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  Subjects -> WATER RESOURCES (Total: 160 journals)
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Applied Water Science
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  This is an Open Access Journal Open Access journal
ISSN (Print) 2190-5487 - ISSN (Online) 2190-5495
Published by SpringerOpen Homepage  [228 journals]
  • Modelling nitrogen transformation in the Lake Bunyonyi ecosystem,
           South-Western Uganda

    • Abstract: Abstract Lake Bunyonyi is one of the major resources of social-economic potential in the districts of Rubanda and Kabale, South-Western Uganda. The lake’s sub-catchment faces environmental problems because of intensive agriculture, settlement, business and tourism activities, which consequently cause pollution of water in the lake’s system. This study, therefore, intended to determine the processes that govern nitrogen dynamism using a numerical model that takes into account various processes in the system using STELLA® 8.1.1 software. From the model simulation, it was found that mineralization, microbial uptake and nitrification were the major processes governing nitrogen transformation in the water phase, accounting for 47.8% (0.49 g/d m−2), 44.2% (0.45 g/d m−2), and 7.8% (0.05 g/d m−2), respectively. The developed model predicted reasonably well the behaviour of the lake evidenced by the validation results of observed and simulated data that showed good linear regression coefficients (R2) of organic nitrogen (0.48), ammonia–nitrogen (0.68), and nitrate–nitrogen (0.61). The model has proven suitable for application on lakes with characteristics similar to that of Lake Bunyonyi. The study recommended that a compressive investigation that puts into consideration all the possible sources of nutrient and water inflow into the lake system be done on Lake Bunyonyi.
      PubDate: 2022-06-24
       
  • Optimizing coagulation–flocculation processes with aluminium coagulation
           using response surface methods

    • Abstract: Abstract A data-based multivariate method such as response surface methods and desirability function is considered advantageous for analysing coagulation treatment optimization. Thus, this study investigated the optimization of coagulation–flocculation using response surface methodology. The parameters investigated were pH, alum dose and alkalinity. The optimum coagulation conditions for the individual responses: turbidity, colour, residual aluminium and phenanthrene were pH 7.0, alum dose 80.0 mg/L and alkalinity 80.0 mg/L; pH, 6.5, alum dose 70.0 mg/L and alkalinity 90.0 mg/L; pH, 7.0, alum dose 63.2 mg/L and alkalinity 80.0 mg/L and pH 6.2, alum dose 80.0 mg/L and alkalinity of 80.0 mg/L, respectively. The model equation derived from the optimization study was adequate for predicting the response values. The quadratic model was significant (p < 0.0001), and it had a high correlation (R2, 0.746–0.975) and an insignificant (LOF, p > 0.05) lack of fit.
      PubDate: 2022-06-24
       
  • Removal of lead ions (Pb2+) from water and wastewater: a review on the
           low-cost adsorbents

    • Abstract: Abstract The presence of lead compounds in the environment is an issue. In particular, supply water consumption has been reported to be a significant source of human exposure to lead compounds, which can pose an elevated risk to humans. Due to its toxicity, the International Agency for Research on Cancer and the US Environmental Protection Agency (USEPA) have classified lead (Pb) and its compounds as probable human carcinogens. The European Community Directive and World Health Organization have set the maximum acceptable lead limits in tap water as 10 µg/L. The USEPA has a guideline value of 15 µg/L in drinking water. Removal of lead ions from water and wastewater is of great importance from regulatory and health perspectives. To date, several hundred publications have been reported on the removal of lead ions from an aqueous solution. This study reviewed the research findings on the low-cost removal of lead ions using different types of adsorbents. The research achievements to date and the limitations were investigated. Different types of adsorbents were compared with respect to adsorption capacity, removal performances, sorbent dose, optimum pH, temperature, initial concentration, and contact time. The best adsorbents and the scopes of improvements were identified. The adsorption capacity of natural materials, industrial byproducts, agricultural waste, forest waste, and biotechnology-based adsorbents were in the ranges of 0.8–333.3 mg/g, 2.5–524.0 mg/g, 0.7–2079 mg/g, 0.4–769.2 mg/g, and 7.6–526.0 mg/g, respectively. The removal efficiency for these adsorbents was in the range of 13.6–100%. Future research to improve these adsorbents might assist in developing low-cost adsorbents for mass-scale applications.
      PubDate: 2022-06-22
       
  • Preliminary analysis of the preparation of Polish water utilities to
           implement mandatory risk management in accordance with the Drinking Water
           Directive 2020/2184

    • Abstract: Abstract The new Directive 2020/2184 of the European Parliament and the Council on the quality of water intended for human consumption (EU, Official J EU 23.12.2020.435, 2020) is to be transposed into the local law of all Member States by 2023 and its implementation will start thereafter. Therefore, it is important to identify what are the water utilities' greatest concerns. The Chamber of Commerce "Polish Waterworks" conducted two surveys among its members, aimed at understanding the needs of the water supply sector in the context of the new requirements of the directive. The surveys were conducted one year apart. The first in January 2019, the second in January 2020, when the new directive has not yet been adopted; however, its content was known to a close approximation. The article focuses on the issues of risk-based management, presents the responses of water companies, and indicates their needs. The research results indicate not only financial needs. A major challenge is the development of knowledge and competencies in the field of risk management in the water supply system. Enterprises need substantive support and their situation, despite the Chamber's actions, does not improve significantly. Surveys indicate the need for expert training and support in assessing and managing risks in water systems. It is necessary to reach the smallest entities with knowledge and support.
      PubDate: 2022-06-22
       
  • Application of hybrid artificial neural network (ANN)–particle swarm
           optimization (PSO) for modelling and optimization of the adsorptive
           removal of cyanide and phenol from wastewater using agro-waste-derived
           adsorbent

    • Abstract: Abstract In the present study, the waste part of the banana tree was used as a precursor, and copper chloride salt was used as an impregnating agent for the preparation of adsorbent to remove both cyanide and phenol from synthetic wastewater. Initially, thermogravimetric analysis was used to determine the rate of carbonization of the material with temperature, and thus, the optimum temperature (370 °C) and time of carbonization (35 min) were assessed. Different samples of adsorbents were prepared next by varying the weight ratio of pseudo-stem of waste banana tree to copper salt from 1:1 to 30:1. All the samples were then tested for removal of both the pollutants, and the ratio (20:1) corresponding to maximum removal of both the pollutants was considered as optimum. Therefore, further studies were conducted with the adsorbent prepared at optimum ratio, temperature and time and such adsorbent was termed as copper impregnated activated banana tree (CIABT). One variable at a time approach was followed to find out the most effective condition based on the maximum removal of pollutants. Maximum removal of 95.99 ± 1.03% and 97.33 ± 0.04% was achieved for cyanide (initial concentration: 100 ppm) and phenol (initial concentration: 450 ppm), respectively, at an optimum contact time of 150 min, the particle size of 90 μ, the adsorbent dosage of 10 g/L, pH 8.0 using CIABT at 25 °C. Hybrid artificial neural network–particle swarm optimization were employed for modelling-optimization of removal of both the pollutants while achieving 91.4–99.99% and 86.43–99.99% removal of cyanide and phenol, respectively, from simulated wastewater.
      PubDate: 2022-06-22
       
  • Correct path to use flumes in water resources management

    • Abstract: Abstract The hydraulic characteristics of the flow are measured using tools such as flumes, in the design and evaluation of furrow irrigation systems. Proper use of these tools, such as their immersion while working, is one of the important executive points in this field; in this study, trapezoidal flumes are used to measure the intensity of input and output flow in furrow irrigation. The proper method of installing these flumes was investigated in this article. For this purpose, during 60 irrigation operations, the results showed that in order to create free flow conditions in these flumes, and not to affect the downstream and upstream current, as well as increasing the accuracy of measurements, in addition to installing flumes in all directions, trapezoidal flume should be installed at a height of at least about 4 cm above the furrow bed; according to the irrigation operations, the percentage of immersion in the installation of the flume at a height of 4 cm from the furrow bed was observed as standard (less than 70% immersion) in order to reduce the percentage of flow measurement error in different depths of water entering the flume. The results also showed that for ensuring free flow in trapezoidal flumes, the flume should be installed at a height of 4 cm or more above the furrow bed, provided the input ridges are strengthened and the end flume is measured to measure the inflow to the furrow. The output current of the furrow should be installed in the floor of the furrow along the bed to prevent the passage of current, provided that after the outflow flume, the furrow bed should be deeper in terms of free flow. Observance of the points and results obtained in this study in furrow irrigation systems prevents errors in flow measurement and consequently increases the accuracy in the design and evaluation of furrow irrigation systems.
      PubDate: 2022-06-22
       
  • Spring water quality assessment of Anantnag district of Kashmir Himalaya:
           towards understanding the looming threats to spring ecosystem services

    • Abstract: Abstract This study reports the significance of freshwater springs primarily in meeting drinking water demands besides offering various ecosystem services. We analyzed a total of eighteen geochemical quality parameters using standard methods from various representative springs of Anantnag district, Kashmir Himalaya. Groundwater quality profiles were generated in a GIS environment for each parameter. Additionally, statistical methods were employed to understand the interdependence of water quality parameters. Highly variable dissolved oxygen (0.4–9.2 mg L−1) and relatively higher values of nitrate ranging from 57 to 2668 µg L−1 noticed during the study may be mostly related to contamination from agricultural waste. The findings of this study revealed that the springs are predominantly hard water type as the water samples found were calcium-rich and exhibited higher total phosphorus in few samples owing to limestone lithology in the catchment. Principal Component Analysis (PCA) to the data generated chiefly three components (VF1, VF2, and VF3) having Eigen values of 2.0 or more (2.28–5.37) contributing for 31.63%, 17.99% and 13.44% of the total variance, respectively. The water quality index (WQI) of the samples for drinking purpose ranged from good to excellent. In light of our findings, it is argued that springs offer a potential, although partial, solution to the drinking water demands of a burgeoning population in Indian Himalayan region. However, equally important is to have a thorough investigation of springs to explore the impacts of other forms of pollution, including heavy metals, pesticides and antibiotic wastes, which can diminish much-needed ecosystem services.
      PubDate: 2022-06-18
       
  • Enhancement of nickel laterite ore bioleaching by Burkholderia sp. using a
           factorial design

    • Abstract: Abstract Interest in low-grade Ni-laterite ores has increased in recent years; however, the laterite process has proven technically difficult and costly, and the development of alternative low-cost biotechnologies for Ni solubilization has been encouraged. In this context, for the first time, a sample of Brazilian Ni-laterite ore was subjected to microbial bioleaching using a heterotrophic Burkholderia sp. strain. Experiments were performed in a 23 two-level full factorial design by determining the influence of glucose concentration (5–15%, w/v), Ni-laterite ore concentration (0.25–0.75%, w/v), and cultivation period (14–42 days) on Ni solubilization. The variable more important for Ni-laterite bioleaching was the glucose concentration (x1). Bioleaching batch experiments demonstrated that about 87% Ni (7.5 mg Ni/g ore) were solubilized by Burkholderia sp. after 42 days. This study's significance is that it has opened up an opportunity for the potential application of potassium-solubilizing bacterial strains to process low-grade Ni-laterite ores.
      PubDate: 2022-06-18
       
  • Kinematic reverse flood routing in natural rivers using stage data

    • Abstract: Abstract In many developing countries, due to economic constraints, a single station on a river reach is often equipped to record flow variables. On the other hand, hydrographs at the upstream sections may also be needed for especially assessing flooded areas. The upstream flow hydrograph prediction is called the reverse flood routing. There are some reverse flood routing pocedures requiring sophisticated methods together with substantial data requirements. This study proposes a new reverse flood routing procedure, based upon the simple kinematic wave (KW) equation, requiring only easily measurable downstream stage data. The KW equation is first averaged along a channel length at a fixed time, t, assuming that channel width is spatially constant, and then the spatially averaged equation is averaged in time, Δt. The temporally averaged terms are approximated as the arithmetical mean of the corresponding terms evaluated at time t and t + Δt. The Chezy roughness equation is employed for flow velocity, and the upstream flow stage hydrograph is assumed be described by a two parameter gamma distribution (Pearson Type III). The spatially averaged mean flow depth and lateral flow are related to the downstream flow stage. The resulting routing equation is thus obtained as a function of only downstream flow stage, meaning that the method mainly requires measurements of downstream flow stage data besides the mean values of channel length, channel width, roughness coefficient and bed slope. The optimal values of the parameters of reverse flood routing are obtained using the genetic algorithm. The calibration of the model is accomplished by using the measured downstream hydrographs. The validation is performed by comparing the model-generated upstream hydrographs against the measured upstream hydrographs. The proposed model is applied to generate upstream hydrographs at four different river reaches of Tiber River, located in central Italy. The length of river reaches varied from 20 to 65 km. Several upstream hydrographs at different stations on this river are generated using the developed method and compared with the observed hydrographs. The method predicts the time to peak with less than 5% error and peak rates with less than 10% error in the short river reaches of 20 km and 31 km. It also predicts the time to peak and peak rate in other two brances of 45 km and 65 km with less than 15% error. The method satisfactorily generates upstream hydrographs, with an overall mean absolute error (MAE) of 42 m3/s.
      PubDate: 2022-06-18
       
  • Drought monitoring using the long-term CHIRPS precipitation over
           Southeastern Iran

    • Abstract: Abstract Climate change and global warming are often considered the main reason for water scarcity in Iran. However, there is little evidence showing that the arid/wet regions get drier/wetter due to climate change. Some researchers believe that parts of water challenges in Iran arise from bad governance and mismanagement of water resources. To address the role of climate change on the water scarcity, this study aims to detect the drought trends in the southeast of Iran to investigate drought characteristics changes during 1981–2020. The nonparametric Mann–Kendall test was used for this purpose. CHIRPS product was collected as an alternative source of ground data for trend analysis of drought characteristics. The evaluation metrics show that the CHIRPS product performs better at monthly and annual scales (correlation higher than 0.8) than daily (correlation less than 0.4). The results also illustrate that the duration and severity of short-term droughts (3, 6, and 9 months) have decreased, while their intensity has increased. Conversely, duration, severity, and intensity changes for long-term droughts (12, 18, and 24 months) are insignificant. The trend in the Standardized Precipitation Index (SPI) showed that, in general, the southeast of Iran has not been getting drier during the last four decades. One may conclude that the change in precipitation is not the only reason for water challenges in this area, and both natural and anthropogenic drought might cause water scarcity. Accordingly, it is suggested that the effects of human activities and governmental plans should be considered as well.
      PubDate: 2022-06-18
       
  • Arsenic removal using calcium hydroxyapatite synthesized from paper mill
           sludge

    • Abstract: Abstract Calcium hydroxyapatite (Ca-HAp) was synthesized from calcium carbonate (CaCO3) extracted from a paper mill sludge. The extraction of CaCO3 was carried out by chemical precipitation process and synthesized to HAp nanoparticle under appropriate stoichiometric condition through wet chemical precipitation process. The size of the HAp nanoparticle was 42.5 nm under an optimized aging period of 24 h. This work aims in the batch adsorption of arsenic, an anionic metal arsenic in the form of Arsenite: As(III) on the synthesized Ca-HAp in laboratory scale. Batch kinetics studies were conducted for varying operational parameters such as temperature, initial adsorbate concentration, dye solution pH and rotation speed (RPM). In comparison with the two suggested isotherm models, Langmuir isotherm was suited to this adsorption process with a correlation coefficient of 0.92 and isotherm constant as 1.18 (KL). Chemisorption was found to be the rate-limiting mechanism for the sorption of arsenite onto Ca-HAp and thus followed pseudo-second-order kinetics. A maximum monolayer adsorption of 0.43 mg/g of arsenic was obtained at an equilibrium time of 60 min with 93% to 94.2% removal efficiency.
      PubDate: 2022-06-14
       
  • Investigation of meteorological variables on runoff archetypal using SWAT:
           basic concepts and fundamentals

    • Abstract: Abstract Hydro-climatic excesses, for example humid and overflows, have most probable enlarged owing to climatically alteration and could due to simple effects on socio-financial, organizational and ecological areas. It was premeditated greatest hydraulic plans, for example barricades, it was distinct the excess of the streams. If the stream presences any situation to quantity the profit, the hydraulic mockups can be used to guesstimate it. SWAT is widely-used high-tech mockups. This investigation contemplates the understanding of the excess approximation for streams, by the SWAT prototypical; depend on changes in such meteorological parameters as rainfall, cosmological energy, airstream, moisture and temperature. The gained significances require that by 30.46% decay in the normal scheduled rainfall, brightness, qualified moisture, airstream and temperature, it was usual ermined 64.73% decay, 115.14% rise, 45.99% decrease, 126.58% rise and 40.15% rise in exhibited excess, independently. The wind speed and the solar energy are the most sensitive and temperature is the smallest penetrating parameters in the overflow approximation. These consequences signify “acceptable” and “very good” performances for discharge. While there is still some quantity of ambiguity, the practice of balancing information, for example soil dampness, to adjust and confirm the SWAT model package prototypical is beneficial, particularly when discharge information is infrequent, as for some watersheds in the humid region. Evaluation of the water usage efficacy is the important to efficiently accomplish agronomic water resource.
      PubDate: 2022-06-14
       
  • Comparison of machine learning and process-based SWAT model in simulating
           streamflow in the Upper Indus Basin

    • Abstract: Abstract This study appraised and compared the performance of process-based hydrological SWAT (soil and water assessment tool) with a machine learning-based multi-layer perceptron (MLP) models for simulating streamflow in the Upper Indus Basin. The study period ranges from 1998 to 2013, where SWAT and MLP models were calibrated/trained and validated/tested for multiple sites during 1998–2005 and 2006–2013, respectively. The performance of both models was evaluated using nash–sutcliffe efficiency (NSE), coefficient of determination (R2), Percent BIAS (PBIAS), and mean absolute percentage error (MAPE). Results illustrated the relatively poor performance of the SWAT model as compared with the MLP model. NSE, PBIAS, R2, and MAPE for SWAT (MLP) models during calibration ranged from the minimum of 0.81 (0.90), 3.49 (0.02), 0.80 (0.25) and 7.61 (0.01) to the maximum of 0.86 (0.99), 9.84 (0.12), 0.87 (0.99), and 15.71 (0.267), respectively. The poor performance of SWAT compared with MLP might be influenced by several factors, including the selection of sensitive parameters, selection of snow specific sensitive parameters that might not represent actual snow conditions, potential limitations of the SCS-CN method used to simulate streamflow, and lack of SWAT ability to capture the hydropeaking in Indus River sub-basins (at Shatial bridge and Bisham Qila). Based on the robust performance of the MLP model, the current study recommends to develop and assess machine learning models and merging the SWAT model with machine learning models.
      PubDate: 2022-06-14
       
  • Estimating discharge coefficient of side weirs in trapezoidal and
           rectangular flumes using outlier robust extreme learning machine

    • Abstract: Abstract Using the outlier robust extreme learning machine (ORELM) method, the discharge coefficient of side weirs placed on rectangular and trapezoidal canals was simulated for the first time in this study. The parameters governing the discharge coefficient of side weirs including Froude number (Fr), the ratio of the weir length to the main channel length (L/b), the ratio of the flow depth at the upstream of the side weir to the main channel width (y1/b) and the ratio of the crest height of the side weir to the flow depth at the upstream of the side weir (W/y1), the ratio of the weir length to the main channel width (L/y1), and the side wall slope parameter (m) were initially detected. Using the parameters governing, eight different input combinations were defined. By randomly selection approach, 65% of the data were considered to train the ORELM models and the rest of samples were applied to test them. The correlation coefficient, Nash–Sutcliffe efficiency coefficient, and Scatter Index for this model were calculated to be 0.937, 0.869 and 0.092, respectively. The results of sensitivity analysis indicated the ORELM model was more sensitive to the W/y1 and L/b than Fr and y1/b. The results of the ORELM model were also compared with the support vector machine optimized with genetic algorithm (SVM-GA) and extreme learning machine (ELM)) and four multiple linear regression models, with a better performance of the ORELM model. The ORELM models demonstrated a higher precision and correlation with experimental values.
      PubDate: 2022-06-14
       
  • Can sampling techniques improve the performance of decomposition-based
           hydrological prediction models' Exploration of some comparative
           experiments

    • Abstract: Abstract The development of sequence decomposition techniques in recent years has facilitated the wide use of decomposition-based prediction models in hydrological forecasting. However, decomposition-based prediction models usually use the overall decomposition (OD) sampling technique to extract samples. Some studies have shown that the OD sampling technique causes abnormally “high” performance of models owing to the utilization of future information, and this technique cannot be applied in practice. Several researchers have also proposed novel sampling techniques, such as semi-stepwise decomposition (SSD), fully stepwise decomposition (FSD), and single-model SSD (SMSSD). Moreover, an improved single-model FSD (SMFSD) sampling technique is proposed in this study. Four decomposition methods, namely discrete wavelet transform (DWT), empirical mode decomposition (EMD), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and variational mode decomposition (VMD), are introduced in this study. A systematic investigation of the models developed using OD sampling techniques is conducted, and the applicability of SSD, FSD, SMSSD, and SMFSD sampling techniques is reasonably evaluated. The application of monthly runoff prediction using the five sampling techniques and four decomposition methods at five representative hydrological stations in Poyang Lake, China, shows that (1) EMD and CEEMDAN (including the improved EMD-based adaptive decomposition method) cannot be used to construct stepwise decomposition prediction models because the implementation of the stepwise decomposition strategy leads to a variable number of sub-series. (2) OD sampling techniques cannot develop convincing models for practical prediction because future information is introduced into the samples for model training. (3) Models developed based on SSD and SMSSD sampling techniques do not use future information in the training process, but suffer from severe overfitting and inferior prediction performance. (4) Models developed based on FSD and SMFSD sampling techniques can produce convincing prediction results, and the combination of the proposed SMFSD sampling technique and VMD develops prediction models with superior performance and significantly enhances the efficiency of the models.
      PubDate: 2022-06-14
       
  • Adsorption characteristics of methyl red dye by Na2CO3-treated jute fibre
           using multi-criteria decision making approach

    • Abstract: Abstract This article reports the use of sodium carbonate-treated jute fibre (SCTJF), for the removal of an azo dye methyl red (MR). Face-centred CCD, based on RSM, experimental design has been used to acquire a definite number of experimental paths in order to ascertain improved experimentation towards reaching performance characteristics that are ideal in order to remove the dye (MR) dissolved in aqueous solution. Independent variable parameters used for dye removal and maximum adsorption capacity (Qmax) are: rotational speed (100 RPM, 150 RPM and 200 RPM), temperature (293 K, 303 K and 313 K), pH (3, 7 and 11) and adsorbent (SCTJF) dose (10 mg/L, 14 mg/L and 18 mg/L), where Qmax of the treated jute was considered to be the performance measure for dye removal. ANOVA was used in conjunction with a quadratic model of second order to explore the impact of operating variables and their elucidation. pH = 7.08, temperature = 299.57 K, SCTJF dose = 14.74 g/L, and stirring speed = 155 RPM were found to be the best process conditions. With a desirability of 0.98, the computed experimental Qmax (32.11 mg/g) and anticipated Qmax (31.7 mg/g) were in resonance within the domain threshold, indicating outstanding accuracy of the experimentation operations.
      PubDate: 2022-06-14
       
  • Comparison of available treatment techniques for hazardous aniline-based
           organic contaminants

    • Abstract: Abstract The growing contamination of various freshwater resources due to industrial effluent is a serious concern among the scientific community. Several organic compounds are essentially used as chemical intermediate in variety of industrial processes. These organic compounds are hazardous chemicals which are already considered dangerous to global public health and other forms of life due to their high toxicity, carcinogenicity. These organic contaminants are found present in the industrial effluents. Several treatment methods were applied in the literature for their elimination from wastewater to make their final disposal safe for environment. In this article, different kinds of physical, biological and advanced oxidation methods (AOPs) applied for the treatment of various important organic compounds were compared for their advantages and disadvantages. The results showed that the conventional treatment methods are not effective to treat these kinds of toxic and refractory chemical compounds. Therefore, AOPs were found to be the most promising treatment methods.
      PubDate: 2022-06-08
       
  • WaSim model for subsurface drainage design using soil hydraulic parameters
           estimated by pedotransfer functions

    • Abstract: Abstract The agricultural drainage engineering community is steadily shifting the design of subsurface drainage systems from the experience-based design approach to the simulation-based design approach. As with any design problem, two challenges are faced; firstly, how to determine all the input data required by the simulation model, and secondly to, a priori, anticipate what the performance of the designed system will be. This study sought to evaluate the performance of the WaSim model to simulate fluctuating water table depths (WTD), and drainage discharges (DD) in KwaZulu-Natal Province, South Africa. Saturated hydraulic conductivity (Ksat), which is an input to the WaSim model, was estimated by the Rosetta computer program, based on soil particle size distribution data, bulk density, and soil water retention characteristics at pressure heads of – 33 and – 1500 kPa. performance of the WaSim model was statistically assessed using the coefficient of determination (R2), coefficient of residual mass (CRM), mean absolute error (MAE), mean percent error (MPE), and the nash–sutcliffe efficiency (NSE). during the validation period, the WaSim model predicted WTDs with R2, CRM, MAE, MPE, and NSE of 0.86, 0.003, 4.9 cm, 6.0%, and 0.98, respectively. In the same validation period, the model predicted DDs with R2, CRM, MAE, MPE, and NSE of 0.57, 0.002, 0.30 mm day−1,11%, and 0.76, respectively. These results suggest that the use of Rosetta-estimated Ksat data as inputs to the WaSim model compromised its accuracy and applicability as a subsurface drainage design tool. Owing to the relatively low R2 value of 0.57, and that the WaSim model was empirically developed, we recommend further improvement on the calibration of the model for it to be suitable for application under the prevailing conditions. Also, in the absence of other means of determining Ksat, we caution the use of Rosetta-estimated Ksat data as inputs to the WaSim model for the design and analysis of subsurface drainage systems in KwaZulu-Natal Province, South Africa.
      PubDate: 2022-06-03
       
  • Groundwater contamination in public water supply wells: risk assessment,
           evaluation of trends and impact of rainfall on groundwater quality

    • Abstract: Abstract This study investigates the risk to contamination of groundwater in public water supply wells in the Koprivnica-Križevci county (northwest Croatia). Five physicochemical parameters were monitored in all groundwater samples from 2008 to 2017 to identify major differences between the wells, assess temporal variations and understand the capacity for rainfall to alter groundwater pollution loadings. Multivariate discriminant analysis showed statistically significant differences between the six sampled wells based on the analyzed parameters (Wilks' lambda: 0.001; F = 26.2; p < 0.0000). Principal component analysis revealed two significant factors, including factor 1 which explained 32.8% of the variance (suggesting that the quality of the groundwater was mainly controlled by nitrate) and factor 2, accounting for 16.2% of the total variance (which corresponded to KMnO4/oxidizability and to a lesser extent, pH). The time series data showed disparate trends, with nitrate concentrations increasing, whereas pH and KMnO4 decreased, while electrical conductivity and chloride levels remained stable. Although rainfall can impact groundwater pollution loadings through dilution processes in aquifers, the resulting fluctuations in physicochemical parameters are complicated by variations in rainfall events and local topography, as well as from climate change. Therefore, it is important to predict the contamination of groundwater quality in the future using machine learning algorithms using artificial neural network or similar methods. Multivariate statistical techniques are useful in verifying temporal and spatial variations caused by anthropogenic factors and natural processes linked to rainfall. The resulting identified risks to groundwater quality would provide the basis for further groundwater protection, particularly for decisions regarding permitted land use in recharge zones.
      PubDate: 2022-06-03
       
  • Outlier robust extreme learning machine to simulate discharge coefficient
           of side slots

    • Abstract: Abstract As the first time, this paper attempts to recreate the discharge coefficient (DC) of side slots by another artificial intelligence procedure named "Outlier Robust Extreme Learning Machine (ORELM)". Accordingly, at first, the variables affecting the DC comprising the ratios of the flow depth to the side slot length (Ym/L), the side slot crest elevation to the side slot length (W/L), the main channel width to the side slot length (B/L), as well as the Froude number (Fr) are determined and subsequently five ORELM models (ORELM 1 to ORELM 5) are created utilizing these variables. From that point forward, laboratory measurements are arranged into two datasets comprising training (70%) and testing (30%). At the subsequent stage, the best model alongside the most affecting input variables is presented by executing a sensitivity examination. The most impressive model (i.e., ORELM 3) reproduces DC values as far as B/L, W/L and Fr. It is worth focusing on that ORELM 3 forecasts DC values with worthy precision. For instance, the correlation coefficient (R), the scatter index (SI) and the Nash–Sutcliffe effectiveness (NSC) for ORELM 3 are acquired in the examination state to be 0.936, 0.049 and 0.852, independently. Examining the outcomes yielded from the simulation demonstrates that W/L and Fr are the most impacting factors to reproduce the DC. Besides, the findings of the sensitivity examination uncover that ORELM 3 acts in an underestimated way. Finally, a computer code is put forward to compute the DC of side slots.
      PubDate: 2022-05-24
       
 
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