Water Quality Simulation Research Articles

Modeling of truncated normal distribution for estimating hydraulic parameters in water distribution systems: taking nodal water demand as an example.

The normal probability density function (PDF) is widely used in parameter estimation in the modeling of dynamic systems, assuming that the random variables are distributed at infinite intervals. However, in practice, these random variables are usually distributed in a finite region confined by the physical process and engineering practice. In this study, we address this issue through the application of truncated normal PDF. This method avoids a non-differentiable problem inherited in the truncated normal PDF at the truncation points, a limitation that can limit the use of analytical methods (e.g., Gaussian approximation). A data assimilation method with the derived formula is proposed to describe the probability of parameter and measurement noise in the truncated space. In application to a water distribution system (WDS), the proposed method leads to estimating nodal water demand and hydraulic pressure key to hydraulic and water quality model simulations. Application results to a hypothetical and a large field WDS clearly show the superiority of the proposed method in parameter estimation for WDS simulations. This improvement is essential for developing real-time hydraulic and water quality simulation and process control in field applications when the parameter and measurement noise are distributed in the finite region.

Water Quality Analysis Using QUAL2Kw Model During The Rainy Season and Dry Season in Upper Citarum Watershed, West Java, Indonesia

The Increasing population in the upstream Citarum watershed area can trigger increased water pollution caused by anthropogenic pollution such as from domestic waste, agriculture, landfills, and settlements. The equilibrium of ecosystems is disrupted when there is an increase in excess pollutants such as suspended materials and nutrients that will cause eutrophication. The purpose of this study was to analyze river water quality from temperature, pH, DO, BOD, Total phosphate (PO4), and Nitrate (NO3) parameters from upstream Citarum watershed waters using a water quality model, namely Qual2Kw. This model is based on the Streeter-Phelps theory, which includes the natural purification process of river water. The Qual2Kw model can be used for decision-making to limit river water pollution. The locations reviewed in the upstream Citarum watershed area were five sampling points (SP), namely at Cisanti Outlets (1), Wangisagara (2), Koyod (3), after the Cisirung WWTP (4), and Nanjung (5). Site selection is based on sources of pollutants from anthropogenic activities that can pollute river water. The QUAL2kw model is used to simulate water quality and is used as a basis for calculating the carrying capacity of pollutant loads against the desired target water quality. The simulation results of a valid water quality model with the observation results are BOD, TSS, and NO3. and parameters such as temperature, pH, DO, and PO4 are invalid because they have errors below 25%. These three parameters (BOD, TSS, and NO)3 have exceeded the Indonesian government’s water quality standard values both in the dry and rainy season. These three parameters indicate that the high pollution in the upper reaches of the Citarum River is caused by pollutant sources from settlements, agriculture, and animal husbandry.

Improving model capability in simulating spatiotemporal variations and flow contributions of nitrate export in tile-drained catchments

It is essential to identify the dominant flow paths, hot spots and hot periods of hydrological nitrate-nitrogen (NO3-N) losses for developing nitrogen loads reduction strategies in agricultural watersheds. Coupled biogeochemical transformations and hydrological connectivity regulate the spatiotemporal dynamics of water and NO3-N export along surface and subsurface flows. However, modeling performance is usually limited by the oversimplification of natural and human-managed processes and insufficient representation of spatiotemporally varied hydrological and biogeochemical cycles in agricultural watersheds. In this study, we improved a spatially distributed process-based hydro-ecological model (DLEM-catchment) and applied the model to four tile-drained catchments with mixed agricultural management and diverse landscape in Iowa, Midwestern US. The quantitative statistics show that the improved model well reproduced the daily and monthly water discharge, NO3-N concentration and loading measured from 2015 to 2019 in all four catchments. The model estimation shows that subsurface flow (tile flow + lateral flow) dominates the discharge (70–75%) and NO3-N loading (77–82%) over the years. However, the contributions of tile drainage and lateral flow vary remarkably among catchments due to different tile-drained area percentages and the presence of farmed potholes (former depressional wetlands that have been drained for agricultural production). Furthermore, we found that agricultural management (e.g. tillage and fertilizer management) and catchment characteristics (e.g. soil properties, farmed potholes, and tile drainage) play important roles in predicting the spatial distributions of NO3-N leaching and loading. The simulated results reveal that the model improvements in representing water retention capacity (snow processes, soil roughness, and farmed potholes) and tile drainage improved model performance in estimating discharge and NO3-N export at a daily time step, while improvement of agricultural management mainly impacts NO3-N export prediction. This study underlines the necessity of characterizing catchment properties, agricultural management practices, flow-specific NO3-N movement, and spatial heterogeneity of NO3-N fluxes for accurately simulating water quality dynamics and predicting the impacts of agricultural conservation nutrient reduction strategies.

Simulation of Water Quality in a River Network with Time-Varying Lateral Inflows and Pollutants

Non-point source pollution inflow is one of the main causes of water quality decline in urban river networks. In this paper, aiming at the problem of non-point source pollutant transport in river network, the lateral outflow term in the Saint-Venant equation is improved from the previous constant to the time-varying flow process, and a mathematical model considering the time-varying source and sink term is established. Based on the initial rainfall intensity, surface confluence and non-point source pollutant concentration, a method for calculating the time-varying lateral pollutant input of nodes and tributaries with linear increase and exponential decay in the initial rainfall period is proposed. Based on the principle of proximity, the watershed is divided into districts. According to the principle of elevation, the non-point source pollutants are allocated to the calculation nodes of adjacent rivers in a certain proportion and incorporated into the model calculation so as to improve the mathematical model of river network water quality and apply it to the simulation of river network water quality in Maozhou River Basin. Verified by the measured data, the NSE values of the improved model are 0.805 and 0.851, respectively, indicating that the model has reliable hydrodynamic and water quality simulation accuracy, indicating that the model can be applied to the calculation of non-point source pollutants in the basin. Based on the improved model, the variation of COD concentration in the Maozhou River of Shenzhen before and after optimized water replenishment was calculated, and the time variation and spatial distribution law of the sudden drop of water quality in the river network caused by the inflow of non-point source pollution in the initial rainfall runoff and the rapid recovery after optimized water replenishment were revealed.

Simulation on TN and TP Distribution of Sediment in Liaohe Estuary National Wetland Park Using MIKE21-Coupling Model

A hydrodynamic-water quality model was established using MIKE21 to analyze the distribution of total nitrogen (TN) and total phosphorus (TP) in the wetland sediments of Liaohe Estuary National Wetland Park. The purpose of this model is to investigate the spatial and temporal variation patterns in wetland sediments and the distribution of pollutant contents. The research determines the applicability of this model in accurately predicting these patterns. The simulated contour map of the water quality concentration displays significant variation characteristics and reflects the actual concentration of pollutants in the wetland sediment of the Liaohe Estuary, proving the coupled model to be reasonable and accurate. The water quality simulation results indicate that water flowing through the wetland aids in purifying pollutants in the sediment, and the purification effect is more significant during the flood season. The MIKE21 model’s treatment reveals that the content of TN in wetland sediments decreases from southwest to northeast in the surface layer, following the flow direction of surface water. As the depth increases, the content of TN also decreases, with the bottom layer showing a decrease from southwest to northeast. The TP in wetland sediments receives surface water runoff and diffuses from upstream to downstream. The content of TP gradually decreases with increasing depth.

HydroPol2D — Distributed hydrodynamic and water quality model: Challenges and opportunities in poorly-gauged catchments

Floods are one of the deadliest natural hazards and are exacerbated by changes in land-use and climate. Urban development decreases infiltration by reducing pervious areas and increases the accumulation of pollutants during dry weather. It also decreases infiltration by reducing pervious areas and increases the accumulation of pollutants during dry periods. During rainy events, there is an increase in pollution concentrations and runoff that may be a source of water supply during drought periods. Modeling the quantity and quality dynamics of stormwater runoff requires a coupled hydrodynamic module capable of estimating the transport and fate of pollutants. In this paper, we evaluate the applicability of a distributed hydrodynamic model coupled with a water quality model (HydroPol2D). First, the model is compared to GSSHA and WCA2D in the V-Tilted catchment, and the limitation of the critical velocity of WCA2D is investigated. We also applied the model in a laboratory wooden board catchment, focusing on the validation of the numerical approach to simulate water quality dynamics. Then, we apply HydroPol2D in the Tijuco Preto catchment, in São Carlos - Brazil, and compare the modeled results with the full momentum solver of the HEC-RAS 2D. This catchment shares similar characteristics with many poorly-gauged and human-impacted catchments worldwide. The implementation of the model, the governing equations, and the estimation of input data are discussed, indicating the challenges and opportunities to scale HydroPol2D into the reality of data scarcity of larger poorly-gauged catchments. For a 1-yr return period of rainfall and antecedent dry days, and assuming an uncertainty of 40% in the water quality parameters, the results indicate that the maximum concentration of total suspended solids (TSS), the maximum load and the mass of the pollutant washed in 30% of the volume are, 456±260 mgL-1km-2, 2.56±0.4 kgs-1km-2, and 89%±10%, respectively.

Data assimilation for urban stormwater and water quality simulations using deep reinforcement learning

Hydrological models have been used to understand the transportation of water quantity and quality in drainage systems, and the stormwater management model (SWMM) is one of the most widely-used models for runoff quantity and quality simulations in urban areas. Although significant efforts have been made to identify the appropriate input parameters of the SWMM model in various watersheds, it is difficult to reflect the variability of the real environment using a fixed input parameter. Data assimilation (DA) is a compatibility method that improves prediction accuracy by merging observations and simulation outputs. It is useful to reduce the forecast error from temporal transferability with information interaction between the model output and observation data. However, conventional DA approaches cannot completely overcome their unrealistic assumptions such as linearity, normality, and error covariances. To address these challenges, we used deep reinforcement learning (RL) to develop an automatic assimilation model that interactively optimizes the SWMM parameters in a real environment (SWMM-RL). In the SWMM-RL model, the agent is trained by rewarding and/or punishing the action (modulated SWMM input parameter) in real time according to the state changes. The model was constructed to minimize the error of runoff and pollutant load simulation of suspended solids (SS), total nitrogen (TN), and total phosphorus (TP) in each stormwater monitoring event. The results demonstrated that the SWMM-RL model primarily outperformed the fixed input parameter SWMM model (SWMM-PS) in simulating the runoff and the three different pollutant loads (the median value of Nash–Sutcliffe efficiency (NSE) for 10 stormwater events was increased by 0.11, 0.31, 0.36, and 0.07 for runoff, SS, TN, and TP, respectively). The SWMM-PS has disadvantages in simulating low rainfall events because of the high sensitivity of runoff peaks to heavy rainfall conditions. Furthermore, the effects of extreme rainfall events were estimated using the SWMM-RL model. This study showed how SWMM-RL combined with the DA method increased forecast accuracy by providing sensitivity and temporal transitions of input parameters.

Remote sensing-enabled machine learning for river water quality modeling under multidimensional uncertainty

Two fundamental problems have inhibited progress in the simulation of river water quality under climate (and other) uncertainty: 1) insufficient data, and 2) the inability of existing models to account for the complexity of factors (e.g., hydro-climatic, basin characteristics, land use features) affecting river water quality. To address these concerns this study presents a technique for augmenting limited ground-based observations of water quality variables with remote-sensed surface reflectance data by leveraging a machine learning model capable of accommodating the multidimensionality of water quality influences. Total Suspended Solids (TSS) can serve as a surrogate for chemical and biological pollutants of concern in surface water bodies. Historically, TSS data collection in the United States has been limited to the location of water treatment plants where state or federal agencies conduct regularly-scheduled water sampling. Mathematical models relating riverine TSS concentration to the explanatory factors have therefore been limited and the relationships between climate extremes and water contamination events have not been effectively diagnosed. This paper presents a method to identify these issues by utilizing a Long Short-Term Memory Network (LSTM) model trained on Moderate Resolution Imaging Spectroradiometer (MODIS) satellite reflectance data, which is calibrated to TSS data collected by the Ohio River Valley Water Sanitation Commission (ORSANCO). The methodology developed enables a thorough empirical analysis and data-driven algorithms able to account for spatial variability within the watershed and provide effective water quality prediction under uncertainty.

Predicting Water Quality Distribution of Lakes through Linking Remote Sensing–Based Monitoring and Machine Learning Simulation

The present study links monitoring and simulation models to predict water quality distribution in lakes using an optimized neural network and remote sensing data processing. Two data driven models were developed. First, a monitoring model was established that is able to convert spectral images to TDS distribution. Moreover, a simulation model was developed to generate a TDS distribution map for unseen scenarios for which no spectral images are available. Outputs of the monitoring model were applied as the observations for training the simulation model. The Nash–Sutcliffe model efficiency coefficient (NSE) was utilized in the system performance measurement of the models. Based on the results in the case study, the monitoring model was sufficiently robust to convert the operational land imager spectral bands of Landsat 8 to the TDS distribution map. The NSE was more than 0.6 for the monitoring model, which confirms the predictive skills of the model. Furthermore, the simulation model was highly reliable in generating the TDS distribution map of the lakes. Three tests were carried out to demonstrate the reliability of the model. When comparing the results of the monitoring model and simulation model, an NSE of more than 0.6 was found for all the tests. It is recommendable to apply the proposed method instead of conventional hydrodynamic models that might be highly time consuming for simulating water quality parameters distribution in lakes. Low computational complexity is the main advantage of the proposed method.

An Improved Model for Water Quality Management Accounting for the Spatiotemporal Benthic Flux Rate

Although water quality models provide useful interpretations for water quality management, it is critical to accurately input and simulate the flux rate, which varies with space and time. In the Environmental Fluid Dynamics Code model, the flux rate value set does not consider spatiotemporal variability. The water quality of the Saemangeum freshwater lake in Korea is poor despite quality improvement measures. In this study, the model was improved by considering the characteristics of flux rates that change spatiotemporally based on environmental conditions and factors influencing the benthic layer. An exponential relational expression was generated and applied to the model while considering the aerobic, anaerobic, and influencing factors. Results from four important sites in the Saemangeum Reservoir were compared with the RSR, %Difference, and AME results of the previous model for evaluating the reproducibility of the improved model. Calibration and verification of the model were performed in 2013 and 2016, respectively. The improved model yielded values close to the optimal value after computing the evaluation functions of both models. It had excellent reproducibility and simulated water quality by reflecting a reasonable value for the benthic flux rate. The improved model can be extended to evaluate other water bodies in the future.

Application of QUAL2K Model for Simulating Water Quality in Hilla River, Iraq

Application of QUAL2K Model for Simulating Water Quality in Hilla River, Iraq

Assessing land use changes' effect on river water quality in the Dez Basin using land change modeler.

Changes in land use due to urbanization, industrialization, and agriculture will adversely affect water quality at all scales. This study examined the possible effects of future land use on the water quality of the Dez River located in Iran. The QUAL2Kw dynamic model was used to simulate the water quality of the Dez River. Data and information available in July 2019 and 2013 were used for calibration and validation. According to the comparison of the RMSE, RMSE%, and percent bias error indices for the model during the calibration and validation period, the QUAL2Kw model of Dez River had high accuracy with acceptable values of errors. The land use changes in the Dez river basin were modeled and predicted by the LCM model after simulating water quality. The images from Landsat 8/OLI were used for 2013, 2016, and 2019, respectively. Based on the accurate evaluation of classified images, Kappa coefficients for 2013, 2016, and 2019 were 88.19, 87.46, and 89.91, respectively. Modeling land use and land cover changes was conducted to predict 2030. As a result of the study, agricultural and built-up areas and water bodies will increase in 2030. The possible effects of land use changes in 2030 on river water quality were examined as a final step. Based on the results of the water quality simulation in 2030, biochemical oxygen demand, chemical oxygen demand, and NO3 parameters exceeded the maximum permissible level of drinking standard. This study recommends frequent water quality monitoring and LULC planning and management to reduce pollution in river basins.

Bayesian Optimization of Booster Disinfection Scheduling in Water Distribution Networks

Chlorine remains the most widely used disinfectant in drinking water treatment and distribution systems worldwide. To maintain a minimum residual throughout the distribution network, chlorine dosage needs to be regulated by optimizing the locations of chlorine boosters and their scheduling (i.e., chlorine injection rates). Such optimization can be computationally expensive since it requires numerous evaluations of water quality (WQ) simulation models. In recent years, Bayesian optimization (BO) has garnered considerable attention due to its efficiency in optimizing black-box functions in a wide range of applications. This study presents the first attempt to implement BO for the optimization of WQ in water distribution networks. The developed python-based framework couples BO with EPANET-MSX to optimize the scheduling of chlorine sources, while ensuring the delivery of water that satisfies water quality standards. Using Gaussian process regression to build the BO surrogate model, a comprehensive analysis was conducted to evaluate the performance of different BO methods. To that end, systematic testing of different acquisition functions, including the probability of improvement, expected improvement, upper confidence bound, and entropy search, in conjunction with different covariance kernels, including Matérn, squared-exponential, gamma-exponential, and rational quadratic, was conducted. Additionally, a thorough sensitivity analysis was performed to understand the influence of different BO parameters, including the number of initial points, covariance kernel length scale, and the level of exploration vs exploitation. The results revealed substantial variability in the performance of different BO methods and showed that the choice of the acquisition function has a more profound influence on the performance of BO than the covariance kernel.

Seismic resilience evaluation of water distribution systems considering hydraulic and water quality performance

The resilience evaluation of infrastructures provides an essential basis for resilience enhancement and disaster mitigation in urban communities. This study presents a seismic resilience evaluation framework of water distribution systems (WDSs) that integrates hydraulic with water quality simulation. The seismic damage scenarios of WDSs are generated according to the seismic fragility model of pipelines and Monte Carlo simulation, and the post-earthquake recovery of pipeline damages is simulated by a dynamic importance-based recovery sequence. The negative influence of earthquake hazards on water quality is investigated by the reduction of chlorine concentration in water, which depends on the changes in water age and supply path caused by pipeline damages. The water quality performance of WDSs is measured by the residual chlorine concentration at user nodes, and two water quality performance indexes, namely, the water supply that meets the quality threshold requirements and the water supply without quality deterioration after the earthquake. The proposed framework is implemented in three benchmark WDSs with different layouts and operational features. Application results show that the water quality resilience of WDSs tends to be lower than that of hydraulic services, and the relative difference in system resilience loss between the water quality and hydraulic service performance ranges from 17% to 286%, which varied according to the layout of WDSs. The water source redundancy, and not the loop pattern of the pipeline network, has crucial impacts on the water quality resilience of WDSs after an earthquake event.

Simulation of Surface and Subsurface Water Quality in Hyper-Arid Environments

Forty-eight water samples (30 groundwater and 18 surface water samples) were collected from the study region. Physical and chemical examinations were performed on the water samples to determine the values of various variables. Several graphs, sheets, and statistical measures, including the sodium solubility percentage (SSP), the sodium absorption ratio (SAR), and Piper’s diagram, were used to plot the concentration of the principal ions and the chloride mass balance (CMB). The contents of the variables were compared with the contents in other local areas and the standard allowable safe limits as recommended by the World Health Organization (WHO). Water pH values were neutral for all water samples. Electric conductivity (EC) readings revealed that water samples vacillated from slightly mineralized to excessively mineralized. Water salinities were fresh and very fresh according to the total dissolved solids (TDS) amounts. The hardness of water ranged from medium to hard in the surface water and from medium to very hard in the groundwater samples. Bicarbonate, sodium, and calcium made up the highest amounts in the surface water samples. The highest concentrations of bicarbonate, sulfate, chloride, and sodium were found in the groundwater. Diagrams show the major ion relationships as well as the type and origin of the water. According to Piper’s plots, most of the water samples under investigation were Ca-HCO3 type, Mg water types, followed by SO4.Ca-Cl water types. This highlighted the elemental preponderance of bicarbonate and alkaline earth (Ca2+ + Mg2+). This dominance is caused by evaporite and carbonate minerals dissolving in water because of anthropogenic activities and interaction processes. The groundwater recharge was estimated to be 0.89–1.6 mm/yr based on Chloride Mass Balance. The examined water samples can also be used for cattle, poultry, and irrigation. Additionally, the groundwater is of poorer quality than the surface water, although both types of water are adequate for various industries, with a range of 14 to 94 percent. With the exception of a few groundwater samples, the tested water samples are suitable for a number of applications.

Optimization of injection costs for water distribution systems under double-sided fuzziness

ABSTRACT Booster disinfectant can help control the growth of microorganism in water distribution systems (WDS). To minimize the booster costs under uncertainty, a double-sided fuzziness chance-constrained programming (DFCCP) model was proposed and applied to two cases. The fuzzy nodal response coefficients were obtained by water quality simulation performed by EPANET simulator with consideration of uncertain parameters. The results indicated that the booster costs increased with the confidence level under both maximum and minimum reliability scenarios. Moreover, the booster costs under maximum reliability are greater than those under minimum reliability. In addition, under minimum reliability the booster costs decreased with the uncertainty level for two cases, while under maximum reliability the booster costs increased or decreased with the uncertainty level, which depends on the variation of nodes with minimum response coefficient. The results obtained can help managers make decisions in a complex consideration of costs and risks.

Water Quality Simulation in the Bois River, Goiás, Central Brazil

The Cerrado is a hotspot for biodiversity conservation and holds the headwater springs that are major Brazilian river basins. The development of industry, agriculture, and mining causes water quality deterioration. Mathematical models appear as a management tool to simulate water quality parameters and the dispersion of pollutants in water bodies. Thus, this study aimed to evaluate the behavior of dissolved oxygen (DO) and other parameters through the QUAL2Kw (Stream Water Quality Model) model in a river in the Brazilian Cerrado. Complementary data were obtained in four experimental measurement campaigns. The calibration results showed a good fit, especially for the DO. The most critical situation occurred in October, where DO remained below 5 mg/L for a long stretch, and the ammoniacal nitrogen (NH4) and biochemical oxygen demand (BOD) presented non-compliance concerning the legal Brazilian requirements. In all campaigns, BOD remained above 5 mg/L for at least 5 km in length, disagreeing with the legislation for exceeding the distance from the mixing zone. The uncertainty analysis for the DO confirmed the critical scenario of October, and the sensitivity analysis by the Monte Carlo Simulation showed the significance of the reaeration coefficient for DO. Thus, it is concluded that the QUAL2Kw model proved dependable for the simulation of point launches in the Bois River, supplying a good fit in the calibration act. Because BOD does not meet the legal requirements in all samplings, the water use of the downstream population may be impaired by the activities found in the basin. Activities such as sand extraction, tanneries, and other food industries increase the organic burden of waterbodies and, therefore, require greater environmental inspections.

Multi-objective optimization of hydrant flushing in a water distribution system using a fast hybrid technique

As a critical element in preserving the health of urban populations, water distribution systems (WDSs) must be ready to implement emergency plans when catastrophic events such as contamination events occur. A risk-based simulation-optimization framework (EPANET–NSGA-III) combined with a decision support model (GMCR) is proposed in this study to determine optimal locations for contaminant flushing hydrants under an array of potentially hazardous scenarios. Risk-based analysis using Conditional Value-at-Risk (CVaR)-based objectives can address uncertainties regarding the mode of WDS contamination, thereby providing a robust plan to minimize the associated risks at a 95% confidence level. Conflict modeling by GMCR achieved an optimal compromise solution within the Pareto front by identifying a final stable consensus among the decision-makers involved. A novel hybrid contamination event grouping-parallel water quality simulation technique was incorporated into the integrated model to reduce model runtime, the main deterrent in optimization-based methods. The nearly 80% reduction in model runtime made the proposed model a viable solution for online simulation-optimization problems. The framework's capacity to address real-world problems was evaluated for the WDS operating in Lamerd, a city in Fars Province, Iran. Results showed that the proposed framework was capable of highlighting a single flushing strategy, which not only optimally reduced risks associated with contamination events, but provided acceptable coverage against such threats, flushing 35–61.3% of input contamination mass on average, and reducing average time-to-return to normal conditions by 14.4–60.2%, while employing less than half of the initial potential hydrants.

Application of wavelet theory to enhance the performance of machine learning techniques in estimating water quality parameters (case study: Gao-Ping River).

There are several methods for modeling water quality parameters, with data-based methods being the focus of research in recent decades. The current study aims to simulate water quality parameters using modern artificial intelligence techniques, to enhance the performance of machine learning techniques using wavelet theory, and to compare these techniques to other widely used machine learning techniques. EC, Cl, Mg, and TDS water quality parameters were modeled using artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). The study area in the present research is Gao-ping River in Taiwan. In the training state, using hybrid models with wavelet transform improved the accuracy of ANN models from 8.1 to 22.5% and from 25.7 to 55.3% in the testing state. In addition, wavelet transforms increased the ANFIS model's accuracy in the training state from 6.7 to 18.4% and in the testing state from 9.9 to 50%. Using wavelet transform improves the accuracy of machine learning model results. Also, the WANFIS (Wavelet-ANFIS) model was superior to the WANN (Wavelet-ANN) model, resulting in more precise modeling for all four water quality parameters.

Effect of the Proposed Outlets on the Hydrodynamic Behavior and Water Quality of the South-West Part of the Al Hammar Marsh

The current study aims to find a new plan to manage the water quality of the western part of the Hammar Marsh to reduce the salts that cause problems for the marshes and preserve their environmental life by isolating the southwestern part of the Hammar Marsh by closing the outlet under the railway embankment. The outlet is discharging saline water to the east-western part of Al Hammar Marsh. After isolating the southwestern part of the marsh, a new outlet is proposed. The impact of the flow hydrodynamics on improving the water quality was simulated using the SMS model. The hydrodynamics and water quality simulation models for the marsh are : a hydrodynamic model and average depth (SMS RMA2) and a two-dimensional water quality model (SMS RM4). The Civil3D was used to determine the area elevation curve for the marsh, while the shape file for the study area was prepared using the Arc-GIS model. The first model is used to simulate the flow conditions in the marsh (water depth and velocity vectors), while the second model is used to simulate the salinity of the water (Total Dissolved Solids (TDS)). For calibration and verification the models, water samples were taken from ten selected locations within the marsh. The measurements were conducted on 1st January and on 2nd February 2022. The simulation results were validated with the field measurement, and the discrepancy between the simulated and measured water depth was found to be 11%. Many scenarios are based on the proposed recommended outlet that considerably reduces TDS concentration in the Al Hammar marsh. Three scenarios were run on the proposed outlet to maintain a submerged area of 88 km2, and to compare the three scenarios, ten points were selected in different locations, where the average TDS ratio for the first scenario was 7528 mg/l, the second scenario was 6982 mg/l and for the third scenario was 8069 mg/l. Results showed that the proposed outlet would improve the hydrodynamics of the flow and reduce the TDS concentration by 10% in addition to controlling the contamination of east western part of the marsh