Shear stress comprises basic information for predicting the average depth velocity and discharge in channels. With knowledge of the percentage of shear force carried by walls (%SFw) it is possible to more accurately estimate shear stress values. The %SFw, non-dimension wall shear stress ([Formula: see text]) and non-dimension bed shear stress ([Formula: see text]) in smooth rectangular channels were predicted by a three methods, the Bayesian Regularized Neural Network (BRNN), the Radial Basis Function (RBF), and the Modified Structure-Radial Basis Function (MS-RBF). For this aim, eight data series of research experimental results in smooth rectangular channels were used. The results of the new method of MS-RBF were compared with those of a simple RBF and BRNN methods and the best model was selected for modeling each predicted parameters. The MS-RBF model with RMSE of 3.073, 0.0366 and 0.0354 for %SFw, [Formula: see text] and [Formula: see text] respectively, demonstrated better performance than those of the RBF and BRNN models. The results of MS-RBF model were compared with three other proposed equations by researchers for trapezoidal channels and rectangular ducts. The results showed that the MS-RBF model performance in estimating %SFw, [Formula: see text] and [Formula: see text] is superior than those of presented equations by researchers.
Dissolved oxygen (DO) is an important indicator of river health for environmental engineers and ecological scientists to understand the state of river health. This study aims to evaluate the reliability of four feature selector algorithms i.e., Boruta, genetic algorithm (GA), multivariate adaptive regression splines (MARS), and extreme gradient boosting (XGBoost) to select the best suited predictor of the applied water quality (WQ) parameters; and compare four tree-based predictive models, namely, random forest (RF), conditional random forests (cForest), RANdom forest GEneRator (Ranger), and XGBoost to predict the changes of dissolved oxygen (DO) in the Klang River, Malaysia. The total features including 15 WQ parameters from monitoring site data and 7 hydrological components from remote sensing data. All predictive models performed well as per the features selected by the algorithms XGBoost and MARS in terms applied statistical evaluators. Besides, the best performance noted in case of XGBoost predictive model among all applied predictive models when the feature selected by MARS and XGBoost algorithms, with the coefficient of determination (R2) values of 0.84 and 0.85, respectively, nonetheless the marginal performance came up by Boruta-XGBoost model on in this scenario.
A wastewater treatment plant (WWTP) is an essential part of the urban water cycle, which reduces concentration of pollutants in the river. For monitoring and control of WWTPs, researchers develop different models and systems. This study introduces a new deep learning model for predicting effluent quality parameters (EQPs) of a WWTP. A method that couples a convolutional neural network (CNN) with a novel version of radial basis function neural network (RBFNN) is proposed to simultaneously predict and estimate uncertainty of data. The multi-kernel RBFNN (MKRBFNN) uses two activation functions to improve the efficiency of the RBFNN model. The salp swarm algorithm is utilized to set the MKRBFNN and CNN parameters. The main advantage of the CNN-MKRBFNN-salp swarm algorithm (SSA) is to automatically extract features from data points. In this study, influent parameters (if) are used as inputs. Biological oxygen demand (BODif), chemical oxygen demand (CODif), total suspended solids (TSSif), volatile suspended solids (VSSif), and sediment (SEDef) are used to predict EQPs, including CODef, BODef, and TSSef. At the testing level, the Nash-Sutcliffe efficiencies of CNN-MKRBFNN-SSA are 0.98, 0.97, and 0.98 for predicting CODef, BODef, and TSSef. Results indicate that the CNN-MKRBFNN-SSA is a robust model for simulating complex phenomena.
The Great Lakes are critical freshwater sources, supporting millions of people, agriculture, and ecosystems. However, climate change has worsened droughts, leading to significant economic and social consequences. Accurate multi-month drought forecasting is, therefore, essential for effective water management and mitigating these impacts. This study introduces the Multivariate Standardized Lake Water Level Index (MSWI), a modified drought index that utilizes water level data collected from 1920 to 2020. Four hybrid models are developed: Support Vector Regression with Beluga whale optimization (SVR-BWO), Random Forest with Beluga whale optimization (RF-BWO), Extreme Learning Machine with Beluga whale optimization (ELM-BWO), and Regularized ELM with Beluga whale optimization (RELM-BWO). The models forecast droughts up to six months ahead for Lake Superior and Lake Michigan-Huron. The best-performing model is then selected to forecast droughts for the remaining three lakes, which have not experienced severe droughts in the past 50 years. The results show that incorporating the BWO improves the accuracy of all classical models, particularly in forecasting drought turning and critical points. Among the hybrid models, the RELM-BWO model achieves the highest level of accuracy, surpassing both classical and hybrid models by a significant margin (7.21 to 76.74%). Furthermore, Monte-Carlo simulation is employed to analyze uncertainties and ensure the reliability of the forecasts. Accordingly, the RELM-BWO model reliably forecasts droughts for all lakes, with a lead time ranging from 2 to 6 months. The study's findings offer valuable insights for policymakers, water managers, and other stakeholders to better prepare drought mitigation strategies.
The fungi-based technology, wild-Serbian Ganoderma lucidum (WSGL) as myco-alternative to existing conventional microbial-based wastewater treatment is introduced in this study as a potential alternative treatment. The mycoremediation is highly persistent for its capability to oxidatively breakdown pollutant substrates and widely researched for its medicinal properties. Utilizing the nonhazardous properties and high degradation performance of WSGL, this research aims to optimize mycoremediation treatment design for chemical oxygen demand (COD) and ammonia nitrogen (AN) removal in domestic wastewater based on proposed Model 1 (temperature and treatment time) and Model 2 (volume of pellet and treatment time) via response surface methodology (RSM). Combined process variables were temperature (0C) (Model 1) and the volume of mycelial pellets (%) (Model 2) against treatment time (hour). Response variables for these two sets of central composite design (CCD) were the removal efficiencies of COD (%) and AN (%). The regression line fitted well with the data with R2 values of 0.9840 (Model 1-COD), 0.9477 (Model 1-AN), 0.9988 (Model 2-COD), and 0.9990 (Model 2-AN). The lack of fit test gives the highest value of sum of squares equal to 9494.91 (Model 1-COD), 9701.68 (Model 1-AN), 23786.55 (Model 2-COD), and 13357.02 (Model 2-AN), with probability F values less than 0.05 showing significant models. The optimized temperature for Model 1 was at 25 °C within 24 h of treatment time with 95.1% COD and 96.3% AN removals. The optimized condition (temperature) in Model 1 was further studied in Model 2. The optimized volume of pellet for Model 2 was 0.25% in 24-h treatment time with 76.0% COD and 78.4% AN removals. Overall, the ascended sequence of high volume of pellet considered in Model 2 will slow down the degradation process. The best fit volume of pellet with maximum degradation of COD and AN is equivalent to 0.1% at 25 °C in 24 h. The high performance achieved demonstrates that the mycoremediation of G. lucidum is highly potential as part of the wastewater treatment system in treating domestic wastewater of high organic loadings.
In nature, streamflow pattern is characterized with high non-linearity and non-stationarity. Developing an accurate forecasting model for a streamflow is highly essential for several applications in the field of water resources engineering. One of the main contributors for the modeling reliability is the optimization of the input variables to achieve an accurate forecasting model. The main step of modeling is the selection of the proper input combinations. Hence, developing an algorithm that can determine the optimal input combinations is crucial. This study introduces the Genetic algorithm (GA) for better input combination selection. Radial basis function neural network (RBFNN) is used for monthly streamflow time series forecasting due to its simplicity and effectiveness of integration with the selection algorithm. In this paper, the RBFNN was integrated with the Genetic algorithm (GA) for streamflow forecasting. The RBFNN-GA was applied to forecast streamflow at the High Aswan Dam on the Nile River. The results showed that the proposed model provided high accuracy. The GA algorithm can successfully determine effective input parameters in streamflow time series forecasting.
Soil requires load bearing impact assessment for stability. Therefore, this study aims to utilize the multi-channel analysis surface wave (MASW) for soil subsurface investigation and profiling around Peninsular Malaysia. The standard penetration test (SPT) was conducted for comparison between factual N-value and computed N-value from shear wave velocity (Vs ) obtained from MASW using the Imai and Tonouchi equation. The correlation coefficient (R) and coefficient of determination, (R2 ), showed strong relationship between factual N-value and computed N-value. The model of Vs and factual N-value data distribution is non-normal but the analyzed relationship shows a significant level of p-value < 0.05. The R2 for each location of Vs -N-value relationship are ranging from 0.5 to 0.9.
The potassium (K) and sodium (Na) elements in banana are needed for hydration reaction that can enhance the strength properties of concrete. This research aims (a) to determine the material engineering properties of banana skin ash (BSA) and concrete containing BSA, (b) to measure the strength enhancement of concrete due to BSA, and (c) to identify optimal application of BSA as supplementary cement materials (SCM) in concrete. The BSA characterization were assessed through X-ray fluorescence (XRF) and Blaine's air permeability. The workability, compressive strength, and microstructures of concrete containing BSA were analysed using slump test, universal testing machine (UTM) and scanning electron microscope (SEM). A total of 15 oxides and 19 non-oxides elements were identified in BSA with K (43.1%) the highest and Na was not detected. At 20 g of mass, the BSA had a higher bulk density (198.43 ± 0.00 cm3) than ordinary Portland cement (OPC) (36.32 ± 0.00 cm3) indicating availability of large surface area for water absorption. The concrete workability was reduced with the presence of BSA (0% BSA: > 100 mm, 1% BSA: 19 ± 1.0 mm, 2%: 15 ± 0.0 mm, 3% BSA: 10 ± 0.0 mm). The compressive strength increased with the number of curing days. The concrete microstructures were improved; interfacial transition zones (ITZ) decreased with an increase of BSA. The optimal percentage of BSA obtained was at 1.25%. The established model showed significant model terms (Sum of Squares = 260.60, F value = 69.84) with probability of 0.01% for the F-value to occur due to noise. The established model is useful for application in construction industries.