Data-driven water quality prediction for wastewater treatment plants.

Monitoring and managing wastewater treatment plants (WWTPs) is crucial for environmental protection. The presection of the quality of treated water is essential for energy efficient operation. The current research presents a comprehensive comparison of machine learning models for water quality parameter prediction in WWTPs. Four machine learning models presented in MLP, GFFR, MLP-PCA, and RBF were employed in this study. The primary notion of this study is to apply the proposed models using two distinct modeling scenarios. The first scenario represents a straightforward approach by utilizing the inputs and outputs of WWTPs; meanwhile, the second scenario involves using multi-step modeling techniques, which incorporate intermediate outputs induced by primary and secondary settlers. The study also investigates the potential of the adopted models to handle high dimensional data as a result of the multi-step modeling since more data points and outputs are progressively integrated at each step. The results show that the GFFR model outperforms the other models across both scenarios, specifically in the second scenario in predicting conductivity (COND) by providing higher correlation accuracy (R = 0.893) and lower prediction deviations (NRMSE = 0.091 and NMAE = 0.071). However, all models across both scenarios struggle to predict the other water quality parameters, generating significantly lower prediction correlations and higher prediction deviations. Nonetheless, the innovative multi-step technique in scenario two has significantly boosted the prediction capacity of all models, with improvement ranging from 0.2 % to 157 % and an average of 60 %. The implementation of AI models has proven its ability to accomplish high accuracy for WQ parameter prediction, highlighting the impact of leveraging intermediate process data.

Machine learning based-model to predict catalytic performance on removal of hazardous nitrophenols and azo dyes pollutants from wastewater.

To maintain human health and purity of drinking water, it is crucial to eliminate harmful chemicals such as nitrophenols and azo dyes, considering their natural presence in the surroundings. In this particular research study, the application of machine learning techniques was employed in order to make an estimation of the performance of reduction catalysis in the context of ecologically detrimental nitrophenols and azo dyes contaminants. The catalyst utilized in the experiment was Ag@CMC, which proved to be highly effective in eliminating various contaminants found in water, like 4-nitrophenol (4-NP). The experiments were carefully conducted at various time intervals, and the machine learning procedures used in this study were all employed to forecast catalytic performance. The evaluation of the performance of such algorithms were done by means of Mean Absolute Error. The noteworthy findings of this research indicated that the ADAM and LSTM algorithm exhibited the most favourable performance in the case of toxic compounds i.e. 4-NP. Moreover, the Ag@CMC catalyst demonstrated an impressive reduction efficiency of 98 % against nitrophenol in just 8 min. Thus, based on these compelling results, it can be concluded that Ag@CMC works as a highly effective catalyst for practical applications in real-world scenarios.

Integrated metaheuristic algorithms with extreme learning machine models for river streamflow prediction.

Accurate river streamflow prediction is pivotal for effective resource planning and flood risk management. Traditional river streamflow forecasting models encounter challenges such as nonlinearity, stochastic behavior, and convergence reliability. To overcome these, we introduce novel hybrid models that combine extreme learning machines (ELM) with cutting-edge mathematical inspired metaheuristic optimization algorithms, including Pareto-like sequential sampling (PSS), weighted mean of vectors (INFO), and the Runge-Kutta optimizer (RUN). Our comparative assessment includes 20 hybrid models across eight metaheuristic categories, using streamflow data from the Aswan High Dam on the Nile River. Our findings highlight the superior performance of mathematically based models, which demonstrate enhanced predictive accuracy, robust convergence, and sustained stability. Specifically, the PSS-ELM model achieves superior performance with a root mean square error of 2.0667, a Pearson's correlation index (R) of 0.9374, and a Nash-Sutcliffe efficiency (NSE) of 0.8642. Additionally, INFO-ELM and RUN-ELM models exhibit robust convergence with mean absolute percentage errors of 15.21% and 15.28% respectively, a mean absolute errors of 1.2145 and 1.2105, and high Kling-Gupta efficiencies values of 0.9113 and 0.9124, respectively. These findings suggest that the adoption of our proposed models significantly enhances water management strategies and reduces any risks.

Crosslinked chitosan-montmorillonite composite and its magnetized counterpart for the removal of basic fuchsin from wastewater: Parametric optimization using Box-Behnken design.

Treating wastewater polluted with organic dyestuffs is still a challenge. In that vein, facile synthesis of a structurally simple composite of chitosan with montmorillonite (CS-MMT) using glutaraldehyde as a crosslinker and the magnetized analogue (MAG@CS-MMT) was proposed as versatile adsorbents for the cationic dye, basic Fuchsin (FUS). Statistical modeling of the adsorption process was mediated using Box-Behnken (BB) design and by varying the composite dose, pH, [FUS], and contact time. Characterization of both composites showed an enhancement of surface features upon magnetization, substantiating a better FUS removal of the MAG@CS-MMT (%R = 98.43 %) compared to CS-MMT (%R = 68.02 %). The surface area analysis demonstrates that MAG@CS-MMT possesses a higher surface area, measuring 41.54 m2/g, and the surface analysis of the magnetized nanocomposite, conducted using FT-IR and Raman spectroscopies, proved the presence of FeO peaks. In the same context, adsorption of FUS onto MAG@CS-MMT fitted-well to the Langmuir isotherm model and the maximum adsorption capacities (qm) were 53.11 mg/g for CS-MMT and 88.34 mg/g for MAG@CS-MMT. Kinetics investigation shows that experimental data fitted well to the pseudo-second order (PSO) model. Regeneration study reveals that MAG@CS-MMT can be recovered effectively for repeated use with a high adsorption efficiency for FUS.

Review of driving-behaviour simulation: VISSIM and artificial intelligence approach.

Examining driving behaviour is crucial for traffic operations because of its influence on driver safety and the potential for increased risk of accidents, injuries, and fatalities. Approximately 95% of severe traffic collisions can be attributed to human error. With the progress in artificial intelligence in recent decades, notable advancements have been achieved in computer capabilities, communication systems and data collection technology. This increase has significantly influenced our capacity to replicate driver behaviour and comprehend underlying driving mechanisms in diverse situations. Traffic microsimulation facilitates an understanding of traffic performance inside a given road network. Among the microsimulation software packages, Verkehr In Städten - SIMulationsmodell (VISSIM) has garnered significant attention owing to its notable ability to accurately replicate traffic circumstances with high dependability in real-world scenarios. Given the diverse applicability of VISSIM-based schemes, this review systematically examines the applications of the VISSIM-based driving-behaviour models within different research contexts, revealing their utility. This review is designed to provide guidance for researchers in selecting the most suitable methodological approach tailored to their specific research objectives and constraints when utilising VISSIM. Five important aspects, including calibration, driving behaviour, incident, and heterogeneous traffic simulation, as well as utilisation of artificial intelligence with VISSIM, are assessed, which could yield substantial advantages in advancing more precise and authentic driving-behaviour modelling in VISSIM.

Groundwater level forecasting with machine learning models: A review.

Groundwater, the world's most abundant source of freshwater, is rapidly depleting in many regions due to a variety of factors. Accurate forecasting of groundwater level (GWL) is essential for effective management of this vital resource, but it remains a complex and challenging task. In recent years, there has been a notable increase in the use of machine learning (ML) techniques to model GWL, with many studies reporting exceptional results. In this paper, we present a comprehensive review of 142 relevant articles indexed by the Web of Science from 2017 to 2023, focusing on key ML models, including artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), support vector regression (SVR), evolutionary computing (EC), deep learning (DL), ensemble learning (EN), and hybrid-modeling (HM). We also discussed key modeling concepts such as dataset size, data splitting, input variable selection, forecasting time-step, performance metrics (PM), study zones, and aquifers, highlighting best practices for optimal GWL forecasting with ML. This review provides valuable insights and recommendations for researchers and water management agencies working in the field of groundwater management and hydrology.

Groundwater level forecasting using ensemble coactive neuro-fuzzy inference system.

A modeling framework utilizing the coactive neuro-fuzzy inference system (CANFIS) has been developed for multi-lead time groundwater level (GWL) forecasting in four different wells located in Texas and Florida, USA. Various model input combinations, including GWL, precipitation, temperature, and surface water level variables, have been derived based on proposed correlation analysis using singular spectrum analysis (SSA) remainders. The models have been trained on data subsets of varying lengths to identify the optimal training data duration. Additionally, we have introduced the bagging ensemble learning method to enhance the performance of the CANFIS model. As part of a comprehensive model evaluation process, the best-performing CANFIS model for each forecasting scenario has undergone uncertainty analysis using bootstrap sampling. Our results reveal that the CANFIS model performs satisfactorily for daily forecasting but leaves room for improvement in monthly forecasting, particularly for two-month and three-month ahead forecasts. Moreover, we have identified several optimal input combinations, highlighting the significance of the temperature variable in monthly forecasting. Furthermore, our findings indicate that additional training data does not necessarily lead to improved performance. The ensemble CANFIS model has demonstrated significant performance enhancement, particularly for monthly forecasting. Finally, the CANFIS model uncertainty analysis has shown satisfactory results for daily forecasting scenarios, while monthly forecasting models exhibit higher uncertainties, particularly during periods with distinctly different GWL fluctuation patterns.

Turning waste avocado stones and montmorillonite into magnetite-supported nanocomposites for the depollution of methylene blue: adsorbent reusability and performance optimization.

The existence of methylene blue (MB) in wastewater even as traces is raising environmental concerns. In this regard, the performances of four adsorbents, avocado stone biochar (AVS-BC), montmorillonite (MMT), and their magnetite Fe3O4-derived counterparts, were compared. Results showed the superior performance of Fe3O4@AVS-BC and Fe3O4@MMT nanocomposites with removal percentages (%R) of 95.59% and 88%. The morphological features of AVS-BC as revealed by SEM analysis showed a highly porous surface compared to a plane and smooth surface in the case of MMT. Surface analysis using FT-IR and Raman spectroscopies corroborated the existence of the Fe-O peaks upon loading with magnetite. The XRD analysis confirmed the formation of cubic magnetite nanoparticles. The adsorption process in the batch mode was optimized using central composite design (CCD). Equilibrium and kinetic isotherms showed that the adsorption of MB onto Fe3O4@AVS-BC fitted well with the Langmuir isotherm and the pseudo-second-order (PSO) model. The maximum adsorption capacity (qm) was 118.9 mg/g (Fe3O4@AVS-BC) and 72.39 mg/g (Fe3O4@MMT). The Fe3O4@AVS-BC showed a higher selectivity toward MB compared to other organic contaminants. The MB-laden adsorbent was successfully used for the remediation of Cr (III), Ni (II), and Cd (II) with removal efficiencies hitting 100% following thermal activation.

Predicting sea levels using ML algorithms in selected locations along coastal Malaysia.

In consideration of the distinct behavior of machine learning (ML) algorithms, six well-defined ML used were carried out in this study for predicting sea level on a day-to-day basis. Data compiled from 1985 to 2018 was utilized for training and testing the developed models. An assessment of the multiple statistics-driven regression algorithms resulted such that each tested location was associated with a particular preferred model. The following were the developed best models for their respective study areas: In Peninsular Malaysia, the interactions linear regression model was the best at Pulau Langkawi (RMSE = 19.066), the Matern 5/2 gaussian process regression model at Geting (RMSE = 49.891), and the trilayered artificial neural network at Pulau Pinang (RMSE = 20.026), while the linear regression model was the best at Sandakan in Sabah, East Malaysia (RMSE = 14.054). Other metrics, such as MAE and R-square, were also at their best values, each providing its best values, further substantiating the RMSE respectively, at each of the study areas. These empirical statistics (or metrics) also revealed that despite employing sea level as the sole parameter, results obtained were exceptional better when utilizing a 7-day lag, regardless of the model used. Notably, lag variables with less than a 7-day lag could degrade the model's accuracy in representing ground reality. The study emphasizes the importance of thorough training and testing of ML to aid decision-makers in developing mitigation actions for the climate change phenomena of sea level rise through reliable ML.

Streamflow classification by employing various machine learning models for peninsular Malaysia.

Due to excessive streamflow (SF), Peninsular Malaysia has historically experienced floods and droughts. Forecasting streamflow to mitigate municipal and environmental damage is therefore crucial. Streamflow prediction has been extensively demonstrated in the literature to estimate the continuous values of streamflow level. Prediction of continuous values of streamflow is not necessary in several applications and at the same time it is very challenging task because of uncertainty. A streamflow category prediction is more advantageous for addressing the uncertainty in numerical point forecasting, considering that its predictions are linked to a propensity to belong to the pre-defined classes. Here, we formulate streamflow prediction as a time series classification with discrete ranges of values, each representing a class to classify streamflow into five or ten, respectively, using machine learning approaches in various rivers in Malaysia. The findings reveal that several models, specifically LSTM, outperform others in predicting the following n-time steps of streamflow because LSTM is able to learn the mapping between streamflow time series of 2 or 3 days ahead more than support vector machine (SVM) and gradient boosting (GB). LSTM produces higher F1 score in various rivers (by 5% in Johor, 2% in Kelantan and Melaka and Selangor, 4% in Perlis) in 2 days ahead scenario. Furthermore, the ensemble stacking of the SVM and GB achieves high performance in terms of F1 score and quadratic weighted kappa. Ensemble stacking gives 3% higher F1 score in Perak river compared to SVM and gradient boosting.

Suspended sediment load prediction modelling based on artificial intelligence methods: The tropical region as a case study.

The impact of the suspended sediment load (SSL) on environmental health, agricultural operations, and water resources planning, is significant. The deposit of SSL restricts the streamflow region, affecting aquatic life migration and finally causing a river course shift. As a result, data on suspended sediments and their fluctuations are essential for a number of authorities especially for water resources decision makers. SSL prediction is often difficult due to a number of issues such as site-specific data, site-specific models, lack of several substantial components to use in prediction, and complexity its pattern. In the past two decades, many machine learning algorithms have shown huge potential for SSL river prediction. However, these models did not provide very reliable results, which led to the conclusion that the accuracy of SSL prediction should be improved. As a result, in order to solve past concerns, this research proposes a Long Short-Term Memory (LSTM) model for SSL prediction. The proposed model was applied for SSL prediction in Johor River located in Malaysia. The study allocated data for suspended sediment load and river flow for period 2010 to 2020. In the current research, four alternative models-Multi-Layer Perceptron (MLP) neural network, Support Vector Regression (SVR), Random Forest (RF), and Long Short-term Memory (LSTM) were investigated to predict the suspended sediment load. The proposed model attained a high correlation value between predicted and actual SSL (0.97), with a minimum RMSE (148.4 ton/day and a minimum MAE (33.43 ton/day). and can thus be generalized for application in similar rivers around the world.

Competitive adsorptive removal of promazine and promethazine from wastewater using olive tree pruning biochar: operational parameters, kinetics, and equilibrium investigations.

This research aims to remove two phenothiazines, promazine (PRO) and promethazine (PMT), from their individual and binary mixtures using olive tree pruning biochar (BC-OTPR). The impact of individual and combinatory effects of operational variables was evaluated for the first time using central composite design (CCD). Simultaneous removal of both drugs was maximized utilizing the composite desirability function. At low concentrations, the uptake of PRO and PMT from their individual solutions was achieved with high efficiency of 98.64%, 47.20 mg/g and 95.87%, 38.16 mg/g, respectively. No major differences in the removal capacity were observed for the binary mixtures. Characterization of BC-OTPR confirmed successful adsorption and showed that the OTPR surface was predominantly mesoporous. Equilibrium investigations revealed that the Langmuir isotherm model best describes the sorption of PRO/PMT from their individual solutions with maximum adsorption capacities of 640.7 and 346.95 mg/g, respectively. The sorption of PRO/PMT conforms to the pseudo-second-order kinetic model. Regeneration of the adsorbent surface was successfully done with desorption efficiencies of 94.06% and 98.54% for PRO and PMT, respectively, for six cycles.

Enhanced adsorptive removal of rifampicin and tigecycline from single system using nano-ceria decorated biochar of mango seed kernel.

Pharmaceutically active compounds (PhACs) represent an emerging class of contaminants. With a potential to negatively impact human health and the ecosystem, existence of pharmaceuticals in the aquatic systems is becoming a worrying concern. Antibiotics is a major class of PhACs and their existence in wastewater signifies a health risk on the long run. With the purpose of competently removing antibiotics from wastewater, cost-effective, and copiously available waste-derived adsorbents were structured. In this study, mango seeds kernel (MSK), both as a pristine biochar (Py-MSK) and as a nano-ceria-laden (Ce-Py-MSK) were applied for the remediation of rifampicin (RIFM) and tigecycline (TIGC). To save time and resources, adsorption experiments were managed using a multivariate-based scheme executing the fractional factorial design (FrFD). Percentage removal (%R) of both antibiotics was exploited in terms of four variables: pH, adsorbent dosage, initial drug concentration, and contact time. Preliminary experiments showed that Ce-Py-MSK has higher adsorption efficiency for both RIFM and TIGC compared to Py-MSK. The %R was 92.36% for RIFM compared to 90.13% for TIGC. With the purpose of comprehending the adsorption process, structural elucidation of both sorbents was performed using FT-IR, SEM, TEM, EDX, and XRD analyses which confirmed the decoration of the adsorbent surface with the nano-ceria. BET analysis revealed that Ce-Py-MSK has a higher surface area (33.83 m2/g) contrasted to the Py-MSK (24.72 m2/g). Isotherm parameters revealed that Freundlich model best fit Ce-Py-MSK-drug interactions. A maximum adsorption capacity (qm) of 102.25 and 49.28 mg/g was attained for RIFM and TIGC, respectively. Adsorption kinetics for both drugs conformed well with both pseudo-second order (PSO) and Elovich models. This study, therefore, has established the suitability of Ce-Py-MSK as a green, sustainable, cost-effective, selective, and efficient adsorbent for the treatment of pharmaceutical wastewater.

Performance of Pristine versus Magnetized Orange Peels Biochar Adapted to Adsorptive Removal of Daunorubicin: Eco-Structuring, Kinetics and Equilibrium Studies.

Drugs and pharmaceuticals are an emergent class of aquatic contaminants. The existence of these pollutants in aquatic bodies is currently raising escalating concerns because of their negative impact on the ecosystem. This study investigated the efficacy of two sorbents derived from orange peels (OP) biochar (OPBC) for the removal of the antineoplastic drug daunorubicin (DNB) from pharmaceutical wastewater. The adsorbents included pristine (OPBC) and magnetite (Fe3O4)-impregnated (MAG-OPBC) biochars. Waste-derived materials offer a sustainable and cost-effective solution to wastewater bioremediation. The results showed that impregnation with Fe3O4 altered the crystallization degree and increased the surface area from 6.99 m2/g in OPBC to 60.76 m2/g in the case of MAG-OPBC. Placket-Burman Design (PBD) was employed to conduct batch adsorption experiments. The removal efficiency of MAG-OPBC (98.51%) was higher compared to OPBC (86.46%). DNB adsorption onto OPBC followed the D-R isotherm, compared to the Langmuir isotherm in the case of MAG-OPBC. The maximum adsorption capacity (qmax) was 172.43 mg/g for MAG-OPBC and 83.75 mg/g for OPBC. The adsorption kinetics for both sorbents fitted well with the pseudo-second-order (PSO) model. The results indicate that MAG-OPBC is a promising adsorbent for treating pharmaceutical wastewater.

Optimal water supply reservoir operation by leveraging the meta-heuristic Harris Hawks algorithms and opposite based learning technique.

To ease water scarcity, dynamic programming, stochastic dynamic programming, and heuristic algorithms have been applied to solve problem matters related to water resources. Development, operation, and management are vital in a reservoir operating policy, especially when the reservoir serves a complex objective. In this study, an attempt via metaheuristic algorithms, namely the Harris Hawks Optimisation (HHO) Algorithm and the Opposite Based Learning of HHO (OBL-HHO) are made to minimise the water deficit as well as mitigate floods at downstream of the Klang Gate Dam (KGD). Due to trade-offs between water supply and flood management, the HHO and OBL-HHO models have configurable thresholds to optimise the KGD reservoir operation. To determine the efficacy of the HHO and OBL-HHO in reservoir optimisation, reliability, vulnerability, and resilience are risk measures evaluated. If inflow categories are omitted, the OBL-HHO meets 71.49% of demand compared to 54.83% for the standalone HHO. The HHO proved superior to OBL-HHO in satisfying demand during medium inflows, achieving 38.60% compared to 20.61%, even though the HHO may have experienced water loss at the end of the storage level. The HHO is still a promising method, as proven by its reliability and resilience indices compared to other published heuristic algorithms: at 62.50% and 1.56, respectively. The Artificial Bee Colony (ABC) outcomes satisfied demand at 61.36%, 59.47% with the Particle Swarm Optimisation (PSO), 55.68% with the real-coded Genetic Algorithm (GA), and 23.5 percent with the binary GA. For resilience, the ABC scored 0.16, PSO scored 0.15, and real coded GA scored 0.14 whilst the binary-GA has the worst failure recovery algorithm with 0.09.

Water budget-salt balance model for calculating net water saving considering different non-conventional water resources in agricultural process.

Iraq is facing a dire water crisis due to the decrease in water quantities flow in Tigris and Euphrates Rivers. Due to population growth, several studies estimated the water shortage in 2035 to be 44 Billion Cubic Meter (BCM). Thus, Water Budget-Salt Balance Model (WBSBM) has been developed, applied and examined for the Euphrates River basin to compute the net water saving from Non-Conventional Water Resources (NCWRs). WBSBM includes 4-stages; the first is to identify the required data correspond to the conventional water resources in the study-area. The second stage is demonstrating the water-users activities. Thirdly, develop model through the proposed NCWR projects that reflect the required data. The final stage involves net water saving computation while applying all the NCWR projects simultaneously. The results obtained the optimal potential net water saving amount, which are 6.823 and 6.626 BCM/year in 2025 and 2035, respectively. In conclusion, the proposed WBSBM model has comprehensively examined different scenarios of utilizing NCWRs and has determined the optimal potential the net water saving amounts.

Watermelon rinds as cost-efficient adsorbent for acridine orange: a response surface methodological approach.

In the current investigation, watermelon rinds (WMR) have been utilized as an eco-friendly and cost-efficient adsorbent for acridine orange (AO) from contaminated water samples. Adsorption of AO onto raw (RWM) and thermally treated rinds (TTWM250 and TTWM500) has been studied. The adsorption efficiency of the three adsorbents was evaluated by measuring the % removal (%R) of AO and the adsorption capacity (qe, mg/g). Dependent variables (%R and qe) were optimized as a function of four factors: pH, sorbent dosage (AD), the concentration of AO (DC), and contact time (ST). Box-Behnken (BB) design has been utilized to obtain the optimum adsorption conditions. Prepared adsorbents have been characterized using scanning electron microscopy (SEM), Fourier-transform infrared (FT-IR), and Raman spectroscopies. The surface area of RWM, TTWM250, and TTWM500, as per the Brunauer-Emmett-Teller (BET) analysis, was 2.66, 2.93, and 5.03 m2/g, respectively. Equilibrium investigations suggest that Freundlich model was perfectly fit for adsorption of AO onto TTWM500. Maximum adsorption capacity (qmax) of 69.44 mg/g was obtained using the Langmuir equation. Adsorption kinetics could be best described by the pseudo-second-order (PSO) model. The multi-cycle sorption-desorption study showed that TTWM500 could be regenerated with the adsorption efficiency being preserved up to 87% after six cycles.

Rainfall prediction using multiple inclusive models and large climate indices.

Rainfall prediction is vital for the management of available water resources. Accordingly, this study used large lagged climate indices to predict rainfall in Iran's Sefidrood basin. A radial basis function neural network (RBFNN) and a multilayer perceptron (MLP) network were used to predict monthly rainfall. The models were trained using the naked mole rat (NMR) algorithm, firefly algorithm (FFA), genetic algorithm (GA), and particle swarm optimization (PSO) algorithm. Large lagged climate indices, as well as three hybrid models, i.e., inclusive multiple model (IMM)-MLP, IMM-RBFNN, and the simple average method (SAM), were then employed to predict rainfall. This paper aims to predict rainfall using large climate indices, ensemble models, and optimized artificial neural network models. Also, the paper considers the uncertainty resources in the modeling process. The inputs were selected using a new input selection method, namely a hybrid gamma test (GT). The GT was integrated with the NMR algorithm to create a new test for determining the best input scenario. Therefore, the main innovations of this study were the introduction of the new ensemble and the new hybrid GT, as well as the new MLP and RBFNN models. The introduced ensemble models of the current study are not only useful for rainfall prediction but also can be used to predict other metrological parameters. The uncertainty of the model parameters and input data were also analysed. It was found that the IMM-MLP model reduced the root mean square error (RMSE) of the IMM-RBFNN, SAM, MLP-NMR, RBFNN-NMR, MLP-FFA, RBFNN-FFA, MLP-PSO, RBFNN-PSO, MLP-GA, and RBFNN-GA, MLP, and RBFNN models by 12%, 25%, 31%, 55%, 60%, 62%, 66%, 69%, 70%, 71%, 72%, and 72%, respectively. The IMMs, such as the IMM-MLP, IMM-RBFNN, and SAM, outperformed standalone models. The uncertainty bound of the multiple inclusive models was narrower than that of the standalone MLP and RBFNN models. The MLP-NMR model decreased the RMSE of the RBFNN-NMR, RBFNN-FFA, RBFNN-PSO, and RBFNN models by 15%, 26%, 37%, 42%, and 45%, respectively. The proposed ensemble models were robust tools for combining standalone models to predict hydrological variables.

Application of Plackett-Burman Design for Spectrochemical Determination of the Last-Resort Antibiotic, Tigecycline, in Pure Form and in Pharmaceuticals: Investigation of Thermodynamics and Kinetics.

Tigecycline (TIGC) reacts with 7,7,8,8-tetracyanoquinodimethane (TCNQ) to form a bright green charge transfer complex (CTC). The spectrum of the CTC showed multiple charge transfer bands with a major peak at 843 nm. The Plackett-Burman design (PBD) was used to investigate the process variables with the objective being set to obtaining the maximum absorbance and thus sensitivity. Four variables, three of which were numerical (temperature-Temp; reagent volume-RV; reaction time-RT) and one non-numerical (diluting solvent-DS), were studied. The maximum absorbance was achieved using a factorial blend of Temp: 25 °C, RV: 0.50 mL, RT: 60 min, and acetonitrile (ACN) as a DS. The molecular composition that was investigated using Job's method showed a 1:1 CTC. The method's validation was performed following the International Conference of Harmonization (ICH) guidelines. The linearity was achieved over a range of 0.5-10 µg mL-1 with the limits of detection (LOD) and quantification (LOQ) of 166 and 504 ng mL-1, respectively. The method was applicable to TIGC per se and in formulations without interferences from common additives. The application of the Benesi-Hildebrand equation revealed the formation of a stable complex with a standard Gibbs free energy change (∆G°) value of -26.42 to -27.95 kJ/mol. A study of the reaction kinetics revealed that the CTC formation could be best described using a pseudo-first-order reaction.

Artificial neural network model with different backpropagation algorithms and meteorological data for solar radiation prediction.

Solar energy serves as a great alternative to fossil fuels as they are clean and renewable energy. Accurate solar radiation (SR) prediction can substantially lower down the impact cost pertaining to the development of solar energy. Lately, many SR forecasting system has been developed such as support vector machine, autoregressive moving average and artificial neural network (ANN). This paper presents a comprehensive study on the meteorological data and types of backpropagation (BP) algorithms used to train and develop the best SR predicting ANN model. The meteorological data, which includes temperature, relative humidity and wind speed are collected from a meteorological station from Kuala Terrenganu, Malaysia. Three different BP algorithms are employed into training the model i.e., Levenberg-Marquardt, Scaled Conjugate Gradient and Bayesian Regularization (BR). This paper presents a comparison study to select the best combination of meteorological data and BP algorithm which can develop the ANN model with the best predictive ability. The findings from this study shows that temperature and relative humidity both have high correlation with SR whereas wind temperature has little influence over SR. The results also showed that BR algorithm trained ANN models with maximum R of 0.8113 and minimum RMSE of 0.2581, outperform other algorithm trained models, as indicated by the performance score of the respective models.