Topic Editors

Institute of Marine Biological Resources and Inland Waters, Hellenic Centre for Marine Research, 19013 Anavyssos, Greece
Departamento de Engenharia Civil, Instituto Politécnico de Coimbra, Coimbra, Portugal

Application of Smart Technologies in Water Resources Management

Abstract submission deadline
closed (29 December 2023)
Manuscript submission deadline
29 February 2024
Viewed by
14450

Topic Information

Dear Colleagues,

The technological advancements of the last century facilitated the development of efficient, low-cost sensors and automatic measurement platforms that allow water resource monitoring in high spatial and temporal scales. Telecommunication technologies and the development of Internet of Things infrastructure provided the opportunity for near-real-time monitoring of water quantity and quality with early warning and adaptive management capabilities. Measurements of key parameters such as water level, discharge and physicochemical properties are nowadays captured with a variety of smart, low-cost sensors, radar systems, remotely operated aerial or floating vehicles and satellites. This state-of-the-art monitoring infrastructure is expected to provide significant amounts of valuable data in the coming years, supporting policy makers and managers to preserve, manage and restore water resources efficiently. Therefore, the aim of this Special Issue is to present the latest technological applications and methods for water resource monitoring and management that provide near-real-time information and/or high-spatial and temporal resolution data that can optimize the current management practices and minimize potential impacts from water-related disasters.

Dr. Elias Dimitriou
Dr. Joaquim Sousa
Topic Editors

Keywords

  • water vulnerability
  • pollution risk
  • water resources management
  • pollution pressures
  • water quality
  • climate and land use change
  • water bodies restoration

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Energies
energies
3.2 5.5 2008 16.1 Days CHF 2600 Submit
Hydrology
hydrology
3.2 4.1 2014 17.8 Days CHF 1800 Submit
Journal of Marine Science and Engineering
jmse
2.9 3.7 2013 15.4 Days CHF 2600 Submit
Remote Sensing
remotesensing
5.0 7.9 2009 23 Days CHF 2700 Submit
Water
water
3.4 5.5 2009 16.5 Days CHF 2600 Submit

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Published Papers (10 papers)

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15 pages, 4186 KiB  
Brief Report
A Comparative Evaluation of eDNA Metabarcoding Primers in Fish Community Monitoring in the East Lake
Water 2024, 16(5), 631; https://doi.org/10.3390/w16050631 - 21 Feb 2024
Viewed by 233
Abstract
East Lake in Wuhan, China, harbors a high number of freshwater fish species of great conservation value, concurrently serving as vital resources for local livelihoods. However, the ecosystem is threatened by an array of anthropogenic activities, thus requiring consistent monitoring of the local [...] Read more.
East Lake in Wuhan, China, harbors a high number of freshwater fish species of great conservation value, concurrently serving as vital resources for local livelihoods. However, the ecosystem is threatened by an array of anthropogenic activities, thus requiring consistent monitoring of the local fish community to enable more efficacious conservation management. In place of conventional surveying methods, we undertook the first analysis of the fish distribution within East Lake via metabarcoding of environmental DNA (eDNA). The accuracy and efficacy of eDNA metabarcoding rely heavily upon selecting an appropriate primer set for PCR amplification. Given the varying environmental conditions and taxonomic diversity across distinct study systems, it remains a challenge to propose an optimal genetic marker for universal use. Thus, it becomes necessary to select PCR primers suitable for the composition of fish in the East Lake. Here, we evaluated the performance of two primer sets, Mifish-U and Metafish, designed to amplify 12S rRNA barcoding genes in fishes. Our results detected a total of 116 taxonomic units and 51 fish species, with beta diversity analysis indicating significant differences in community structure diversity between the six sampling locations encompassing East Lake. While it was difficult to accurately compare the species-level discriminatory power and amplification bias of the two primers, Mifish outperformed Metafish in terms of taxonomic specificity for fish taxa and reproducibility. These findings will assist with primer selection for eDNA-based fish monitoring and biodiversity conservation in the East Lake and other freshwater ecosystems. Full article
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20 pages, 6169 KiB  
Article
A Combined Model for Water Quality Prediction Based on VMD-TCN-ARIMA Optimized by WSWOA
Water 2023, 15(24), 4227; https://doi.org/10.3390/w15244227 - 08 Dec 2023
Viewed by 777
Abstract
With environmental degradation and water scarcity becoming increasingly serious, it is urgent to carry out effective management of water resources. The key task of water environment monitoring is to conduct statistics and analysis of changes in water quality characteristics. Aiming to address the [...] Read more.
With environmental degradation and water scarcity becoming increasingly serious, it is urgent to carry out effective management of water resources. The key task of water environment monitoring is to conduct statistics and analysis of changes in water quality characteristics. Aiming to address the problem of the strong fluctuation and strong temporal correlation of water quality characteristics prediction, a new framework for water quality prediction based on variational mode decomposition–temporal convolutional networks–autoregressive integrated moving average (VMD-TCN-ARIMA) optimized by weighted swarm the whale search algorithm (WSWOA) algorithm is proposed. First, the WSWOA was proposed by introducing the two-weighted-factor perturbation strategy and the particle swarm search method based on the whale optimization algorithm (WOA), which effectively improves the convergence speed and global search capabilities. Second, to adaptively decompose the original water quality sequences, the VMD algorithm optimized by WSWOA was utilized, which can extract features and reduce noise in the original sequence. Furthermore, the TCN-ARIMA combined model is proposed for time series analysis. The combined model is introduced to assign different algorithms to the decomposed components to reduce prediction error and modeling effort. In comparison to VMD-TCN model, the experimental results have shown that on the data of water quality characteristic dissolved oxygen (DO), the proposed model’s root mean square error (RMSE) and computational time is reduced by 41.05% and 26.06%, further improving the accuracy and efficiency of prediction. Full article
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16 pages, 1504 KiB  
Review
Applications of Smart Water Management Systems: A Literature Review
Water 2023, 15(19), 3492; https://doi.org/10.3390/w15193492 - 06 Oct 2023
Viewed by 2364
Abstract
Issues such as climate change, water scarcity, population growth, and distribution losses have stimulated the use of new technologies to manage water resources. This is how the concept of smart water management emerged as a subcategory of the concept of smart cities. This [...] Read more.
Issues such as climate change, water scarcity, population growth, and distribution losses have stimulated the use of new technologies to manage water resources. This is how the concept of smart water management emerged as a subcategory of the concept of smart cities. This article aimed first to identify the applications of smart water-management systems described in academic articles either as applications in development or as applications already implemented or as future trends; and, second, to classify them according to the processes in the value chain of public water supply services. To this end, a systematic review of the literature was carried out, in which 100 mentions of applications were identified in 62 selected articles; then, the mentions were grouped into 10 categories. The most frequent application categories were smart meters, implementation models and architectures, and loss management. Among the processes of the value chain, applications in processes of distribution and water use were highly predominant. The lack of detail about the integration between the different applications for a smart water-management system was pointed out as a limitation and an opportunity for future research development, especially in terms of a technological roadmap study based on the relationship between smart meters and loss management. Full article
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29 pages, 7802 KiB  
Article
Assessing the Water Status and Leaf Pigment Content of Olive Trees: Evaluating the Potential and Feasibility of Unmanned Aerial Vehicle Multispectral and Thermal Data for Estimation Purposes
Remote Sens. 2023, 15(19), 4777; https://doi.org/10.3390/rs15194777 - 30 Sep 2023
Cited by 2 | Viewed by 805
Abstract
Global warming presents a significant threat to the sustainability of agricultural systems, demanding increased irrigation to mitigate the impacts of prolonged dry seasons. Efficient water management strategies, including deficit irrigation, have thus become essential, requiring continuous crop monitoring. However, conventional monitoring methods are [...] Read more.
Global warming presents a significant threat to the sustainability of agricultural systems, demanding increased irrigation to mitigate the impacts of prolonged dry seasons. Efficient water management strategies, including deficit irrigation, have thus become essential, requiring continuous crop monitoring. However, conventional monitoring methods are laborious and time-consuming. This study investigates the potential of aerial imagery captured by unmanned aerial vehicles (UAVs) to predict critical water stress indicators—relative water content (RWC), midday leaf water potential (ΨMD), stomatal conductance (gs)—as well as the pigment content (chlorophyll ab, chlorophyll a, chlorophyll b and carotenoids) of trees in an olive orchard. Both thermal and spectral vegetation indices are calculated and correlated using linear and exponential regression models. The results reveal that the thermal vegetation indices contrast in estimating the water stress indicators, with the Crop Water Stress Index (CWSI) demonstrating higher precision in predicting the RWC (R2 = 0.80), ΨMD (R2 = 0.61) and gs (R2 = 0.72). Additionally, the Triangular Vegetation Index (TVI) shows superior accuracy in predicting the chlorophyll ab (R2 = 0.64) and chlorophyll a (R2 = 0.61), while the Modified Chlorophyll Absorption in Reflectance Index (MCARI) proves most effective for estimating the chlorophyll b (R2 = 0.52). This study emphasizes the potential of UAV-based multispectral and thermal infrared imagery in precision agriculture, enabling assessments of the water status and pigment content. Moreover, these results highlight the vital importance of this technology in optimising resource allocation and enhancing olive production, critical steps towards sustainable agriculture in the face of global warming. Full article
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16 pages, 1829 KiB  
Article
A Blockchain Approach for Migrating a Cyber-Physical Water Monitoring Solution to a Decentralized Architecture
Water 2023, 15(16), 2874; https://doi.org/10.3390/w15162874 - 09 Aug 2023
Viewed by 901
Abstract
Water is one of the most important resources in our lives, and because of this, the interest in water management systems is growing constantly. A primary concern regarding urban water distribution is how to build robust solutions to facilitate water monitoring flows with [...] Read more.
Water is one of the most important resources in our lives, and because of this, the interest in water management systems is growing constantly. A primary concern regarding urban water distribution is how to build robust solutions to facilitate water monitoring flows with the support of consumer involvement. Crowdsensing solutions contribute to the involvement in social platforms for increased awareness about the importance of water resources based on incentives and rewards. Blockchain is one of the technologies that has become increasingly popular in the last few years. The possibility of using this architecture in such different sectors while integrating emerging concepts, such as crowdsensing, the Internet of Things, serious gaming, and decision support systems, offers a lot of alternatives and approaches for designing modern applications. This paper aims to present how these technologies can be combined in order to migrate the functionalities of a water distribution management system from a centralized architecture to a decentralized one by leveraging blockchain technologies. The proposed application was designed to facilitate incident reporting flows in public water distribution networks. The proposed solution was to migrate the rewarding mechanisms using the Ethereum infrastructure. The novelty of this solution is determined by the introduction of this decentralized approach into the architecture and also by increasing customer interest by offering tradeable rewards and dynamic subscription discounts. This results in a new decentralized architecture that allows for more transparent interactions between the water provider and clients and increases customer engagement to contribute to water reporting flows. Full article
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21 pages, 6972 KiB  
Article
The Combined Power of Double Mass Curves and Bias Correction for the Maximisation of the Accuracy of an Ensemble Satellite-Based Precipitation Estimate Product
Hydrology 2023, 10(7), 154; https://doi.org/10.3390/hydrology10070154 - 22 Jul 2023
Viewed by 1371
Abstract
Precise estimation of the spatial and temporal characteristics of rainfall is essential for producing the reliable catchment response needed for proper management of water resources. However, in most parts of the world, gauged rainfall stations are sparsely distributed and fail to properly capture [...] Read more.
Precise estimation of the spatial and temporal characteristics of rainfall is essential for producing the reliable catchment response needed for proper management of water resources. However, in most parts of the world, gauged rainfall stations are sparsely distributed and fail to properly capture the spatial variability of rainfall. Furthermore, the gauged rainfall data can sometimes be of short length or require validation. Following this, we present a procedure that enhances the trustworthiness of gauged rainfall data and the accuracy of the rainfall estimations of five satellite-based precipitation estimate (SPE) products by validating them using the 1779 gauged rainfall stations across Thailand. The five SPE products considered include CMORPH-BLD; TRMM-3B42; CHIRPS; CHIRPS-PL; and TRMM-3B42RT. Prior to validation, the gauged rainfall dataset was verified using double mass curve (DMC) analysis to eliminate questionable and inconsistent readings. This led to the improvement of the Nash–Sutcliffe Efficiency (NSE) between the station of interest and its surroundings by 13.9% (0.758–0.863), together with an average 11.8% increase with SPE products, whilst dropping only 7% of questionable dataset. Three different bias correction (BC) procedures were applied to correct SPE products using gauge-based gridded rainfall (GGR). Once DMC and BC procedures were implemented together, the performance of the SPE products was found to increase significantly. Finally, the application of the ensemble weighted average of the three best-performing bias-corrected SPE products (Bias-CMORPH-BLD, Bias-TRMM-3B42, and Bias-CHIRPS) further enhanced the NSE to 0.907 and 0.880 in calibration and validation time periods, respectively. The proposed DMC-based correction SPE and the weighting procedure of multiple SPE products allows for an easy means of obtaining daily rainfall in remote locations with sufficient accuracy. Full article
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21 pages, 723 KiB  
Article
Financial Analysis of a Desalination–Wastewater Recycle Plant Powered by a DC-DC Photovoltaic-Batteries System on the Aeolian Islands, Italy
by and
Energies 2023, 16(13), 4935; https://doi.org/10.3390/en16134935 - 25 Jun 2023
Viewed by 864
Abstract
The scarcity of drinking water is an increasingly pressing issue in many regions of the world, even in areas up till now considered developed. Climate change deprives many populations of the amount of water necessary for human consumption and traditional crops. Therefore, finding [...] Read more.
The scarcity of drinking water is an increasingly pressing issue in many regions of the world, even in areas up till now considered developed. Climate change deprives many populations of the amount of water necessary for human consumption and traditional crops. Therefore, finding new water sources and making their usage more efficient and able to adapt to new environmental conditions without worsening the situation with further pollution is becoming mandatory. In the case study considered here, set on the Italian island of Vulcano in the Central Mediterranean, we propose the economic analysis and financial sustainability of plants for the desalination and recycled wastewater, powered by a DC-DC photovoltaic tracker system with silicon crystal panels sustained by a daily pack of lithium batteries. We present an estimation of the necessary budget, and propose a mix of traditional and innovative financial instruments to construct and analyse the economic trends of the installations over 30 years, considering the specific area price levels, salaries and interest rates. Finally, through the net present value index, we evaluate the financial sustainability of the entire operation, namely, identifying the circumstances when funding and building these plants in areas as remote as the one considered here is cost-effective. Full article
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27 pages, 3954 KiB  
Article
Spatiotemporal Variability in Total Dissolved Solids and Total Suspended Solids along the Colorado River
Hydrology 2023, 10(6), 125; https://doi.org/10.3390/hydrology10060125 - 02 Jun 2023
Cited by 4 | Viewed by 1994
Abstract
The Colorado River is a principal source of water for 40 million people and farmlands in seven states in the western US and the Republic of Mexico. The river has been under intense pressure from the effects of climate change and anthropogenic activities [...] Read more.
The Colorado River is a principal source of water for 40 million people and farmlands in seven states in the western US and the Republic of Mexico. The river has been under intense pressure from the effects of climate change and anthropogenic activities associated with population growth leading to elevated total dissolved solid (TDS) and total suspended solid (TSS) concentrations. Elevated TDS- and TSS-related issues in the basin have a direct negative impact on the water usage and the ecological health of aquatic organisms. This study, therefore, analyzed the spatiotemporal variability in the TDS and TSS concentrations along the river. Results from our analysis show that TDS concentration was significantly higher in the Upper Colorado River Basin while the Lower Colorado River Basin shows a generally high level of TSSs. We found that the activities in these two basins are distinctive and may be a factor in these variations. Results from the Kruskal–Wallis significance test show there are statistically significant differences in TDSs and TSSs from month to month, season to season, and year to year. These significant variations are largely due to seasonal rises in consumptive use, agriculture practices, snowmelts runoffs, and evaporate rates exacerbated by increased temperature in the summer months. The findings from this study will aid in understanding the river’s water quality, detecting the sources and hotspots of pollutions to the river, and guiding legislative actions. The knowledge obtained forms a strong basis for management and conservation efforts and consequently helps to reduce the economic damage caused by these water quality parameters including the over USD 300 million associated with TDS damages. Full article
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23 pages, 3687 KiB  
Article
Predicting Optical Water Quality Indicators from Remote Sensing Using Machine Learning Algorithms in Tropical Highlands of Ethiopia
Hydrology 2023, 10(5), 110; https://doi.org/10.3390/hydrology10050110 - 11 May 2023
Cited by 10 | Viewed by 2370
Abstract
Water quality degradation of freshwater bodies is a concern worldwide, particularly in Africa, where data are scarce and standard water quality monitoring is expensive. This study explored the use of remote sensing imagery and machine learning (ML) algorithms as an alternative to standard [...] Read more.
Water quality degradation of freshwater bodies is a concern worldwide, particularly in Africa, where data are scarce and standard water quality monitoring is expensive. This study explored the use of remote sensing imagery and machine learning (ML) algorithms as an alternative to standard field measuring for monitoring water quality in large and remote areas constrained by logistics and finance. Six machine learning (ML) algorithms integrated with Landsat 8 imagery were evaluated for their accuracy in predicting three optically active water quality indicators observed monthly in the period from August 2016 to April 2022: turbidity (TUR), total dissolved solids (TDS) and Chlorophyll a (Chl-a). The six ML algorithms studied were the artificial neural network (ANN), support vector machine regression (SVM), random forest regression (RF), XGBoost regression (XGB), AdaBoost regression (AB), and gradient boosting regression (GB) algorithms. XGB performed best at predicting Chl-a, with an R2 of 0.78, Nash–Sutcliffe efficiency (NSE) of 0.78, mean absolute relative error (MARE) of 0.082 and root mean squared error (RMSE) of 9.79 µg/L. RF performed best at predicting TDS (with an R2 of 0.79, NSE of 0.80, MARE of 0.082, and RMSE of 12.30 mg/L) and TUR (with an R2 of 0.80, NSE of 0.81, and MARE of 0.072 and RMSE of 7.82 NTU). The main challenges were data size, sampling frequency, and sampling resolution. To overcome the data limitation, we used a K-fold cross validation technique that could obtain the most out of the limited data to build a robust model. Furthermore, we also employed stratified sampling techniques to improve the ML modeling for turbidity. Thus, this study shows the possibility of monitoring water quality in large freshwater bodies with limited observed data using remote sensing integrated with ML algorithms, potentially enhancing decision making. Full article
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16 pages, 6035 KiB  
Article
Providencia alcalifaciens—Assisted Bioremediation of Chromium-Contaminated Groundwater: A Computational Study
Water 2023, 15(6), 1142; https://doi.org/10.3390/w15061142 - 15 Mar 2023
Cited by 2 | Viewed by 1527
Abstract
In Saudi Arabia, seawater desalination is the primary source of acquiring freshwater, and groundwater contains a high concentration of toxic heavy metals. Chromium (Cr) is one of the heavy metals that is widely distributed in the environment, particularly in the groundwater of Madinah. [...] Read more.
In Saudi Arabia, seawater desalination is the primary source of acquiring freshwater, and groundwater contains a high concentration of toxic heavy metals. Chromium (Cr) is one of the heavy metals that is widely distributed in the environment, particularly in the groundwater of Madinah. Diverse techniques are employed to eliminate the toxicity of heavy metals from the environment, but, lately, the focus has shifted to biological remediation systems, due to their higher removal efficiencies, lower costs, and more ecologically benign characteristics than the conventional methods. Providencia bacteria engage in a variety of adsorption processes to interact with heavy metals. In this study, we aim to investigate the role of potential active site residues in the bioengineering of chromate reductase (ChrR) from Providencia alcalifaciens to reduce the Cr to a lesser toxic form by employing robust computational approaches. This study highlights Cr bioremediation by providing high-quality homology-modeled structures of wild type and mutants and key residues of ChrR for bioengineering to reduce the Cr toxicity in the environment. Glu79 is found to be a key residue for Cr binding. The mutant models of Arg82Cys, Gln126Trp, and Glu144Trp are observed to establish more metallic interactions within the binding pocket of ChrR. In addition, the wild type ChrR (P. alcalifaciens) has been found to be unstable. However, the mutations stabilized the structure by preserving the metallic contacts between the critical amino acid residues of the identified motifs and the Cr(VI). Therefore, the mutants discovered in the study can be taken into account for protein engineering to create reliable and effective enzymes to convert Cr(VI) into a lesser toxic form. Full article
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