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AI Solutions for Improving Sustainability in Water Resource Management
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AI Solutions for Improving Sustainability in Water Resource Management

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Water Management".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 3847

Special Issue Editors

Dr. Hossein Bonakdari

E-Mail Website
Guest Editor
Department of Soil and Agri-Food Engineering, Universite Laval, Québec, QC G1V 0A6, Canada
Interests: water resources management; hydrological modelling; artificial intelligence; sustainable development; time series
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Bahram Gharabaghi

E-Mail Website
Guest Editor
School of Engineering, University of Guelph, Guelph, ON NIG 2W1, Canada
Interests: hydrology; sediment transport; water quality; watershed management; climate change; water resources
Special Issues, Collections and Topics in MDPI journals
Dr. Silvio José Gumiere

E-Mail Website
Guest Editor
Department of Soil and Agri-Food Engineering, Universite Laval, Québec, QC G1V 0A6, Canada
Interests: water erosion; sediment transport; hydrology; environmental modeling; numer-ical methods; water resources
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Water resources play a pivotal role in fostering sustainable socio-economic advancement and preserving the environment for future generations. While prevalent techniques in water resource management primarily hinge on time series modeling, they often presume linearity in water demand and usage data. These conventional approaches employ models and methods that overlook the intricacies inherent in the datasets. Hence, the precision of forecasting water quantity and quality time series holds immense significance for sustainable progress, impacting economic, social, and environmental domains.

The examination of historical datasets through cutting-edge artificial intelligence modeling techniques is a promising avenue for innovative water resources management solutions. This field holds the potential to surmount the limitations posed by complex input datasets inherent in deterministic hydrologic models. This Special Issue endeavors to address two core objectives:

  1. The development of novel pioneering artificial intelligence (AI) and stochastic techniques tailored for modeling water quantity and quality time series, which could overcome the limits of conventional methodologies;
  2. The establishment of more accurate and streamlined predictive models, geared towards real-time forecasting, optimization, and the automation of meteorological and hydrological watershed variables. These efforts are directed to enhance our comprehension of water resource management challenges entwined with the realm of sustainable development in today's swiftly globalizing and urbanizing landscape.

Within this context, research that delves into the intricate and dynamic meteorological and hydrological watershed variables, coupled with the integration of novel modeling approaches, tool creation, and enhancements in existing predictive models, is of utter significance. Thus, this Special Issue seeks to provide a platform for the exchange of knowledge and expertise in the sphere of water sustainable water resource management.

We look forward to receiving your contributions.

Dr. Hossein Bonakdari
Prof. Dr. Bahram Gharabaghi
Dr. Silvio José Gumiere
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. Sustainability 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 2400 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

  • time series
  • watershed
  • artificial intelligence
  • stochastic methods
  • hydrology
  • sustainability
  • hydrological processes
  • real-time prediction
  • optimization algorithms
  • predictive modelling
  • water balance
  • environmental sustainability
  • water demand
  • meteorological variables
  • water quantity and quality
  • watershed variables

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Published Papers (5 papers)

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Research

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18 pages, 3584 KiB  
Article
Advanced Predictive Modeling for Dam Occupancy Using Historical and Meteorological Data
by Ahmet Cemkut Badem, Recep Yılmaz, Muhammet Raşit Cesur and Elif Cesur
Sustainability 2024, 16(17), 7696; https://doi.org/10.3390/su16177696 - 4 Sep 2024
Viewed by 484
Abstract
Dams significantly impact the environment, industries, residential areas, and agriculture. Efficient dam management can mitigate negative impacts and enhance benefits such as flood and drought reduction, energy efficiency, water access, and improved irrigation. This study tackles the critical issue of predicting dam occupancy [...] Read more.
Dams significantly impact the environment, industries, residential areas, and agriculture. Efficient dam management can mitigate negative impacts and enhance benefits such as flood and drought reduction, energy efficiency, water access, and improved irrigation. This study tackles the critical issue of predicting dam occupancy levels precisely to contribute to sustainable water management by enabling efficient water allocation among sectors, proactive drought management, controlled flood risk mitigation, and preservation of downstream ecological integrity. Our research suggests that combining physical models of water inflow and outflow “such as evapotranspiration using the Penman–Monteith equation, along with parameters like water consumption, solar radiation, and rainfall” with data-driven models based on historical reservoir data is crucial for accurately predicting occupancy levels. We implemented various prediction models, including Random Forest, Extra Trees, Long Short-Term Memory, Orthogonal Matching Pursuit CV, and Lasso Lars CV. To strengthen our proposed model with robust evidence, we conducted statistical tests on the mean absolute percentage errors of the models. Consequently, we demonstrated the impact of physical model parameters on prediction performance and identified the best method for predicting dam occupancy levels by comparing it with findings from the scientific literature. Full article
(This article belongs to the Special Issue AI Solutions for Improving Sustainability in Water Resource Management)
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22 pages, 7626 KiB  
Article
An Improved Aggregation–Decomposition Optimization Approach for Ecological Flow Supply in Parallel Reservoir Systems
by Inkyung Min, Nakyung Lee, Sanha Kim, Yelim Bang, Juyeon Jang, Kichul Jung and Daeryong Park
Sustainability 2024, 16(17), 7475; https://doi.org/10.3390/su16177475 - 29 Aug 2024
Viewed by 352
Abstract
The efficient operation of multi-reservoirs is highly beneficial for securing supply for prevailing demand and ecological flow. This study proposes a monthly hedging rule-based aggregation–decomposition model for optimizing a parallel reservoir system. The proposed model, which is an aggregated hedging rule for ecological [...] Read more.
The efficient operation of multi-reservoirs is highly beneficial for securing supply for prevailing demand and ecological flow. This study proposes a monthly hedging rule-based aggregation–decomposition model for optimizing a parallel reservoir system. The proposed model, which is an aggregated hedging rule for ecological flow (AHRE), uses external optimization to determine the total release of the reservoir system based on improved hedging rules—the optimization model aims to minimize water demand and ecological flow deficits. Additionally, inner optimization distributes the release to individual reservoirs to maintain equal reservoir storage rates. To verify the effectiveness of the AHRE, a standard operation policy and transformed hedging rules were selected for comparison. Three parallel reservoirs in the Naesung Stream Basin in South Korea were selected as a study area. The results of this study demonstrate that the AHRE is better than the other two methods in terms of supplying water in line with demand and ecological flow. In addition, the AHRE showed relatively stable operation results with small water-level fluctuations, owing to the application of improved hedging rules and a decomposition method. The results indicate that the AHRE has the capacity to improve downstream river ecosystems while maintaining human water use and provide a superior response to uncertain droughts. Full article
(This article belongs to the Special Issue AI Solutions for Improving Sustainability in Water Resource Management)
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22 pages, 9241 KiB  
Article
Research on Air Quality in Response to Meteorological Factors Based on the Informer Model
by Xiaoqing Tian, Chaoqun Zhang, Huan Liu, Baofeng Zhang, Cheng Lu, Pengfei Jiao and Songkai Ren
Sustainability 2024, 16(16), 6794; https://doi.org/10.3390/su16166794 - 8 Aug 2024
Viewed by 611
Abstract
The quality of the air exerts considerable effects on human health, and meteorological factors affect air quality. The relationships between meteorological factors and air quality parameters are complex dependency correlations. This article is based on the air quality monitoring data and meteorological monitoring [...] Read more.
The quality of the air exerts considerable effects on human health, and meteorological factors affect air quality. The relationships between meteorological factors and air quality parameters are complex dependency correlations. This article is based on the air quality monitoring data and meteorological monitoring data obtained from a monitoring station in Binjiang District, Hangzhou City, China, spanning from 01:00 on 14 April 2021 to 23:00 on 31 December 2021. The Informer model was used to explore the air quality parameters’ response to meteorological factors. By analyzing 12 different kinds of 2-Minute Average Wind Speed (2-MAWSP), 10-Minute Average Wind Speed (10-MAWSP), and Maximum Wind Speed (MXSPD); 16 different kinds of Hourly Precipitation (HP) and Air Temperature (AT); 11 different kinds of Relative Humidity (RH); and 8 different kinds of Station Pressure (STP), the following results were obtained: (1) The influence of wind speed on various air quality parameters is multifaceted and lacks a standardized form, potentially influenced by factors like wind direction and geographical location. One clear effect of wind speed is evident in the levels of particulate matter 10 (with an aerodynamic diameter smaller than 10 μm, PM10), as the values of this parameter first decrease and then increase with increasing wind speed. (2) HP has an evident reducing effect on most air quality parameters, including particulate matter (including PM2.5 and PM10), ozone (O3), sulfur dioxide (SO2), and nitrogen dioxide (NO2), as well as nitrogen oxides (NOx). (3) The increase in AT has a clear reducing effect on the concentration of NO2, while the trend for the concentrations of PM10 and NOx is one of initial decrease followed by a gradual rise. (4) RH only reduces the concentrations of SO2 and PM10. (5) With the rise in STP, the concentrations of most air quality parameters generally rise as well, except for the decrease in NOx concentration. This can give some indications and assistance to meteorological and environmental departments for improving air quality. This model can be used for a performance analysis and the forecasting of multi-parameter non-correlated systems. Full article
(This article belongs to the Special Issue AI Solutions for Improving Sustainability in Water Resource Management)
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15 pages, 2799 KiB  
Article
A Comparative Analysis of Advanced Machine Learning Techniques for River Streamflow Time-Series Forecasting
by Antoifi Abdoulhalik and Ashraf A. Ahmed
Sustainability 2024, 16(10), 4005; https://doi.org/10.3390/su16104005 - 10 May 2024
Viewed by 839
Abstract
This study examines the contribution of rainfall data (RF) in improving the streamflow-forecasting accuracy of advanced machine learning (ML) models in the Syr Darya River Basin. Different sets of scenarios included rainfall data from different weather stations located in various geographical locations with [...] Read more.
This study examines the contribution of rainfall data (RF) in improving the streamflow-forecasting accuracy of advanced machine learning (ML) models in the Syr Darya River Basin. Different sets of scenarios included rainfall data from different weather stations located in various geographical locations with respect to the flow monitoring station. Long short-term memory (LSTM)-based models were used to examine the contribution of rainfall data on streamflow-forecasting performance by investigating five scenarios whereby RF data from different weather stations were incorporated depending on their geographical positions. Specifically, the All-RF scenario included all rainfall data collected at 11 stations; Upstream-RF (Up-RF) and Downstream-RF (Down-RF) included only the rainfall data measured upstream and downstream of the streamflow-measuring station; Pearson-RF (P-RF) only included the rainfall data exhibiting the highest level of correlation with the streamflow data, and the Flow-only (FO) scenario included streamflow data. The evaluation metrics used to quantitively assess the performance of the models included the RMSE, MAE, and the coefficient of determination, R2. Both ML models performed best in the FO scenario, which shows that the diversity of input features (hydrological and meteorological data) did not improve the predictive accuracy regardless of the positions of the weather stations. The results show that the P-RF scenarios yielded better prediction accuracy compared to all the other scenarios including rainfall data, which suggests that only rainfall data upstream of the flow monitoring station tend to make a positive contribution to the model’s forecasting performance. The findings evidence the suitability of simple monolayer LSTM-based networks with only streamflow data as input features for high-performance and budget-wise river flow forecast applications while minimizing data processing time. Full article
(This article belongs to the Special Issue AI Solutions for Improving Sustainability in Water Resource Management)
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Review

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29 pages, 1730 KiB  
Review
Hydrogel Applications in Nitrogen and Phosphorus Compounds Recovery from Water and Wastewater: An Overview
by Daniel Szopa, Paulina Wróbel, Beata Anwajler and Anna Witek-Krowiak
Sustainability 2024, 16(15), 6321; https://doi.org/10.3390/su16156321 - 24 Jul 2024
Viewed by 752
Abstract
This article provides an overview of the diverse applications of hydrogels in nutrient recovery from water and wastewater. Due to their unique properties, such as high water-retention capacity, nutrient rerelease, and tunable porosity, hydrogels have emerged as promising materials for efficient nutrient capture [...] Read more.
This article provides an overview of the diverse applications of hydrogels in nutrient recovery from water and wastewater. Due to their unique properties, such as high water-retention capacity, nutrient rerelease, and tunable porosity, hydrogels have emerged as promising materials for efficient nutrient capture and recycling. It has been suggested that hydrogels, depending on their composition, can be reused in agriculture, especially in drought-prone areas. Further research paths have been identified that could expand their application in these regions. However, the main focus of the article is to highlight the current gaps in understanding how hydrogels bind nitrogen and phosphorus compounds. The study underscores the need for research that specifically examines how different components of hydrogel matrices interact with each other and with recovered nutrients. Furthermore, it is essential to assess how various nutrient-recovery parameters, such as temperature, pH, and heavy metal content, interact with each other and with specific matrix compositions. This type of research is crucial for enhancing both the recovery efficiency and selectivity of these hydrogels, which are critical for advancing nutrient-recovery technologies and agricultural applications. A comprehensive research approach involves using structured research methodologies and optimization techniques to streamline studies and identify crucial relationships. Full article
(This article belongs to the Special Issue AI Solutions for Improving Sustainability in Water Resource Management)
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