climate variables and this occurs both for the dataset level and for the months with the lowest streamflow records. The importance of the XAI approach adopted in this study is discussed in terms of its contribution to the understanding of hydrological processes, as well as its role in high-stakes decision-making. Full article
(This article belongs to the Special Issue The Application of Artificial Intelligence in Hydrology, Volume II)
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23 pages, 9034 KiB  
Article
A Spatiotemporal Deep Learning Approach for Urban Pluvial Flood Forecasting with Multi-Source Data
by Benjamin Burrichter, Julian Hofmann, Juliana Koltermann da Silva, Andre Niemann and Markus Quirmbach
Water 2023, 15(9), 1760; https://doi.org/10.3390/w15091760 - 3 May 2023
Cited by 9 | Viewed by 3997
Abstract
This study presents a deep-learning-based forecast model for spatial and temporal prediction of pluvial flooding. The developed model can produce the flooding situation for the upcoming time steps as a sequence of flooding maps. Thus, a dynamic overview of the forthcoming flooding situation [...] Read more.
This study presents a deep-learning-based forecast model for spatial and temporal prediction of pluvial flooding. The developed model can produce the flooding situation for the upcoming time steps as a sequence of flooding maps. Thus, a dynamic overview of the forthcoming flooding situation is generated to support the decision of crisis management actors. The influence of different input data, data formats, and model setups on the prediction results was investigated. Data from multiple sources were considered as follows: precipitation information, spatial information, and an overflow forecast. In addition, models with different layers and network architectures such as convolutional layers, graph convolutional layers, or generative adversarial networks (GANs) were considered and evaluated. The data required to train and test the models were generated using a coupled hydrodynamic 1D/2D model. The model setup with the inclusion of all available input variables and an architecture with graph convolutional layers presented, in general, the best results in terms of root mean square error (RMSE) and critical success index (CSI). The prediction results of the final model showed a high agreement with the simulation results of the hydrodynamic model, with drastic reductions in computation time, making this model suitable for integration into an early warning system for pluvial flooding. Full article
(This article belongs to the Special Issue The Application of Artificial Intelligence in Hydrology, Volume II)
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17 pages, 4632 KiB  
Article
Application of Artificial Intelligence in the Assessment and Forecast of Avalanche Danger in the Ile Alatau Ridge
by Viktor Blagovechshenskiy, Akhmetkal Medeu, Tamara Gulyayeva, Vitaliy Zhdanov, Sandugash Ranova, Aidana Kamalbekova and Ulzhan Aldabergen
Water 2023, 15(7), 1438; https://doi.org/10.3390/w15071438 - 6 Apr 2023
Cited by 2 | Viewed by 3115
Abstract
The assessment and forecast of avalanche danger are very important means of preventing avalanche fatalities, especially in recreational areas. The use of artificial intelligence methods for these purposes significantly increases the accuracy of avalanche forecasts. The purpose of this re-search was to improve [...] Read more.
The assessment and forecast of avalanche danger are very important means of preventing avalanche fatalities, especially in recreational areas. The use of artificial intelligence methods for these purposes significantly increases the accuracy of avalanche forecasts. The purpose of this re-search was to improve the methods for assessing and forecasting avalanche danger in the Ile Alatau Ridge. To create a training sample, the data from three meteorological and two avalanche stations for the period from 2002 to 2022 were used. The following predictors were chosen: air temperature, snow cover depth, precipitation, and snowpack stability index. The subject of the assessment and forecasts was the level of avalanche danger, assessed on a five-point scale. The program Statistica StatSoft was used as a neurosimulator. When forecasting avalanche danger, the predictive values of air temperature and precipitation, obtained from numerical weather forecast models, were used. The model correctly assessed the current level of avalanche danger in 90% of cases. The forecast of avalanche danger was justified in 80% of cases. The artificial intelligence program helped the avalanche forecaster to improve the forecast quality. This method is currently being used for compiling an avalanche bulletin for two river basins in the Ile Alatau. Full article
(This article belongs to the Special Issue The Application of Artificial Intelligence in Hydrology, Volume II)
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19 pages, 5060 KiB  
Article
Coupled Model for Assessing the Present and Future Watershed Vulnerabilities to Climate Change Impacts
by Adrián Martínez, Manuel Herrera, Jesús López de la Cruz and Ismael Orozco
Water 2023, 15(4), 711; https://doi.org/10.3390/w15040711 - 11 Feb 2023
Cited by 1 | Viewed by 2143
Abstract
There is great uncertainty about the future effects of climate change on the global economic, social, environmental, and water sectors. This paper focuses on watershed vulnerabilities to climate change by coupling a distributed hydrological model with artificial neural networks and spatially distributed indicators [...] Read more.
There is great uncertainty about the future effects of climate change on the global economic, social, environmental, and water sectors. This paper focuses on watershed vulnerabilities to climate change by coupling a distributed hydrological model with artificial neural networks and spatially distributed indicators for the use of a predictive model of such vulnerability. The analyses are complemented by a Monte Carlo evaluation of the uncertainty associated with the projections of the global circulation models, including how such uncertainty impacts the vulnerability forecast. To test the proposal, the paper uses current and future vulnerabilities of the Turbio River watershed, located in the semi-arid zone of Guanajuato (Mexico). The results show that nearly 50% of the watershed currently has medium and high vulnerabilities, and only the natural areas in the watershed show low vulnerabilities. In the future, an increase from medium to high vulnerability is expected to occur in urban and agricultural areas of the basin, with an associated uncertainty of ±15 mm in the projected precipitation. Full article
(This article belongs to the Special Issue The Application of Artificial Intelligence in Hydrology, Volume II)
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18 pages, 6616 KiB  
Article
Intercomparing LSTM and RNN to a Conceptual Hydrological Model for a Low-Land River with a Focus on the Flow Duration Curve
by Alexander Ley, Helge Bormann and Markus Casper
Water 2023, 15(3), 505; https://doi.org/10.3390/w15030505 - 27 Jan 2023
Cited by 9 | Viewed by 3368
Abstract
Machine learning (ML) algorithms slowly establish acceptance for the purpose of streamflow modelling within the hydrological community. Yet, generally valid statements about the modelling behavior of the ML models remain vague due to the uniqueness of catchment areas. We compared two ML models, [...] Read more.
Machine learning (ML) algorithms slowly establish acceptance for the purpose of streamflow modelling within the hydrological community. Yet, generally valid statements about the modelling behavior of the ML models remain vague due to the uniqueness of catchment areas. We compared two ML models, RNN and LSTM, to the conceptual hydrological model Hydrologiska Byråns Vattenbalansavdelning (HBV) within the low-land Ems catchment in Germany. Furthermore, we implemented a simple routing routine in the ML models and used simulated upstream streamflow as forcing data to test whether the individual model errors accumulate. The ML models have a superior model performance compared to the HBV model for a wide range of statistical performance indices. Yet, the ML models show a performance decline for low-flows in two of the sub-catchments. Signature indices sampling the flow duration curve reveal that the ML models in our study provide a good representation of the water balance, whereas the HBV model instead has its strength in the reproduction of streamflow dynamics. Regarding the applied routing routine in the ML models, there are no strong indications of an increasing error rising upstream to downstream throughout the sub-catchments. Full article
(This article belongs to the Special Issue The Application of Artificial Intelligence in Hydrology, Volume II)
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