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Advance in Time Series Modelling for Water Resources Management
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Advance in Time Series Modelling for Water Resources Management

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

Deadline for manuscript submissions: closed (1 August 2022) | Viewed by 25778

Special Issue Editors

Dr. Amir H. Azimi

E-Mail Website
Guest Editor
Department of Civil Engineering, Lakehead University, Thunder Bay, ON P7B 5E1, Canada
Interests: environmental sustainability; modelling; optimization algorithms; water resources engineering; transport of sediment; aquatic systems
Special Issues, Collections and Topics in MDPI journals
Dr. Hossein Bonakdari

E-Mail Website
Guest Editor
Department of Civil Engineering, University of Ottawa, Ottawa, ON K1N 6N5, Canada
Interests: water resources; hydrology; AI; climate change; sustainable development; time series; hydrological modelling; machine learning
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. Andrew Binns

E-Mail Website
Guest Editor
School of Engineering, University of Guelph, Guelph, ON NIG 2W1, Canada
Interests: surface water hydraulics; hydrological processes; rivers and streams; sediment transport
Special Issues, Collections and Topics in MDPI journals
Dr. Pijush Samui

E-Mail Website1 Website2
Guest Editor
Department of Civil Engineering, NIT Patna, Bihar, Patna 800005, India
Interests: machine learning, reliability; earthquake engineering; pile foundation; site characterization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Water resources are at the core of sustainable socio-economic development and environmental protection for future generations. Most of the current methods in water resource management are based on time series modelling, which assumes linearity in water demand and water use data, and utilises models and methods that do not consider the complex nature of the datasets involved. Accurate forecasting of water quantity/quality time series has major economic, social, and environmental implications for sustainable development. Analysis of the historic dataset-based time-series using advanced artificial intelligence modelling techniques offers promising new water resources management tools for overcoming the limitations of using the complex input datasets of the deterministic hydrologic models.

This Special Issue will focus on two primary goals: (1) Developing innovative artificial intelligence (AI) and/or stochastic-based techniques for water quantity/quality time series modelling purposes and (2) establishing more accurate and efficient predictive models for the monitoring and real-time prediction, optimisation, and for the automation of the meteorological and hydrological watershed variables. These objectives will also enhance our understanding of water resource problems associated with sustainable development in today’s rapidly globalizing and urbanising world. Research studies focusing on complex and dynamic meteorological/hydrological watershed variables and implementing novel modelling approaches, developing new tools, or improving the existing predictive models are especially welcome.

Prof. Hossein Bonakdari
Prof. Amir Hossein Azimi
Prof. Bahram Gharabaghi
Dr. Andrew D Binns
Dr. Pijush Samui
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 processes
  • Hydrology
  • Sustainability
  • Hydrological processes
  • Real-time prediction
  • Optimisation algorithms
  • Predictive modelling
  • Water balance
  • Environmental sustainability
  • Water demand

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Published Papers (6 papers)

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Research

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20 pages, 5184 KiB  
Article
Water Quality Prediction Based on LSTM and Attention Mechanism: A Case Study of the Burnett River, Australia
by Honglei Chen, Junbo Yang, Xiaohua Fu, Qingxing Zheng, Xinyu Song, Zeding Fu, Jiacheng Wang, Yingqi Liang, Hailong Yin, Zhiming Liu, Jie Jiang, He Wang and Xinxin Yang
Sustainability 2022, 14(20), 13231; https://doi.org/10.3390/su142013231 - 14 Oct 2022
Cited by 26 | Viewed by 3549
Abstract
Prediction of water quality is a critical aspect of water pollution control and prevention. The trend of water quality can be predicted using historical data collected from water quality monitoring and management of water environment. The present study aims to develop a long [...] Read more.
Prediction of water quality is a critical aspect of water pollution control and prevention. The trend of water quality can be predicted using historical data collected from water quality monitoring and management of water environment. The present study aims to develop a long short-term memory (LSTM) network and its attention-based (AT-LSTM) model to achieve the prediction of water quality in the Burnett River of Australia. The models developed in this study introduced an attention mechanism after feature extraction of water quality data in the section of Burnett River considering the effect of the sequences on the prediction results at different moments to enhance the influence of key features on the prediction results. This study provides one-step-ahead forecasting and multistep forward forecasting of dissolved oxygen (DO) of the Burnett River utilizing LSTM and AT-LSTM models and the comparison of the results. The research outcomes demonstrated that the inclusion of the attention mechanism improves the prediction performance of the LSTM model. Therefore, the AT-LSTM-based water quality forecasting model, developed in this study, demonstrated its stronger capability than the LSTM model for informing the Water Quality Improvement Plan of Queensland, Australia, to accurately predict water quality in the Burnett River. Full article
(This article belongs to the Special Issue Advance in Time Series Modelling for Water Resources Management)
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11 pages, 7523 KiB  
Article
PyGEE-SWToolbox: A Python Jupyter Notebook Toolbox for Interactive Surface Water Mapping and Analysis Using Google Earth Engine
by Collins Owusu, Nusrat J. Snigdha, Mackenzie T. Martin and Alfred J. Kalyanapu
Sustainability 2022, 14(5), 2557; https://doi.org/10.3390/su14052557 - 23 Feb 2022
Cited by 11 | Viewed by 5049
Abstract
Continuous monitoring of surface water resources is often challenging due to the lack of monitoring systems in remote areas and the high spatial distribution of water bodies. The Google Earth Engine (GEE) platform, which houses a large set of remote sensing datasets and [...] Read more.
Continuous monitoring of surface water resources is often challenging due to the lack of monitoring systems in remote areas and the high spatial distribution of water bodies. The Google Earth Engine (GEE) platform, which houses a large set of remote sensing datasets and geospatial processing power, has been applied in various aspects of surface water resources monitoring to solve some of the challenges. PyGEE-SWToolbox is a freely available Google Earth Engine-enabled open-source toolbox developed with Python to be run in Jupyter Notebooks that provides an easy-to-use graphical user interface (GUI) that enables the user to obtain time series of Landsat, Sentinel-1, and Sentinel-2 satellite imagery, pre-process them, and extract surface water using water indices, such as the Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), Automated Water Extraction Index (AWEI), and Dynamic Surface Water Extent (DSWE). The validation of the toolbox is carried out at four reservoir and lake locations: Elephant Butte Lake, Hubbard Creek Reservoir, Clearwater Lake, and Neversink Reservoir in the United States. A time series of the water surface area generated from PyGEE-SWToolbox compared to the observed surface areas yielded good results, with R2 ranging between 0.63 and 0.99 for Elephant Butte Lake, Hubbard Creek Reservoir, and Clearwater Lake except the Neversink Reservoir with a maximum R2 of 0.52. The purpose of PyGEE-SWToolbox is to provide water resource managers, engineers, researchers, and students a user-friendly environment to utilize the GEE platform for water resource monitoring and generation of datasets. The toolbox is accompanied by a step-by-step user manual and Readme documentation for installation and usage. Full article
(This article belongs to the Special Issue Advance in Time Series Modelling for Water Resources Management)
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21 pages, 3295 KiB  
Article
Estimation of Daily Water Table Level with Bimonthly Measurements in Restored Ombrotrophic Peatland
by Sebastian Gutierrez Pacheco, Robert Lagacé, Sandrine Hugron, Stéphane Godbout and Line Rochefort
Sustainability 2021, 13(10), 5474; https://doi.org/10.3390/su13105474 - 13 May 2021
Cited by 2 | Viewed by 2607
Abstract
Daily measurements of the water table depth are sometimes needed to evaluate the influence of seasonal water stress on Sphagnum recolonization in restored ombrotrophic peatlands. However, continuous water table measurements are often scarce due to high costs and, as a result, water table [...] Read more.
Daily measurements of the water table depth are sometimes needed to evaluate the influence of seasonal water stress on Sphagnum recolonization in restored ombrotrophic peatlands. However, continuous water table measurements are often scarce due to high costs and, as a result, water table depth is more commonly measured manually bimonthly with daily logs in few reference wells. A literature review identified six potential methods to estimate daily water table depth with bimonthly records and daily measurements from a reference well. A new estimation method based on the time series decomposition (TSD) is also presented. TSD and the six identified methods were compared with the water table records of an experimental peatland site with controlled water table regime located in Eastern Canada. The TSD method was the best performing method (R2 = 0.95, RMSE = 2.48 cm and the lowest AIC), followed by the general linear method (R2 = 0.92, RMSE = 3.10 cm) and support vector machines method (R2 = 0.91, RMSE = 3.24 cm). To estimate daily values, the TSD method, like the six traditional methods, requires daily data from a reference well. However, the TSD method does not require training nor parameter estimation. For the TSD method, changing the measurement frequency to weekly measurements decreases the RMSE by 16% (2.08 cm); monthly measurements increase the RMSE by 13% (2.80 cm). Full article
(This article belongs to the Special Issue Advance in Time Series Modelling for Water Resources Management)
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15 pages, 1210 KiB  
Article
Optimizing Water Use Structures in Resource-Based Water-Deficient Regions Using Water Resources Input–Output Analysis: A Case Study in Hebei Province, China
by Yang Wei and Boyang Sun
Sustainability 2021, 13(7), 3939; https://doi.org/10.3390/su13073939 - 2 Apr 2021
Cited by 7 | Viewed by 2843
Abstract
Hebei is a representative province facing the scarcity of water resource in China. China is promoting the coordinated development of Beijing, Tianjin, and Hebei, as well as the establishment of Xiong’an New Area. Hebei Province therefore has to bear the population pressure brought [...] Read more.
Hebei is a representative province facing the scarcity of water resource in China. China is promoting the coordinated development of Beijing, Tianjin, and Hebei, as well as the establishment of Xiong’an New Area. Hebei Province therefore has to bear the population pressure brought by the construction of Xiong’an New Area, while also absorbing the transfer of industries from Beijing and Tianjin. Therefore, its water supply tensions will be further exacerbated. This study constructed an input–output (IO) table utilizing the input and output data of Hebei in 2015 and analyzed the industrial structure and the characteristics of water usage in relevant industries. The research results show that the agricultural sector in Hebei Province consumes the highest water consumption per 10,000 yuan in output value, while the service and transportation industries are the lowest. And a large amount of water used in the agricultural sector is transferred to the manufacturing sector and construction sector in the form of virtual water. The main way to solve the contradiction between water supply and demand in the typical water-deficient areas represented by Hebei Province is to improve water resource utilization efficiency in the short term, and to change the regional water use structure through industrial structure adjustment in the long term. Full article
(This article belongs to the Special Issue Advance in Time Series Modelling for Water Resources Management)
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18 pages, 7886 KiB  
Article
Genetic-Algorithm-Optimized Sequential Model for Water Temperature Prediction
by Stephen Stajkowski, Deepak Kumar, Pijush Samui, Hossein Bonakdari and Bahram Gharabaghi
Sustainability 2020, 12(13), 5374; https://doi.org/10.3390/su12135374 - 2 Jul 2020
Cited by 57 | Viewed by 5243
Abstract
Advances in establishing real-time river water quality monitoring networks combined with novel artificial intelligence techniques for more accurate forecasting is at the forefront of urban water management. The preservation and improvement of the quality of our impaired urban streams are at the core [...] Read more.
Advances in establishing real-time river water quality monitoring networks combined with novel artificial intelligence techniques for more accurate forecasting is at the forefront of urban water management. The preservation and improvement of the quality of our impaired urban streams are at the core of the global challenge of ensuring water sustainability. This work adopted a genetic-algorithm (GA)-optimized long short-term memory (LSTM) technique to predict river water temperature (WT) as a key indicator of the health state of the aquatic habitat, where its modeling is crucial for effective urban water quality management. To our knowledge, this is the first attempt to adopt a GA-LSTM to predict the WT in urban rivers. In recent research trends, large volumes of real-time water quality data, including water temperature, conductivity, pH, and turbidity, are constantly being collected. Specifically, in the field of water quality management, this provides countless opportunities for understanding water quality impairment and forecasting, and to develop models for aquatic habitat assessment purposes. The main objective of this research was to develop a reliable and simple urban river water temperature forecasting tool using advanced machine learning methods that can be used in conjunction with a real-time network of water quality monitoring stations for proactive water quality management. We proposed a hybrid time series regression model for WT forecasting. This hybrid approach was applied to solve problems regarding the time window size and architectural factors (number of units) of the LSTM network. We have chosen an hourly water temperature record collected over 5 years as the input. Furthermore, to check its robustness, a recurrent neural network (RNN) was also tested as a benchmark model and the performances were compared. The experimental results revealed that the hybrid model of the GA-LSTM network outperformed the RNN and the basic problem of determining the optimal time window and number of units of the memory cell was solved. This research concluded that the GA-LSTM can be used as an advanced deep learning technique for time series analysis. Full article
(This article belongs to the Special Issue Advance in Time Series Modelling for Water Resources Management)
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Review

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31 pages, 2322 KiB  
Review
Extending Natural Limits to Address Water Scarcity? The Role of Non-Conventional Water Fluxes in Climate Change Adaptation Capacity: A Review
by Sandra Ricart, Rubén A. Villar-Navascués, Maria Hernández-Hernández, Antonio M. Rico-Amorós, Jorge Olcina-Cantos and Enrique Moltó-Mantero
Sustainability 2021, 13(5), 2473; https://doi.org/10.3390/su13052473 - 25 Feb 2021
Cited by 33 | Viewed by 5063
Abstract
Water consumption continues to grow globally, and it is estimated that more than 160% of the total global water volume will be needed to satisfy the water requirements in ten years. In this context, non-conventional water resources are being considered to overcome water [...] Read more.
Water consumption continues to grow globally, and it is estimated that more than 160% of the total global water volume will be needed to satisfy the water requirements in ten years. In this context, non-conventional water resources are being considered to overcome water scarcity and reduce water conflicts between regions and sectors. A bibliometric analysis and literature review of 81 papers published between 2000 and 2020 focused on south-east Spain were conducted. The aim was to examine and re-think the benefits and concerns, and the inter-connections, of using reclaimed and desalinated water for agricultural and urban-tourist uses to address water scarcity and climate change impacts. Results highlight that: (1) water use, cost, quality, management, and perception are the main topics debated by both reclaimed and desalinated water users; (2) water governance schemes could be improved by including local stakeholders and water users in decision-making; and (3) rainwater is not recognized as a complementary option to increase water supply in semi-arid regions. Furthermore, the strengths–weaknesses–opportunities–threats (SWOT) analysis identifies complementary concerns such as acceptability and investment in reclaimed water, regulation (cost recovery principle), and environmental impacts of desalinated water. Full article
(This article belongs to the Special Issue Advance in Time Series Modelling for Water Resources Management)
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