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

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 30028

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Special Issue Editors

Prof. Dr. Salim Heddam

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Guest Editor

Faculty of Science, Agronomy Department, Hydraulics Division, Laboratory of Research in Biodiversity Interaction Ecosystem and Biotechnology, University 20 Août 1955, Route El Hadaik, BP 26, Skikda 21000, Algeria
Interests: machine learning; modelling using artificial intelligence technique; reservoir water quality modeling; application of statistical methods in water and environmental resources; environmental and water quality; dams reservoir operation; hydrology; environmental monitoring; ecology and pollution research; evaporation and evapotranspiration; modelling water quality; river water temperature and dissolved oxygen modelling and forecasting; river flow modelling and forecasting, reservoir water operation; modelling hydro-climatic variables; hydro-Informatics; deep learning; hydrometeorology
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Sungwon Kim

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Guest Editor

Department of Railroad Construction and Safety Engineering, Dongyang University, Yeongju 36040, Republic of Korea
Interests: hydrology; water resources; machine learning; data-driven modeling; hydrometeorology; climate change
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This special issue deals with the challenges for sustainable management of water resource and environmental monitoring issues and climate change problems particular focus semi-arid and arid regions. Furthermore, water resource and environmental parameters affects all levels of the human, climate and natural resources: from sustainable planning, development and policy, to management and withdrawing and monitoring. Remote sensing, GIS, Hydrological modeling and Machine Learning models adapted to sustainability management of water resource is a modern and promising research area. Interest is rising in adopting a sustainable planning and management perspective in these fields. This Special Issue welcomes research and review papers on introduce the various advanced techniques, modeling, climate change, Land Use/ Land Cover, Hydrological models and machine learning approach to sustainable planning and management of water resources with applications applied to solve the water resource and environmental monitoring, water and flood planning and management. Papers selected for this Special Issue will be subject to a rigorous peer review procedure with the aim of rapid and wide dissemination of research results, developments, and applications. The main purpose of this special issue will develop better outlines for the sustainable management of water resource and environmental monitoring.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

Modelling water quality using artificial intelligence technique
reservoir water quality modeling
application of statistical methods in water and environmental resources; environmental and water quality
dams reservoir operation
environmental monitoring
ecology and pollution research
modelling water quality
river water temperature and dissolved oxygen modelling and forecasting; evaporation and evapotranspiration
river flow modelling and forecasting, reservoir water operation; modelling hydro-climatic variables
Hydro-Informatics; Ecohydraulic modeling
sediment transport
assessment of changes in land use and land cover classes and their impacts on the water resources and environmental monitoring in the semi-arid and arid areas
climate change impact on the water resources and environmental monitoring changes with glacier mapping and increased sea water level on the earth surface, surface runoff modelling and forecasting using machine learning models and hydrological Modflow models
hydrological modelling for sustainable water resources
climate change impact on agriculture water management and groundwater level in semi-arid and arid regions
soil erosion and sediments identification and predication using machines learning models and satellite data
forecasting of drought and rainfall with other environmental factors for water resources management and development in the arid and semi-arid areas
planning and management of irrigation water requirement and estimation of ET₀ for water resources planning
flood assessment and susceptibility modelling based on the Google earth engine and remote sensing technology
environmental parameters monitoring and relationship with sustainable water resources planning

We look forward to receiving your contributions.

Prof. Dr. Salim Heddam
Prof. Dr. Sungwon KIM
Guest Editors

Manuscript Submission Information

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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

hydrology and ecohydraulic
wastewater, water treatment and water resources planning and management
water pollution
environmental monitoring
surface water quality
groundwater quantity and quality
groundwater hydrology
modelling of water resource
forecasting of water quality variables
dams and reservoirs operations

Published Papers (11 papers)

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Research

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22 pages, 3198 KiB

Open AccessFeature PaperArticle

Assessment of Bioaccumulation of Heavy Metals and Their Ecological Risk in Sea Lettuce (Ulva spp.) along the Coast Alexandria, Egypt: Implications for Sustainable Management

by Mohammed E. El-Mahrouk, Yaser H. Dewir, Yaser M. Hafez, Antar El-Banna, Farahat S. Moghanm, Hassan El-Ramady, Qaisar Mahmood, Fathy Elbehiry and Eric C. Brevik

Sustainability 2023, 15(5), 4404; https://doi.org/10.3390/su15054404 - 1 Mar 2023

Cited by 2 | Viewed by 1983

Abstract

The pollution of aquatic ecosystems is an issue facing many countries all over the world and may result in issues such as eutrophication in coastal zones. Managing this eutrophication is a real challenge. The current study focuses on the investigation and identification of aquatic environmental characteristics, including the sediments, waters, and seaweed, of seven eutrophicated locations along the Mediterranean coast of Alexandria (Egypt). Different ecological risk assessment and bioaccumulation factors were calculated in order to identify the probable pollution source and the degree of the problem, in addition to the accumulation of heavy metals in the seaweed. The characteristics of the seaweed, sediments, and waters were chemically analyzed and heavy metals were measured. The genetically and biochemically identified seaweed species were Ulva compressa, Ulva fasciata, Ulva lactuca and Ulva linzea. The sediments of the El-Tabia location contained the highest concentrations of Cd, Co, Ni, and Pb, because this location receives these elements from the El-Amia drain. The Abu Qir location was found to contain the highest concentrations of the same heavy metals in the studied water samples because it was located much closer to the Abu Qir harbor. Ecological risk assessment indices indicated moderate to high contamination for most of the studied elements and locations. The results of the bioaccumulation factor analysis indicated that the studied seaweed species are accumulators of trace elements. These seaweed species should be further investigated concerning ecotoxicology if they are to be used in the human diet and for other benefits. This study opens many windows of research to be investigated in the future regarding the sustainable management of polluted coastal zones. Full article

(This article belongs to the Special Issue Sustainable Management of Water Resource and Environmental Monitoring)

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16 pages, 2440 KiB

Open AccessArticle

Investigation of Irrigation Water Requirement and Evapotranspiration for Water Resource Management in Southern Punjab, Pakistan

by Sajjad Hussain, Muhammad Mubeen, Wajid Nasim, Shah Fahad, Musaddiq Ali, Muhammad Azhar Ehsan and Ali Raza

Sustainability 2023, 15(3), 1768; https://doi.org/10.3390/su15031768 - 17 Jan 2023

Cited by 14 | Viewed by 2382

Abstract

Water scarcity and water quality degradation are exacerbated by climate change in all countries, including Pakistan. The use of water in agriculture is one of the most predominant resources, so reducing consumption and improving resource management is of utmost importance. In the past few decades, excessive irrigation has led to severe water scarcity and reduced water quality. This study determined the irrigation requirements for cotton, rice, and wheat, using the CROPWAT model in Southern Punjab (Multan District). In the study area, evapotranspiration ranged from 1.8 to 10.24 mm/day, while effective rainfall ranged from 2 to 31.3 mm. Rice, cotton, and wheat each required 996.4, 623.3, and 209.5 mm of irrigation, respectively. Among rice, cotton, and wheat, the total net irrigation was 72.4, 67.8, and 44.1 mm, respectively, while the total gross irrigation was 103.5, 99.8, and 63 mm. The CROPWAT model showed a moderately useful result for identifying irrigation needs in Southern Punjab. The study emphasizes the need for groundwater harvesting and water management technologies to implement a water management system that reduces water shortages. Full article

(This article belongs to the Special Issue Sustainable Management of Water Resource and Environmental Monitoring)

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28 pages, 6603 KiB

Open AccessArticle

Chemometrics of the Environment: Hydrochemical Characterization of Groundwater in Lioua Plain (North Africa) Using Time Series and Multivariate Statistical Analysis

by Ali Athamena, Aissam Gaagai, Hani Amir Aouissi, Juris Burlakovs, Selma Bencedira, Ivar Zekker and Andrey E. Krauklis

Sustainability 2023, 15(1), 20; https://doi.org/10.3390/su15010020 - 20 Dec 2022

Cited by 12 | Viewed by 2066

Abstract

This study aims to analyze the chemical composition of Lioua’s groundwater in order to determine the geological processes influencing the composition and origin of its chemical elements. Therefore, chemometrics techniques, such as multivariate statistical analysis (MSA) and time series methods (TSM) are used. Indeed, MSA includes a component analysis (PCA) and a cluster analysis (CA), while autocorrelation analysis (AA), supplemented by a simple spectral density analysis (SDA), is used for the TMS. PCA displays three main factors explaining a total variance (TV) of 85.01 %. Factors 1, 2, and 3 are 68.72%, 11.96%, and 8.89 % of TV, respectively. In the CA, total dissolved solids (TDS) and electrical conductivity (EC) controlled three groups. The elements SO₄²⁻, K⁺, and Ca²⁺ are closely related to TDS, the elements Na⁺, Cl⁻, and Mg²⁺ are closely related to CE, while HCO³⁻ and NO³⁻ indicate the dissociation of other chemical elements. AA shows a linear interrelationship of EC, Mg²⁺, Na⁺, K⁺, Cl⁻, and SO₄²⁻. However, NO₃⁻ and HCO₃⁻ indicate uncorrelated characteristics with other parameters. For SDA, the correlograms of Mg²⁺, Na⁺, K⁺, Cl⁻, and SO₄²⁻ have a similar trend with EC. Nonetheless, pH, Ca²⁺, HCO₃⁻ and NO₃⁻ exhibit multiple peaks related to the presence of several distinct cyclic mechanisms. Using these techniques, the authors were able to draw the following conclusion: the geochemical processes impacting the chemical composition are (i) dissolution of evaporated mineral deposits, (ii) water–rock interaction, and (iii) evaporation process. In addition, the groundwater exhibits two bipolar characteristics, one recorded with negative and positive charges on pH and Ca⁺ and another recorded only with negative charges on HCO₃⁻ and NO₃⁻. On the other hand, SO₄²⁻, K⁺, Ca²⁺, and TDS are the major predominant elements in the groundwater’s chemical composition. Chloride presence mainly increases the electrical conductivity of water. The lithological factor is dominant in the overall mineralization of the Plio Quaternary surface aquifer waters. The origins of HCO₃⁻ and NO₃⁻ are as follows: HCO₃⁻ has a carbonate origin, whereas NO₃^– has an anthropogenic origin. The salinity was affected by Mg²⁺, SO₄²⁻, Cl⁻, Na⁺, K⁺, and EC. Ca²⁺, HCO₃⁻, and NO₃⁻ result from human activity such as the usage of fertilizers, the carbonate facies outcrops, and domestic sewage. Full article

(This article belongs to the Special Issue Sustainable Management of Water Resource and Environmental Monitoring)

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17 pages, 1769 KiB

Open AccessArticle

Classification of Cotton Genotypes with Mixed Continuous and Categorical Variables: Application of Machine Learning Models

by Sudha Bishnoi, Nadhir Al-Ansari, Mujahid Khan, Salim Heddam and Anurag Malik

Sustainability 2022, 14(20), 13685; https://doi.org/10.3390/su142013685 - 21 Oct 2022

Cited by 2 | Viewed by 1445

Abstract

Mixed data is a combination of continuous and categorical variables and occurs frequently in fields such as agriculture, remote sensing, biology, medical science, marketing, etc., but only limited work has been done with this type of data. In this study, data on continuous and categorical characters of 452 genotypes of cotton (Gossypium hirsutum) were obtained from an experiment conducted by the Central Institute of Cotton Research (CICR), Sirsa, Haryana (India) during the Kharif season of the year 2018–2019. The machine learning (ML) classifiers/models, namely k-nearest neighbor (KNN), Classification and Regression Tree (CART), C4.5, Naïve Bayes, random forest (RF), bagging, and boosting were considered for cotton genotypes classification. The performance of these ML classifiers was compared to each other along with the linear discriminant analysis (LDA) and logistic regression. The holdout method was used for cross-validation with an 80:20 ratio of training and testing data. The results of the appraisal based on hold-out cross-validation showed that the RF and AdaBoost performed very well, having only two misclassifications with the same accuracy of 97.26% and the error rate of 2.74%. The LDA classifier performed the worst in terms of accuracy, with nine misclassifications. The other performance measures, namely sensitivity, specificity, precision, F1 score, and G-mean, were all together used to find out the best ML classifier among all those considered. Moreover, the RF and AdaBoost algorithms had the highest value of all the performance measures, with 96.97% sensitivity and 97.50% specificity. Thus, these models were found to be the best in classifying the low- and high-yielding cotton genotypes. Full article

(This article belongs to the Special Issue Sustainable Management of Water Resource and Environmental Monitoring)

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41 pages, 10231 KiB

Open AccessArticle

Assessment of Weather Research and Forecasting (WRF) Physical Schemes Parameterization to Predict Moderate to Extreme Rainfall in Poorly Gauged Basin

by Syeda Maria Zaidi, Jacqueline Isabella Anak Gisen, Mohamed Eltahan, Qian Yu, Syarifuddin Misbari and Su Kong Ngien

Sustainability 2022, 14(19), 12624; https://doi.org/10.3390/su141912624 - 4 Oct 2022

Viewed by 1561

Abstract

Incomplete hydro-meteorological data and insufficient rainfall gauges have caused difficulties in establishing a reliable flood forecasting system. This study attempted to adopt the remotely sensed hydro-meteorological data as an alternative to the incomplete observed rainfall data in the poorly gauged Kuantan River Basin (KRB), the main city at the east coast of Peninsula Malaysia. Performance of Weather Research and Forecasting (WRF) schemes’ combinations, including eight microphysics (MP) and six cumulus, were evaluated to determine the most suitable combination of WRF MPCU in simulating rainfall over KRB. All the obtained results were validated against observed moderate to extreme rainfall events. Among all, the combination scheme Stony Brook University and Betts–Miller–Janjic (SBUBMJ) was found to be the most suitable to capture both spatial and temporal rainfall, with average percentage error of about ±17.5% to ±25.2% for heavy and moderate rainfall. However, the estimated PE ranges of −58.1% to 68.2% resulted in uncertainty while simulating extreme rainfall events, requiring more simulation tests for the schemes’ combinations using different boundary layer conditions and domain configurations. Findings also indicate that for the region where hydro-meteorological data are limited, WRF, as an alternative approach, can be used to achieve more sustainable water resource management and reliable hydrological forecasting. Full article

(This article belongs to the Special Issue Sustainable Management of Water Resource and Environmental Monitoring)

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Graphical abstract

13 pages, 1203 KiB

Open AccessArticle

Effectiveness of Biochar and Zeolite Soil Amendments in Reducing Pollution of Municipal Wastewater from Nitrogen and Coliforms

by Hamid Reza Asghari, Günther Bochmann and Zahra Taghizadeh Tabari

Sustainability 2022, 14(14), 8880; https://doi.org/10.3390/su14148880 - 20 Jul 2022

Cited by 5 | Viewed by 1647

Abstract

A greenhouse experiment with soil cores and wastewater application was carried out to investigate the effects of biochar and zeolite on the mobility of nitrogen and coliform bacteria during the leaching of columns repacked by a silty loam soil. Triticum aestivum plants were grown in cores with and without biochar and zeolite irrigated with municipal wastewater for 4 months in the greenhouse. Cores were then flushed with 800 mLof distillate water and, finally, the leachate was collected. Application of biochar or zeolite significantly (p ≤ 0.05) reduced nitrate and ammonium loss in soil after leaching process, compared to their non-treated counterparts. In addition, interactions of biochar and zeolite significantly decreased nitrate and ammonium content in leachate. Biochar had higher removal effects of coliform bacteria in leachate than zeolite. Lower nitrate and ammonium content in leachate was related to the increased retention of soil amendments. Application of 5% w/w of biochar also reduced the volume of leachate by 11% compare to control, but using 5% w/w and 10% w/w of zeolite increased the volume of leachate compared with non-treated columns by 21% and 48%, respectively. Taken together, these data highlight the need to consider the potential benefits of biochar and zeolite as soil amendment to reduce nitrogen mobility and remove coliform bacteria in the leaching process of municipal wastewater in agricultural systems. Full article

(This article belongs to the Special Issue Sustainable Management of Water Resource and Environmental Monitoring)

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30 pages, 13290 KiB

Open AccessArticle

An Integrated Statistical-Machine Learning Approach for Runoff Prediction

by Abhinav Kumar Singh, Pankaj Kumar, Rawshan Ali, Nadhir Al-Ansari, Dinesh Kumar Vishwakarma, Kuldeep Singh Kushwaha, Kanhu Charan Panda, Atish Sagar, Ehsan Mirzania, Ahmed Elbeltagi, Alban Kuriqi and Salim Heddam

Sustainability 2022, 14(13), 8209; https://doi.org/10.3390/su14138209 - 5 Jul 2022

Cited by 47 | Viewed by 4349

Abstract

Nowadays, great attention has been attributed to the study of runoff and its fluctuation over space and time. There is a crucial need for a good soil and water management system to overcome the challenges of water scarcity and other natural adverse events like floods and landslides, among others. Rainfall–runoff (R-R) modeling is an appropriate approach for runoff prediction, making it possible to take preventive measures to avoid damage caused by natural hazards such as floods. In the present study, several data-driven models, namely, multiple linear regression (MLR), multiple adaptive regression splines (MARS), support vector machine (SVM), and random forest (RF), were used for rainfall–runoff prediction of the Gola watershed, located in the south-eastern part of the Uttarakhand. The rainfall–runoff model analysis was conducted using daily rainfall and runoff data for 12 years (2009 to 2020) of the Gola watershed. The first 80% of the complete data was used to train the model, and the remaining 20% was used for the testing period. The performance of the models was evaluated based on the coefficient of determination (R²), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), and percent bias (PBAIS) indices. In addition to the numerical comparison, the models were evaluated. Their performances were evaluated based on graphical plotting, i.e., time-series line diagram, scatter plot, violin plot, relative error plot, and Taylor diagram (TD). The comparison results revealed that the four heuristic methods gave higher accuracy than the MLR model. Among the machine learning models, the RF (RMSE (m³/s), R², NSE, and PBIAS (%) = 6.31, 0.96, 0.94, and −0.20 during the training period, respectively, and 5.53, 0.95, 0.92, and −0.20 during the testing period, respectively) surpassed the MARS, SVM, and the MLR models in forecasting daily runoff for all cases studied. The RF model outperformed in all four models’ training and testing periods. It can be summarized that the RF model is best-in-class and delivers a strong potential for the runoff prediction of the Gola watershed. Full article

(This article belongs to the Special Issue Sustainable Management of Water Resource and Environmental Monitoring)

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19 pages, 3672 KiB

Open AccessEditor’s ChoiceArticle

A Study of Assessment and Prediction of Water Quality Index Using Fuzzy Logic and ANN Models

by Roman Trach, Yuliia Trach, Agnieszka Kiersnowska, Anna Markiewicz, Marzena Lendo-Siwicka and Konstantin Rusakov

Sustainability 2022, 14(9), 5656; https://doi.org/10.3390/su14095656 - 7 May 2022

Cited by 24 | Viewed by 2816

Abstract

Various human activities have been the main causes of surface water pollution. The uneven distribution of industrial enterprises in the territories of the main river basins of Ukraine do not always allow the real state of the water quality to be assessed. This article has three purposes: (1) the modification of the Ukrainian method for assessing the WQI, taking into account the level of negative impact of the most dangerous chemical elements, (2) the modeling of WQI assessment using fuzzy logic and (3) the creation of an artificial neural network model for the prediction of the WQI. The fuzzy logic model used four input variables and calculated one output variable (WQI). In the final stage of the study, six ANN models were analyzed, which differed from each other in various loss function optimizers and activation functions. The optimal results were shown using an ANN with the softmax activation function and Adam’s loss function optimizer (MAPE = 9.6%; R² = 0.964). A comparison of the MAPE and R² indicators of the created ANN model with other models for assessing water quality showed that the level of agreement between the forecast and target data is satisfactory. The novelty of this study is in the proposal to modify the WQI assessment methodology which is used in Ukraine. At the same time, the phased and joint use of mathematical tools such as the fuzzy logic method and the ANN allow one to effectively evaluate and predict WQI values, respectively. Full article

(This article belongs to the Special Issue Sustainable Management of Water Resource and Environmental Monitoring)

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32 pages, 18483 KiB

Open AccessArticle

Spatio-Temporal Analysis of Rainfall Dynamics of 120 Years (1901–2020) Using Innovative Trend Methodology: A Case Study of Haryana, India

by Abhilash Singh Chauhan, Surender Singh, Rajesh Kumar Singh Maurya, Ozgur Kisi, Alka Rani and Abhishek Danodia

Sustainability 2022, 14(9), 4888; https://doi.org/10.3390/su14094888 - 19 Apr 2022

Cited by 8 | Viewed by 2453

Abstract

As we know, climate change and climate variability significantly influence the most important component of global hydrological cycle, i.e., rainfall. The study pertaining to change in the spatio-temporal patterns of rainfall dynamics is crucial to take appropriate actions for managing the water resources at regional level and to prepare for extreme events such as floods and droughts. Therefore, our study has investigated the spatio-temporal distribution and performance of seasonal rainfall for all districts of Haryana, India. The gridded rainfall datasets of 120 years (1901 to 2020) from the India Meteorological Department (IMD) were categorically analysed and examined with statistical results using mean rainfall, rainfall deviation, moving-average, rainfall categorization, rainfall trend, correlation analysis, probability distribution function, and climatology of heavy rainfall events. During each season, the eastern districts of Haryana have received more rainfall than those in its western equivalent. Rainfall deviation has been positive during the pre-monsoon season, while it has been negative for all remaining seasons during the third quad-decadal time (QDT3, covering the period of 1981–2020); rainfall has been declining in most of Haryana’s districts during the winter, summer monsoon, and post-monsoon seasons in recent years. The Innovative Trend Analysis (ITA) shows a declining trend in rainfall during the winter, post-monsoon, and summer monsoon seasons while an increasing trend occurs during the pre-monsoon season. Heavy rainfall events (HREs) were identified for each season from the last QDT3 (1981–2020) based on the available data and their analysis was done using European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis Interim (ERA-Interim), which helped in understanding the dynamics of atmospheric parameters during HREs. Our findings are highlighting the qualitative and quantitative aspects of seasonal rainfall dynamics at the districts level in Haryana state. This study is beneficial in understanding the impact of climate change and climate variability on rainfall dynamics in Haryana, which may further guide the policymakers and beneficiaries for optimizing the use of hydrological resources. Full article

(This article belongs to the Special Issue Sustainable Management of Water Resource and Environmental Monitoring)

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21 pages, 7180 KiB

Open AccessArticle

Neurocomputing Modelling of Hydrochemical and Physical Properties of Groundwater Coupled with Spatial Clustering, GIS, and Statistical Techniques

by Mohammed Benaafi, Mohamed A. Yassin, A. G. Usman and S. I. Abba

Sustainability 2022, 14(4), 2250; https://doi.org/10.3390/su14042250 - 16 Feb 2022

Cited by 11 | Viewed by 2567

Abstract

Groundwater (GW) is a critical freshwater resource for billions of individuals worldwide. Rapid anthropogenic exploitation has increasingly deteriorated GW quality and quantity. Reliable estimation of complex hydrochemical properties of GW is crucial for sustainable development. Real field and experimental studies in an agricultural area from the significant sandstone aquifers (Wajid Aquifer) were conducted. For the modelling purpose, three types of computational models, including the emerging Hammerstein–Wiener (HW), back propagation neural network (BPNN), and statistical multi-variate regression (MVR), were developed for the multi-station estimation of total dissolved solids (TDS) (mg/L) and total hardness (TH) (mg/L). A geographic information system (GIS) was used for the spatial variability assessment of 32 hydrochemical and physical properties of the GW aquifer. A comprehensive visualized literature review spanning several decades was conducted in order to gain an understanding of the existing research and debates relevant to a particular GW and artificial intelligence (AI) study. The experimental data, pre-processing, and feature selection were conducted to determine the most dominant variables for AI-based modelling. The estimation results were evaluated using determination coefficient (DC), mean bias error (MBE), mean square error (MSE), and root mean square error (RMSE). The outcomes proved that TDS (mg/L) and TH (mg/L) correlated more than 90% and 70–85% with Ca²⁺, Cl⁻, Br⁻, NO3⁻, and Fe, and Na⁺, SO₄²⁻, Mg²⁺, and F⁻ combinations, respectively. HW-M1 justified promising among all the models with MBE = 1.41 × 10⁻¹¹, 1.14 × 10⁻¹⁴, and MSE = 7.52 × 10⁻², 3.88 × 10⁻¹¹ for TDS (mg/L), TH (mg/L), respectively. The accuracy proved merit for the overall development of and practical estimation of hydrochemical variables (TDS, TH) (mg/L) and decision-making benchmarks. Full article

(This article belongs to the Special Issue Sustainable Management of Water Resource and Environmental Monitoring)

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Review

Jump to: Research

16 pages, 1015 KiB

Open AccessReview

Application of Biochar for Improving Physical, Chemical, and Hydrological Soil Properties: A Systematic Review

by Shakeel Ahmad Bhat, Alban Kuriqi, Mehraj U. Din Dar, Owais Bhat, Saad Sh. Sammen, Abu Reza Md. Towfiqul Islam, Ahmed Elbeltagi, Owais Shah, Nadhir AI-Ansari, Rawshan Ali and Salim Heddam

Sustainability 2022, 14(17), 11104; https://doi.org/10.3390/su141711104 - 5 Sep 2022

Cited by 21 | Viewed by 5062

Abstract

Biochar is a carbon-based substance made by the pyrolysis of organic waste. The amount of biochar produced is determined by the type of feedstock and pyrolysis conditions. Biochar is frequently added to the soil for various reasons, including carbon sequestration, greenhouse gas mitigation, improved crop production by boosting soil fertility, removing harmful contaminants, and drought mitigation. Biochar may also be used for waste management and wastewater treatment. Biochar’s various advantages make it a potentially appealing instrument material for current science and technology. Although biochar’s impacts on soil chemical qualities and fertility have been extensively researched, little is known about its impact on enhancing soil physical qualities. This review is intended to describe biochar’s influence on some crucial soil physical and hydrological properties, including bulk density of soil, water holding capacity, soil porosity, soil hydraulic conductivity, soil water retention, water repellence–available plant water, water infiltration, soil temperature, soil color, and surface albedo. Therefore, we propose that the application of biochar in soils has considerable advantages, and this is especially true for arable soils with low fertility. Full article

(This article belongs to the Special Issue Sustainable Management of Water Resource and Environmental Monitoring)

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