Application of Smart Technologies in Water Resources Management

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

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 (13 papers)

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All Energies Hydrology JMSE Remote Sensing Water

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26 pages, 9512 KiB  

Open AccessArticle

Post-Analysis of Daniel Extreme Flood Event in Thessaly, Central Greece: Practical Lessons and the Value of State-of-the-Art Water-Monitoring Networks

by Elias Dimitriou, Andreas Efstratiadis, Ioanna Zotou, Anastasios Papadopoulos, Theano Iliopoulou, Georgia-Konstantina Sakki, Katerina Mazi, Evangelos Rozos, Antonios Koukouvinos, Antonis D. Koussis, Nikos Mamassis and Demetris Koutsoyiannis

Water 2024, 16(7), 980; https://doi.org/10.3390/w16070980 - 28 Mar 2024

Viewed by 1027

Abstract

Storm Daniel initiated on 3 September 2023, over the Northeastern Aegean Sea, causing extreme rainfall levels for the following four days, reaching an average of about 360 mm over the Peneus basin, in Thessaly, Central Greece. This event led to extensive floods, with [...] Read more.

Storm Daniel initiated on 3 September 2023, over the Northeastern Aegean Sea, causing extreme rainfall levels for the following four days, reaching an average of about 360 mm over the Peneus basin, in Thessaly, Central Greece. This event led to extensive floods, with 17 human lives lost and devastating environmental and economic impacts. The automatic water-monitoring network of the HIMIOFoTS National Research Infrastructure captured the evolution of the phenomenon and the relevant hydrometeorological (rainfall, water stage, and discharge) measurements were used to analyse the event’s characteristics. The results indicate that the average rainfall’s return period was up to 150 years, the peak flow close to the river mouth reached approximately 1950 m³/s, and the outflow volume of water to the sea was 1670 hm³. The analysis of the observed hydrographs across Peneus also provided useful lessons from the flood-engineering perspective regarding key modelling assumptions and the role of upstream retentions. Therefore, extending and supporting the operation of the HIMIOFoTS infrastructure is crucial to assist responsible authorities and local communities in reducing potential damages and increasing the socioeconomic resilience to natural disasters, as well as to improve the existing knowledge with respect to extreme flood-simulation approaches. Full article

(This article belongs to the Topic Application of Smart Technologies in Water Resources Management)

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19 pages, 3737 KiB  

Open AccessArticle

Machine Learning Models for Water Quality Prediction: A Comprehensive Analysis and Uncertainty Assessment in Mirpurkhas, Sindh, Pakistan

by Farkhanda Abbas, Zhihua Cai, Muhammad Shoaib, Javed Iqbal, Muhammad Ismail, Arifullah, Abdulwahed Fahad Alrefaei and Mohammed Fahad Albeshr

Water 2024, 16(7), 941; https://doi.org/10.3390/w16070941 - 25 Mar 2024

Viewed by 820

Abstract

Groundwater represents a pivotal asset in conserving natural water reservoirs for potable consumption, irrigation, and diverse industrial uses. Nevertheless, human activities intertwined with industry and agriculture contribute significantly to groundwater contamination, highlighting the critical necessity of appraising water quality for safe drinking and [...] Read more.

Groundwater represents a pivotal asset in conserving natural water reservoirs for potable consumption, irrigation, and diverse industrial uses. Nevertheless, human activities intertwined with industry and agriculture contribute significantly to groundwater contamination, highlighting the critical necessity of appraising water quality for safe drinking and effective irrigation. This research primarily focused on employing the Water Quality Index (WQI) to gauge water’s appropriateness for these purposes. However, the generation of an accurate WQI can prove time-intensive owing to potential errors in sub-index calculations. In response to this challenge, an artificial intelligence (AI) forecasting model was devised, aiming to streamline the process while mitigating errors. The study collected 422 data samples from Mirpurkash, a city nestled in the province of Sindh, for a comprehensive exploration of the region’s WQI attributes. Furthermore, the study probed into unraveling the interdependencies amidst variables in the physiochemical analysis of water. Diverse machine learning classifiers were employed for WQI prediction, with findings revealing that Random Forest and Gradient Boosting lead with 95% and 96% accuracy, followed closely by SVM at 92%. KNN exhibits an accuracy rate of 84%, and Decision Trees achieve 77%. Traditional water quality assessment methods are time-consuming and error-prone; a transformative approach using artificial intelligence and machine learning addresses these limitations. In addition to WQI prediction, the study conducted an uncertainty analysis of the models using the R-factor, providing insights into the reliability and consistency of predictions. This dual approach, combining accurate WQI prediction with uncertainty assessment, contributes to a more comprehensive understanding of water quality in Mirpurkash and enhances the reliability of decision-making processes related to groundwater utilization. Full article

(This article belongs to the Topic Application of Smart Technologies in Water Resources Management)

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22 pages, 6542 KiB  

Open AccessArticle

Development and Application of Technical Key Performance Indicators (KPIs) for Smart Water Cities (SWCs) Global Standards and Certification Schemes

by Lea Dasallas, Junghwan Lee, Sungphil Jang and Suhyung Jang

Water 2024, 16(5), 741; https://doi.org/10.3390/w16050741 - 29 Feb 2024

Viewed by 697

Abstract

Smart water cities (SWCs) use advanced technologies for efficient management and preservation of the urban water cycle, strengthening sustainability and improving the quality of life of the residents. This research aims to develop measurement and evaluation tools for SWC key performance indicators (KPIs), [...] Read more.

Smart water cities (SWCs) use advanced technologies for efficient management and preservation of the urban water cycle, strengthening sustainability and improving the quality of life of the residents. This research aims to develop measurement and evaluation tools for SWC key performance indicators (KPIs), focusing on innovative water technologies in establishing unified global standards and certification schemes. The KPIs are categorized based on the stage at which water is being measured, namely the urban water cycle, water disaster management and water supply and treatment. The objective is to assess cities’ use of technologies in providing sufficient water supply, monitoring water quality, strengthening disaster resilience and maintaining and preserving the urban water ecosystem. The assessment is composed of a variety of procedures performed in a quantitative and qualitative manner, the details of which are presented in this study. The developed SWC KPI measurements are used to evaluate the urban water management practices for Busan Eco Delta City, located in Busan, South Korea. Evaluation processes were presented and established, serving as the guideline basis for certification in analyzing future cities, providing integrated and comprehensive information on the status of their urban water system, gathering new techniques, and proposing solutions for smarter measures. Full article

(This article belongs to the Topic Application of Smart Technologies in Water Resources Management)

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15 pages, 4186 KiB  

Open AccessBrief Report

A Comparative Evaluation of eDNA Metabarcoding Primers in Fish Community Monitoring in the East Lake

by Yiwen Li, Minzhe Tang, Suxiang Lu, Xiaochun Zhang, Chengchi Fang, Li Tan, Fan Xiong, Honghui Zeng and Shunping He

Water 2024, 16(5), 631; https://doi.org/10.3390/w16050631 - 21 Feb 2024

Viewed by 1387

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

(This article belongs to the Topic Application of Smart Technologies in Water Resources Management)

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20 pages, 6169 KiB  

Open AccessArticle

A Combined Model for Water Quality Prediction Based on VMD-TCN-ARIMA Optimized by WSWOA

by Hongyu Zuo, Xiantai Gou, Xin Wang and Mengyin Zhang

Water 2023, 15(24), 4227; https://doi.org/10.3390/w15244227 - 08 Dec 2023

Viewed by 928

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

(This article belongs to the Topic Application of Smart Technologies in Water Resources Management)

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16 pages, 1504 KiB  

Open AccessReview

Applications of Smart Water Management Systems: A Literature Review

by Érico Soares Ascenção, Fernando Melo Marinangelo, Carlos Frederico Meschini Almeida, Nelson Kagan and Eduardo Mário Dias

Water 2023, 15(19), 3492; https://doi.org/10.3390/w15193492 - 06 Oct 2023

Viewed by 3638

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

(This article belongs to the Topic Application of Smart Technologies in Water Resources Management)

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29 pages, 7802 KiB  

Open AccessArticle

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

by Pedro Marques, Luís Pádua, Joaquim J. Sousa and Anabela Fernandes-Silva

Remote Sens. 2023, 15(19), 4777; https://doi.org/10.3390/rs15194777 - 30 Sep 2023

Cited by 3 | Viewed by 1000

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 (Ψ_MD), stomatal conductance (g_s)—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), Ψ_MD (R² = 0.61) and g_s (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

(This article belongs to the Topic Application of Smart Technologies in Water Resources Management)

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16 pages, 1829 KiB  

Open AccessArticle

A Blockchain Approach for Migrating a Cyber-Physical Water Monitoring Solution to a Decentralized Architecture

by Bogdan-Ionut Pahontu, Adrian Petcu, Alexandru Predescu, Diana Andreea Arsene and Mariana Mocanu

Water 2023, 15(16), 2874; https://doi.org/10.3390/w15162874 - 09 Aug 2023

Viewed by 1043

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

(This article belongs to the Topic Application of Smart Technologies in Water Resources Management)

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21 pages, 6972 KiB  

Open AccessArticle

The Combined Power of Double Mass Curves and Bias Correction for the Maximisation of the Accuracy of an Ensemble Satellite-Based Precipitation Estimate Product

by Nutchanart Sriwongsitanon, Chanphit Kaprom, Kamonpat Tantisuvanichkul, Nattakorn Prasertthonggorn, Watchara Suiadee, Wim G. M. Bastiaanssen and James Alexander Williams

Hydrology 2023, 10(7), 154; https://doi.org/10.3390/hydrology10070154 - 22 Jul 2023

Viewed by 1778

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

(This article belongs to the Topic Application of Smart Technologies in Water Resources Management)

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21 pages, 723 KiB  

Open AccessArticle

Financial Analysis of a Desalination–Wastewater Recycle Plant Powered by a DC-DC Photovoltaic-Batteries System on the Aeolian Islands, Italy

by Gabriele Mosconi and Maurizio F. Acciarri

Energies 2023, 16(13), 4935; https://doi.org/10.3390/en16134935 - 25 Jun 2023

Viewed by 1046

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

(This article belongs to the Topic Application of Smart Technologies in Water Resources Management)

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27 pages, 3954 KiB  

Open AccessFeature PaperArticle

Spatiotemporal Variability in Total Dissolved Solids and Total Suspended Solids along the Colorado River

by Godson Ebenezer Adjovu, Haroon Stephen and Sajjad Ahmad

Hydrology 2023, 10(6), 125; https://doi.org/10.3390/hydrology10060125 - 02 Jun 2023

Cited by 6 | Viewed by 2342

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

(This article belongs to the Topic Application of Smart Technologies in Water Resources Management)

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23 pages, 3687 KiB  

Open AccessArticle

Predicting Optical Water Quality Indicators from Remote Sensing Using Machine Learning Algorithms in Tropical Highlands of Ethiopia

by Elias S. Leggesse, Fasikaw A. Zimale, Dagnenet Sultan, Temesgen Enku, Raghavan Srinivasan and Seifu A. Tilahun

Hydrology 2023, 10(5), 110; https://doi.org/10.3390/hydrology10050110 - 11 May 2023

Cited by 12 | Viewed by 2743

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

(This article belongs to the Topic Application of Smart Technologies in Water Resources Management)

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16 pages, 6035 KiB  

Open AccessArticle

Providencia alcalifaciens—Assisted Bioremediation of Chromium-Contaminated Groundwater: A Computational Study

by Munazzah Tasleem, Wesam M. Hussein, Abdel-Aziz A. A. El-Sayed and Abdulwahed Alrehaily

Water 2023, 15(6), 1142; https://doi.org/10.3390/w15061142 - 15 Mar 2023

Cited by 4 | Viewed by 1701

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

(This article belongs to the Topic Application of Smart Technologies in Water Resources Management)

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