Enhancing groundwater quality assessment in coastal area: A hybrid modeling approach.

Monitoring of groundwater (GW) resources in coastal areas is vital for human needs, agriculture, ecosystems, securing water supply, biodiversity, and environmental sustainability. Although the utilization of water quality index (WQI) models has proven effective in monitoring GW resources, it has faced substantial criticism due to its inconsistent outcomes, prompting the need for more reliable assessment methods. Therefore, this study addressed this concern by employing the data-driven root mean squared (RMS) models to evaluate groundwater quality (GWQ) in the coastal Bhola district near the Bay of Bengal, Bangladesh. To enhance the reliability of the RMS-WQI model, the research incorporated the extreme gradient boosting (XGBoost) machine learning (ML) algorithm. For the assessment of GWQ, the study utilized eleven crucial indicators, including turbidity (TURB), electric conductivity (EC), pH, total dissolved solids (TDS), nitrate (NO3 -), ammonium (NH4 +), sodium (Na), potassium (K), magnesium (Mg), calcium (Ca), and iron (Fe). In terms of the GW indicators, concentration of K, Ca and Mg exceeded the guideline limit in the collected GW samples. The computed RMS-WQI scores ranged from 54.3 to 72.1, with an average of 65.2, categorizing all sampling sites' GWQ as "fair." In terms of model reliability, XGBoost demonstrated exceptional sensitivity (R2 = 0.97) in predicting GWQ accurately. Furthermore, the RMS-WQI model exhibited minimal uncertainty (<1 %) in predicting WQI scores. These findings implied the efficacy of the RMS-WQI model in accurately assessing GWQ in coastal areas, that would ultimately assist regional environmental managers and strategic planners for effective monitoring and sustainable management of coastal GW resources.

Mapping reservoir water quality from Sentinel-2 satellite data based on a new approach of weighted averaging: Application of Bayesian maximum entropy.

In regions like Oman, which are characterized by aridity, enhancing the water quality discharged from reservoirs poses considerable challenges. This predicament is notably pronounced at Wadi Dayqah Dam (WDD), where meeting the demand for ample, superior water downstream proves to be a formidable task. Thus, accurately estimating and mapping water quality indicators (WQIs) is paramount for sustainable planning of inland in the study area. Since traditional procedures to collect water quality data are time-consuming, labor-intensive, and costly, water resources management has shifted from gathering field measurement data to utilizing remote sensing (RS) data. WDD has been threatened by various driving forces in recent years, such as contamination from different sources, sedimentation, nutrient runoff, salinity intrusion, temperature fluctuations, and microbial contamination. Therefore, this study aimed to retrieve and map WQIs, namely dissolved oxygen (DO) and chlorophyll-a (Chl-a) of the Wadi Dayqah Dam (WDD) reservoir from Sentinel-2 (S2) satellite data using a new procedure of weighted averaging, namely Bayesian Maximum Entropy-based Fusion (BMEF). To do so, the outputs of four Machine Learning (ML) algorithms, namely Multilayer Regression (MLR), Random Forest Regression (RFR), Support Vector Regression (SVRs), and XGBoost, were combined using this approach together, considering uncertainty. Water samples from 254 systematic plots were obtained for temperature (T), electrical conductivity (EC), chlorophyll-a (Chl-a), pH, oxidation-reduction potential (ORP), and dissolved oxygen (DO) in WDD. The findings indicated that, throughout both the training and testing phases, the BMEF model outperformed individual machine learning models. Considering Chl-a, as WQI, and R-squared, as evaluation indices, BMEF outperformed MLR, SVR, RFR, and XGBoost by 6%, 9%, 2%, and 7%, respectively. Furthermore, the results were significantly enhanced when the best combination of various spectral bands was considered to estimate specific WQIs instead of using all S2 bands as input variables of the ML algorithms.

Long-term resilience in wastewater management: Optimizing treated wastewater allocation with a dynamic multi-agent approach.

Water scarcity poses a significant challenge to sustainable development, necessitating innovative approaches to manage limited resources efficiently. Effective water resource management involves not just the conservation and distribution of freshwater supplies but also the strategic reuse of treated wastewater (TWW). This study proposes a novel approach for the optimal allocation of treated wastewater among three key sectors (user agents): agriculture, industry, and urban green space. Recognizing the intricate interplays among these sectors, System Dynamics (SD) and Agent-Based Modeling (ABM) were integrated in a Complex Adaptive System (CAS) to capture the interactions and feedback mechanisms inherent within treated wastewater allocation systems. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) serves as the optimization tool, enabling the identification of optimal allocation strategies across various management scenarios over a 25-year simulation period. Our research navigates the complexities of long-term resource management, accounting for each sector's evolving its objectives and guidelines along the whole system objectives and strategies. The outcomes demonstrate how treated wastewater can be effectively distributed to support economic and social equity -as the system objectives-while supporting agricultural and industrial growth and enhancing efficiency and social well-being -reflecting individual agent objectives-within the CAS framework. The research explores four distinct management scenarios, each prioritizing different sectors to address water resource management challenges. Notably, all four scenarios align with the strategies required by the ruler (government), providing strategic guidance to water resource managers for decision-making. The simulation results reveal a scenario where all sectors' demands are met, with Scenario 4 emerging as the most effective. Scenario 4 aligned with the objectives and guidelines of each sector, demonstrating significant improvements in the CY (Agriculture agent index; increased from 0.2 to 0.68), IGI (Industry agent index; increased from 1 to 1.63), and GAI (Urban Green Space agent index; increased from 1 to 1.23) indices over the 25-year simulation period. By providing a strategic blueprint for policymakers and stakeholders, this study contributes significantly to the discourse on sustainable water resource management, presenting a replicable model for similar contexts globally, where judicious allocation of treated wastewater is paramount for achieving harmony between human activity and ecological preservation.

Performance evaluation of a new sponge-based moving bed biofilm reactor for the removal of pharmaceutical pollutants from real wastewater.

Pharmaceutical pollutants, a group of emerging contaminants, have attracted outstanding attention in recent years, and their removal from aquatic environments has been addressed. In the current study, a new sponge-based moving bed biofilm reactor (MBBR) was developed to remove chemical oxygen demand (COD) and the pharmaceutical compound Ibuprofen (IBU). A 30-L pilot scale MBBR was constructed, which was continuously fed from the effluent of the first clarifier of the Southern Tehran wastewater treatment plant. The controlled operational parameters were pH in the natural range, Dissolved Oxygen of 1.5-2 mg/L, average suspended mixed liquor suspended solids (MLSS), and mixed liquor volatile suspended solids (MLVSS) of 1.68 ± 0.1 g/L and 1.48 ± 0.1 g/L, respectively. The effect of hydraulic retention time (HRT) (5 h, 10 h, 15 h), filling ratio (10%, 20%, 30%), and initial IBU concentration (2 mg/L, 5 mg/L, 10 mg/L) on removal efficiencies was assessed. The findings of this study revealed a COD removal efficiency ranging from 48.9 to 96.7%, with the best removal efficiency observed at an HRT of 10 h, a filling ratio of 20%, and an initial IBU concentration of 2 mg/L. Simultaneously, the IBU removal rate ranged from 25 to 92.7%, with the highest removal efficiency observed under the same HRT and filling ratio, albeit with an initial IBU concentration of 5 mg/L. An extension of HRT from 5 to 10 h significantly improved both COD and IBU removal. However, further extension from 10 to 15 h slightly enhanced the removal efficiency of COD and IBU, and even in some cases, removal efficiency decreased. Based on the obtained results, 20% of the filling ratio was chosen as the optimum state. Increasing the initial concentration of IBU from 2 to 5 mg/L generally improved COD and IBU removal, whereas an increase from 5 to 10 mg/L caused a decline in COD and IBU removal. This study also optimized the reactor's efficiency for COD and IBU removal by using response surface methodology (RSM) with independent variables of HRT, filling ratio, and initial IBU concentration. In this regard, the quadratic model was found to be significant. Utilizing the central composite design (CCD), the optimal operating parameters at an HRT of 10 h, a filling ratio of 21%, and an initial IBU concentration of 3 mg/L were pinpointed, achieving the highest COD and IBU removal efficiencies. The present study demonstrated that sponge-based MBBR stands out as a promising technology for COD and IBU removal.

Robust clustering-based hybrid technique enabling reliable reservoir water quality prediction with uncertainty quantification and spatial analysis.

Machine learning methodology has recently been considered a smart and reliable way to monitor water quality parameters in aquatic environments like reservoirs and lakes. This study employs both individual and hybrid-based techniques to boost the accuracy of dissolved oxygen (DO) and chlorophyll-a (Chl-a) predictions in the Wadi Dayqah Dam located in Oman. At first, an AAQ-RINKO device (CTD+ sensor) was used to collect water quality parameters from different locations and varying depths in the reservoir. Second, the dataset is segmented into homogeneous clusters based on DO and Chl-a parameters by leveraging an optimized K-means algorithm, facilitating precise estimations. Third, ten sophisticated variational-individual data-driven models, namely generalized regression neural network (GRNN), random forest (RF), gaussian process regression (GPR), decision tree (DT), least-squares boosting (LSB), bayesian ridge (BR), support vector regression (SVR), K-nearest neighbors (KNN), multilayer perceptron (MLP), and group method of data handling (GMDH) are employed to estimate DO and Chl-a concentrations. Fourth, to improve prediction accuracy, bayesian model averaging (BMA), entropy weighted (EW), and a new enhanced clustering-based hybrid technique called Entropy-ORNESS are employed to combine model outputs. The Entropy-ORNESS method incorporates a Genetic Algorithm (GA) to determine optimal weights and then combine them with EW weights. Finally, the inclusion of bootstrapping techniques introduces a stochastic assessment of model uncertainty, resulting in a robust estimator model. In the validation phase, the Entropy-ORNESS technique outperforms the independent models among the three fusion-based methods, yielding R2 values of 0.92 and 0.89 for DO and Chl-a clusters, respectively. The proposed hybrid-based methodology demonstrates reduced uncertainty compared to single data-driven models and two combination frameworks, with uncertainty levels of 0.24% and 1.16% for cluster 1 of DO and cluster 2 of Chl-a parameters. As a highlight point, the spatial analysis of DO and Chl-a concentrations exhibit similar pattern variations across varying depths of the dam according to the comparison of field measurements with the best hybrid technique, in which DO concentration values notably decrease during warmer seasons. These findings collectively underscore the potential of the upgraded weighted-based hybrid approach to provide more accurate estimations of DO and Chl-a concentrations in dynamic aquatic environments.

The effect of extracorporeal shock-wave therapy on pain in patients with various tendinopathies: a systematic review and meta-analysis of randomized control trials.

OBJECTIVES: Tendinopathy is a common condition that affects the body's tendon structures, causing discomfort, restricted movement, and reduced functionality. In this study, we looked at how extracorporeal shock wave therapy (ESWT) affected pain levels in individuals with various forms of tendinopathy around the world. DESIGN: This study is a comprehensive review and meta-analysis of previously published randomized controlled trials. To gather relevant data, the researchers performed keyword searches in international databases, including PubMed (Medline), Scopus, Web of Sciences, Cochrane Central Register of Controlled Trials (CENTRAL), Research Registers of ongoing trials (ClinicalTrials.gov), as well as Embase. The search was conducted up until March 2023. The quality of the selected articles was assessed using the Cochrane risk-of-bias method for randomized trials (RoB2). RESULTS: Based on the results of the meta-analysis, which included 45 clinical studies, the use of ESWT was found to have a significant impact on reducing pain in various conditions. The standardized mean difference (SMD) in patients with plantar fasciitis (PF) was reduced by 1.63 (SMD: -1.63, 95% CI: -3.04, -0.21; I2: 77.36%; P heterogeneity: 0.0001). For lateral epicondylitis (LE), the SMD was 0.63 (SMD: -0.63, 95% CI: -1.11, -0.16; I2: 67.50%; P heterogeneity: 0.003). In the case of chronic Achilles tendinopathy, the SMD was 1.38 (SMD: -1.38, 95% CI: -1.66, -1.10; I2: 96.44%; P heterogeneity: 0.0001). Additionally, in individuals with rotator cuff tendinopathy, the SMD for pain reduction was 2.37 units (SMD: -2.37, 95% CI: -3.58, -1.15; I2: 98.46%; P heterogeneity: 0.0001). CONCLUSION: This study suggests that ESWT can be a highly effective therapy option for relieving pain in people with tendinopathy. Nonetheless, it is encouraged to make additional recommendations based on high-quality clinical research and more accurate information in order to define the optimal therapeutic options for each type of tendinopathy.

Hybrid WT-CNN-GRU-based model for the estimation of reservoir water quality variables considering spatio-temporal features.

Water quality indicators (WQIs), such as chlorophyll-a (Chl-a) and dissolved oxygen (DO), are crucial for understanding and assessing the health of aquatic ecosystems. Precise prediction of these indicators is fundamental for the efficient administration of rivers, lakes, and reservoirs. This research utilized two unique DL algorithms-namely, convolutional neural network (CNNs) and gated recurrent units (GRUs)-alongside their amalgamation, CNN-GRU, to precisely gauge the concentration of these indicators within a reservoir. Moreover, to optimize the outcomes of the developed hybrid model, we considered the impact of a decomposition technique, specifically the wavelet transform (WT). In addition to these efforts, we created two distinct machine learning (ML) algorithms-namely, random forest (RF) and support vector regression (SVR)-to demonstrate the superior performance of deep learning algorithms over individual ML ones. We initially gathered WQIs from diverse locations and varying depths within the reservoir using an AAQ-RINKO device in the study area to achieve this. It is important to highlight that, despite utilizing diverse data-driven models in water quality estimation, a significant gap persists in the existing literature regarding implementing a comprehensive hybrid algorithm. This algorithm integrates the wavelet transform, convolutional neural network (CNN), and gated recurrent unit (GRU) methodologies to estimate WQIs accurately within a spatiotemporal framework. Subsequently, the effectiveness of the models that were developed was assessed utilizing various statistical metrics, encompassing the correlation coefficient (r), root mean square error (RMSE), mean absolute error (MAE), and Nash-Sutcliffe efficiency (NSE) throughout both the training and testing phases. The findings demonstrated that the WT-CNN-GRU model exhibited better performance in comparison with the other algorithms by 13% (SVR), 13% (RF), 9% (CNN), and 8% (GRU) when R-squared and DO were considered as evaluation indices and WQIs, respectively.

Efficient implicit constraint handling approaches for constrained optimization problems.

Many real-world optimization problems, particularly engineering ones, involve constraints that make finding a feasible solution challenging. Numerous researchers have investigated this challenge for constrained single- and multi-objective optimization problems. In particular, this work extends the boundary update (BU) method proposed by Gandomi and Deb (Comput. Methods Appl. Mech. Eng. 363:112917, 2020) for the constrained optimization problem. BU is an implicit constraint handling technique that aims to cut the infeasible search space over iterations to find the feasible region faster. In doing so, the search space is twisted, which can make the optimization problem more challenging. In response, two switching mechanisms are implemented that transform the landscape along with the variables to the original problem when the feasible region is found. To achieve this objective, two thresholds, representing distinct switching methods, are taken into account. In the first approach, the optimization process transitions to a state without utilizing the BU approach when constraint violations reach zero. In the second method, the optimization process shifts to a BU method-free optimization phase when there is no further change observed in the objective space. To validate, benchmarks and engineering problems are considered to be solved with well-known evolutionary single- and multi-objective optimization algorithms. Herein, the proposed method is benchmarked using with and without BU approaches over the whole search process. The results show that the proposed method can significantly boost the solutions in both convergence speed and finding better solutions for constrained optimization problems.

Assessing vulnerability of coastal aquifer to seawater intrusion using Convolutional Neural Network.

This study examined coastal aquifer vulnerability to seawater intrusion (SWI) in the Shiramin area in northwest Iran. Here, six types of hydrogeological data layers existing in the traditional GALDIT framework (TGF) were used to build one vulnerability map. Moreover, a modified traditional GALDIT framework (mod-TGF) was prepared by eliminating the data layer of aquifer type from the GALDIT model and adding the data layers of aquifer media and well density. To the best of our knowledge, there is a research gap to improve the TGF using deep learning algorithms. Therefore, this research adopted the Convolutional Neural Network (CNN) as a new deep learning algorithm to improve the mod-TGF framework for assessing the coastal aquifer vulnerability. Based on the findings, the CNN model could increase the performance of the mod-TGF by >30 %. This research can be a reference for further aquifer vulnerability studies.

Optimal waste load allocation in river systems based on a new multi-objective cuckoo optimization algorithm.

Water pollution escalates with rising waste discharge in river systems, as the rivers' limited pollution tolerance and constrained self-cleaning capacity compel the release of treated pollutants. Although several studies have shown that the non-dominated sorting genetic algorithm-II (NSGA-II) is an effective algorithm regarding the management of river water quality to reach water quality standards, to our knowledge, the literature lacks using a new optimization model, namely, the multi-objective cuckoo optimization algorithm (MOCOA). Therefore, this research introduces a new optimization framework, including non-dominated sorting and ranking selection using the comparison operator densely populated towards the best Pareto front and a trade-off estimation between the goals of discharges and environmental protection authorities. The suggested algorithm is implemented for a waste load allocation issue in Jajrood River, located in the North of Iran. The limitation of this research is that discharges are point sources. To analyze the performance of the new optimization algorithm, the simulation model is linked with a hybrid optimization model using a cuckoo optimization algorithm and non-dominated sorting genetic algorithms to convert a single-objective algorithm to a multi-objective algorithm. The findings indicate that, in terms of violation index and inequity values, MOCOA's Pareto front is superior to NSGA-II, which highlights the MOCOA's effectiveness in waste load allocation. For instance, with identical population sizes and violation indexes for both algorithms, the optimal Pareto front ranges from 1.31 to 2.36 for NSGA-II and 0.379 to 2.28 for MOCOA. This suggests that MOCOA achieves a superior Pareto front in a more efficient timeframe. Additionally, MOCOA can attain optimal equity in the smaller population size.

Forecasting water quality variable using deep learning and weighted averaging ensemble models.

Water quality variables, including chlorophyll-a (Chl-a), play a pivotal role in comprehending and evaluating the condition of aquatic ecosystems. Chl-a, a pigment present in diverse aquatic organisms, notably algae and cyanobacteria, serves as a valuable indicator of water quality. Thus, the objectives of this study encompass: (1) the assessment of the predictive capabilities of four deep learning (DL) models - namely, recurrent neural network (RNN), long short-term memory (LSTM), gated recurrence unit (GRU), and temporal convolutional network (TCN) - in forecasting Chl-a concentrations; (2) the incorporation of these DL models into ensemble models (EMs) employing genetic algorithm (GA) and non-dominated sorting genetic algorithm (NSGA-II) to harness the strengths of each standalone model; and (3) the evaluation of the efficacy of the developed EMs. Utilizing data collected at 15-min intervals from Small Prespa Lake (SPL) in Greece, the models employed hourly Chl-a concentration lag times, extending up to 6 h, as models' inputs to forecast Chla (t+1). The proposed models underwent training on 70% of the dataset and were subsequently validated on the remaining 30%. Among the standalone DL models, the GRU model exhibited superior performance in Chl-a forecasting, surpassing the RNN, LSTM, and TCN models by 8%, 2%, and 2%, respectively. Furthermore, the integration of DL models through single-objective GA and multi-objective NSGA-II optimization algorithms yielded hybrid models adept at effectively forecasting both low and high Chl-a concentrations. The ensemble model based on NSGA-II outperformed standalone DL models as well as the GA-based model across a range of evaluation indices. For instance, considering the R-squared metric, the study's findings demonstrated that the EM-NSGA-II stands out with exceptional effectiveness compared to DL and EM-GA models, showcasing improvements of 14% (RNN), 8% (LSTM), 6% (GRU), 8% (TCN), and 3% (EM-GA) during the testing phase.

Machine learning model optimization for removal of steroid hormones from wastewater.

In the past few decades, there has been a significant focus on detecting steroid hormones in aquatic environments due to their influence on the endocrine system. Most compounds of these pollutants are the natural steroidal estrogens, i.e., estrone (E1), 17ß-Estradiol (E2), and the synthetic estrogen 17α-Ethinylestradiol (EE2). The Moving-Bed Biofilm Reactor (MBBR) technique is appropriate for eliminating steroid hormones. This study centers on creating a model to estimate the effectiveness of the MBBR system regarding its ability to eliminate E1, E2, and EE2. The results were modeled with artificial neural networks (ANNs). The Particle Warm Optimization (PSO) and Levenberg Marquardt (LM) algorithms were selected for network training. The models incorporated five input parameters, encompassing the COD loading rate, initial levels of E1, E2, and EE2 steroid hormones, and Hydraulic Retention Time (HRT). The optimum removal conditions (three steroid hormones and COD) were determined using the optimized ANN based on both PSO and LM algorithms. The optimal transfer functions for the hidden and output layers were identified as tan-sigmoid and linear, respectively. The best ANN structures (Neurons in input, hidden, and output layers) and correlation coefficients (R) were 5:9:4, with R = 0.9978, and 5:10:4, with R = 0.9982 for the trained networks with LM and PSO algorithms, respectively. Eventually, the input parameters' importance was ranked using sensitivity analysis (SA) through Pearson correlation and developed ANNs.

Comparison of The Effectiveness of Exercise Therapy and Electroacupuncture in Patients with Forward Head Abnormalities and Myofascial Pain Syndrome: A Randomized Clinical Trial.

Background: Forward Head Posture (FHP), which refers to the head being more forward than the shoulder, is one of the most common postural defects of all ages. Therefore, in this study, we aimed to compare the effectiveness of exercise therapy and electroacupuncture in patients with FHP and myofascial pain syndrome (MPS). Methods: The present study was an open-label randomized clinical trial. A total of 61 patients with FHP and MPS who were referred to the physical medicine clinic of Besat Hospital between 2020 and 2021 were analyzed. Patients in one group were treated with electroacupuncture, and another one was treated with exercise therapy. The primary outcomes were FHP angles (CVA, CA, and shoulder angle), pain intensity (VAS), and quality of life (SF-12). Paired t-test was used to compare the results obtained in the pre-test and post-test. To detect differences over time, the analysis of variance models was used to repeat the observations. If the p-test result is less than the test significance level of 0.05, the null hypothesis is not confirmed. Results: The rate of final CVA and increase in CVA in the exercise therapy group were significantly higher than in the electroacupuncture group (P < 0.001). The average shoulder angle in the exercise therapy group increased from 47.1° ± 3.0° to 51.9° ± 3.3° (P < 0.001) and in the electroacupuncture group from 47.9° ± 3.1° to 51.0° ± 2.8° (P < 0.001). A significant difference was observed between the two groups in terms of pain intensity changes during the study. Conclusion: Overall, the results of this study showed that both exercise therapy and electroacupuncture significantly improved patients' posture, reduced pain intensity, and increased quality of life in FHP patients with MPS; But exercise therapy was more effective in improving FHP angles and electroacupuncture was more successful in reducing patients' pain intensity.

Entropy-based air quality monitoring network optimization using NINP and Bayesian maximum entropy.

Effectual air quality monitoring network (AQMN) design plays a prominent role in environmental engineering. An optimal AQMN design should consider stations' mutual information and system uncertainties for effectiveness. This study develops a novel optimization model using a non-dominated sorting genetic algorithm II (NSGA-II). The Bayesian maximum entropy (BME) method generates potential stations as the input of a framework based on the transinformation entropy (TE) method to maximize the coverage and minimize the probability of selecting stations. Also, the fuzzy degree of membership and the nonlinear interval number programming (NINP) approaches are used to survey the uncertainty of the joint information. To obtain the best Pareto optimal solution of the AQMN characterization, a robust ranking technique, called Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE) approach, is utilized to select the most appropriate AQMN properties. This methodology is applied to Los Angeles, Long Beach, and Anaheim in California, USA. Results suggest using 4, 4, and 5 stations to monitor CO, NO2, and ozone, respectively; however, implementing this recommendation reduces coverage by 3.75, 3.75, and 3 times for CO, NO2, and ozone, respectively. On the positive side, this substantially decreases TE for CO, NO2, and ozone concentrations by 8.25, 5.86, and 4.75 times, respectively.

A comparative study of data-driven models for runoff, sediment, and nitrate forecasting.

Effective prediction of qualitative and quantitative indicators for runoff is quite essential in water resources planning and management. However, although several data-driven and model-driven forecasting approaches have been employed in the literature for streamflow forecasting, to our knowledge, the literature lacks a comprehensive comparison of well-known data-driven and model-driven forecasting techniques for runoff evaluation in terms of quality and quantity. This study filled this knowledge gap by comparing the accuracy of runoff, sediment, and nitrate forecasting using four robust data-driven techniques: artificial neural network (ANN), long short-term memory (LSTM), wavelet artificial neural network (WANN), and wavelet long short-term memory (WLSTM) models. These comparisons were performed in two main tiers: (1) Comparing the machine learning algorithms' results with the model-driven approach; In order to simulate the runoff, sediment, and nitrate loads, the Soil and Water Assessment Tool (SWAT) model was employed, and (2) Comparing the machine learning algorithms with each other; The wavelet function was utilized in the ANN and LSTM algorithms. These comparisons were assessed based on the substantial statistical indices of coefficient of determination (R-Squared), Nash-Sutcliff efficiency coefficient (NSE), mean absolute error (MAE), and root mean square error (RMSE). Finally, to prove the applicability and efficiency of the proposed novel framework, it was successfully applied to Eagle Creek Watershed (ECW), Indiana, U.S. Results demonstrated that the data-driven algorithms significantly outperformed the model-driven models for both the calibration/training and validation/testing phases. Furthermore, it was found that the coupled ANN and LSTM models with wavelet function led to more accurate results than those without this function.

Comparison of the Effectiveness of Local Corticosteroid Injection and Extracorporeal Shockwave Therapy in Patients With Pes Anserine Bursitis: An Open-Label Randomized Clinical Trial.

Background: The success rate of extracorporeal shock wave therapy (ESWT) in treating epicondylitis, plantar fasciitis, rotator cuff tendonitis, Achilles tendonitis, and Jumper knee has been reported to be 60% to 80%. Most published studies have compared focused ESWT at different intensities with local corticosteroid injection (LCI). We only identified a few studies that specifically compared ESWT with LCI in patients with pes anserine bursitis (PAB). This study aimed to compare the effectiveness of ESWT and LCI in patients with PAB. Methods: The present study was a randomized clinical trial. Patients diagnosed with PAB who were referred to the physical medicine and rehabilitation clinic underwent a complete physical examination. They (n = 60 patients) were randomly assigned to the ESWT and LCI groups if they met the study criteria. In the ESWT group, 1 ESWT session was performed weekly for 3 consecutive weeks. In the LCI group, 1 injection was performed under an ultrasonography guide. Pes anserine thickness, pain intensity, and treatment satisfaction were measured with visual analog scale (VAS) and quality of life (Short Form-12). A paired-samples t test was used to compare the results obtained in the pre-and posttests. Analysis of variance for repeated measures was used to detect differences over time. The null hypothesis would not be confirmed if the P value was less than the 0.05 level of significance. Results: Pes anserine thickness and pain intensity decreased significantly during the study in both groups (P < 0.001). However, the mean difference of pes anserine thickness was more in the LCI group the ESWT group [(-0.6; 95% CI, -1.0 to -0.3) than (-0.1; 95% CI, -0.5, -0.2); P = 0.008]. Also, the mean difference of pain intensity was lower in the ESWT group] than the LCI group [(-2.9; 95% CI, -3.7 to -2.1) (1.0; 95% CI, 0.1to 1.8); P < 0.001]. Patients' quality of life in both groups increased significantly during the study period (P < 0.001), but the increase in quality of life in patients in the ESWT group (mean difference, 15.3 [95% CI, 10.6-19.9]) was considerably more than in the LCI group (mean difference, -5.3 (95% CI, -10.0 to -0.6). Conclusion: Overall, the results of this study showed that both local corticosteroid injections and extracorporeal shock wave therapy are safe and effective in PAB patients.

Non-stationary evaluation of runoff peaks in response to climate variability and land use change in Ferson Creek, Illinois, USA.

In this paper, we examine how surface runoff affects public safety and urban infrastructure worldwide and how human activity has significantly altered the frequency and magnitude of these events. We investigate this issue in Ferson Creek, IL, USA. Our study focuses on three specific areas of impact: (1) the primary reasons for a considerable increase in average runoff peaks, using annual maximum runoff discharge and annual maximum precipitation and temperature to evaluate the role of climate variability; (2) the effect of land use change on runoff peaks by coupling dominant land use categories with annual maximum runoff discharge; and (3) the use of return level plots as a reference to explore the watershed's sensitivity to land use change. Our findings indicate that land use change has a greater effect on runoff peak values than climate variability in our region of interest. The agricultural areas of Ferson Creek have been most affected by the rapid transformation of about 20% of their land into developed areas. Although agricultural areas can sometimes intensify runoff peaks, their reduction has led to excessive runoff discharges in Ferson Creek, as they have higher relative infiltration capacity than developed areas. We conclude that each watershed has its own fingerprint in terms of the connection between its land use types and hydrological patterns and that the region is most sensitive to the percentage of forests. These results are essential for improving infrastructure design and risk estimation methods in the region of interest.

Multi-variable approach to groundwater vulnerability elucidation: A risk-based multi-objective optimization model.

Groundwater vulnerability mapping is essential in environmental management since there is an increase in contamination caused by excessive population growth. However, to our knowledge, there is rare research dedicated to optimizing the groundwater vulnerability models, considering risk conditions, using a robust multi-objective optimization algorithm coupled with a multi-criteria decision-making model (MCDM). This study filled this knowledge gap by developing an innovative hybrid risk-based multi-objective optimization model using three distinguished models. The first model generated two series of scenarios for rate modifications associated with two common contaminations, Nitrate and Sulfate, based on susceptibility index (SI) and DRASTICA models. The second model was a multi-objective optimization framework using non-dominated sorting genetic algorithms- II and III (NSGA-II and NSGA-III), considering uncertainties in the input rates by the conditional value-at-risk (CVaR) technique. Finally, the third model was a well-known MCDM model, the COmplex PRoportional ASsessment (COPRAS), which identified the best compromise solution among Pareto-optimal solutions for weights of the contaminations. Regarding the Sulfate's results, although the optimized DRASTICA model led to the same correlation as the initial model, 0.7, the optimized SI model increased the correlation to 0.8 compared to the initial model as 0.58. For the Nitrate, both the optimized SI and the optimized DRASTICA models raised the correlation to 0.6 and 0.7 compared to the initial model with a correlation value of 0.36, respectively. Hence, the best and the lowest correlation among the optimized models were between SI and Sulfate concentration and SI and Nitrate concentration, respectively.

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.

Non-parametric severity-duration-frequency analysis of drought based on satellite-based product and model fusion techniques.

Climate change has increased the severity and frequency of droughts over the last decades. To alleviate the adverse impacts of droughts, an effective planning and management framework requires high-resolution spatiotemporal data. TRMM multi-satellite precipitation analysis (TMPA) dataset provides sufficient accuracy with fine spatio-temporal resolution. However, it only covers a short temporal span, which limits its applicability for drought studies. This paper presents a methodology for efficient and accurate temporal extension of TMPA using four artificial intelligence (AI)-based models. To improve AI-based model precipitation estimations, fusion techniques including Orness, Orlike, and genetic algorithm (GA)-based weighting methods were employed. Results show that fusion approaches provide more accurate estimates of precipitation. Different timescales of n-SPI time series and drought spatial maps were prepared to visually evaluate the performance of long-term TMPA (LT-TMPA) alongside statistical error indices. The results confirm that this dataset is effective for meteorological drought monitoring over southern Iran. Finally, drought risk assessment was carried out to determine the spatiotemporal characteristics of droughts through severity-duration-frequency (SDF) contour maps. In contrast to the traditional SDF curves, SDF contour maps provide a superior understanding of drought for policymakers since they preserve spatial information.