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Sustainability
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Water Resources and Irrigation Management for Sustainable Agroecosystems
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Water Resources and Irrigation Management for Sustainable Agroecosystems

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

Deadline for manuscript submissions: 30 April 2025 | Viewed by 1333

Special Issue Editor

Prof. Dr. Suren Kulshreshtha

E-Mail Website
Guest Editor
Department of Agricultural and Resource Economics, University of Saskatchewan, Saskatoon, SK S7N 5A8, Canada
Interests: water resource economics; climate change; agro-forestry; economic impact assessment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The overall objective of this Special Issue is to trace the role of water resources’ use and misuse and water conservation in ecosystem sustainability. Water is a very precious resource in many parts of the world. However, it is also misused in other parts of the world. This Special Issue will hopefully identify such situations and develop solutions. One of these situations of misuse can be found in irrigation water use. However, such situations do occur wherever water is not priced.

The scope of this Special Issue is open but limited to water resources and irrigation water use and misuse. Manuscripts must show a connection with ecosystem sustainability. Papers on other types of other water users are also welcome.

This issue of the journal will be highly relevant as it will highlight the connection between ecosystem sustainability and water resources. These studies might lead to better policy formulation in the future for regulation of water resources. 

Water resources, including the use of water for irrigation of crops, have been widely studied, but their connection to sustainability and, in particular, ecosystem sustainability has not received the same attention. Studies in this issue of the journal will not only demonstrate the importance of water management to ecosystem sustainability but also develop measures to improve it.

Prof. Dr. Suren Kulshreshtha
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. 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

  • holistic approach to water management
  • irrigation water management
  • other water management
  • water management under scarcity
  • interrelationships between water management and ecosystem sustainability

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Published Papers (1 paper)

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Research

18 pages, 15128 KiB  
Article
Research on a Non-Stationary Groundwater Level Prediction Model Based on VMD-iTransformer and Its Application in Sustainable Water Resource Management of Ecological Reserves
by Hexiang Zheng, Hongfei Hou and Ziyuan Qin
Sustainability 2024, 16(21), 9185; https://doi.org/10.3390/su16219185 - 23 Oct 2024
Viewed by 831
Abstract
The precise forecasting of groundwater levels significantly influences plant growth and the sustainable management of ecosystems. Nonetheless, the non-stationary characteristics of groundwater level data often hinder the current deep learning algorithms from precisely capturing variations in groundwater levels. We used Variational Mode Decomposition [...] Read more.
The precise forecasting of groundwater levels significantly influences plant growth and the sustainable management of ecosystems. Nonetheless, the non-stationary characteristics of groundwater level data often hinder the current deep learning algorithms from precisely capturing variations in groundwater levels. We used Variational Mode Decomposition (VMD) and an enhanced Transformer model to address this issue. Our objective was to develop a deep learning model called VMD-iTransformer, which aims to forecast variations in the groundwater level. This research used nine groundwater level monitoring stations located in Hangjinqi Ecological Reserve in Kubuqi Desert, China, as case studies to forecast the groundwater level over four months. To enhance the predictive performance of VMD-iTransformer, we introduced a novel approach to model the fluctuations in groundwater levels in the Kubuqi Desert region. This technique aims to achieve precise predictions of the non-stationary groundwater level conditions. Compared with the classic Transformer model, our deep learning model more effectively captured the non-stationarity of groundwater level variations and enhanced the prediction accuracy by 70% in the test set. The novelty of this deep learning model lies in its initial decomposition of multimodal signals using an adaptive approach, followed by the reconfiguration of the conventional Transformer model’s structure (via self-attention and inversion of a feed-forward neural network (FNN)) to effectively address the challenge of multivariate time prediction. Through the evaluation of the prediction results, we determined that the method had a mean absolute error (MAE) of 0.0251, a root mean square error (RMSE) of 0.0262, a mean absolute percentage error (MAPE) of 1.2811%, and a coefficient of determination (R2) of 0.9287. This study validated VMD and the iTransformer deep learning model, offering a novel modeling approach for precisely predicting fluctuations in groundwater levels in a non-stationary context, thereby aiding sustainable water resource management in ecological reserves. The VMD-iTransformer model enhances projections of the water level, facilitating the reasonable distribution of water resources and the long-term preservation of ecosystems, providing technical assistance for ecosystems’ vitality and sustainable regional development. Full article
(This article belongs to the Special Issue Water Resources and Irrigation Management for Sustainable Agroecosystems)
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