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This study presents a modelling and monitoring framework to assess the hydrological and geomorphic impacts of removing the Kirkpatrick (Rodman) Dam on the Ocklawaha River in Florida. Utilizing historical streamflow records from USGS (1994–2015), sediment samples from a 2019 field study, and HEC-RAS simulations, the research explores changes in flow regimes, sediment transport, and floodplain dynamics under current and post-dam conditions. Indicators of Hydrologic Alteration (IHA) analysis reveals significant disruption to the river’s natural flow following dam construction, with increased low-flow frequency and reduced seasonal variability. HEC-RAS simulations under steady and unsteady scenarios demonstrate altered hydraulic parameters, reduced sediment transport capacity, and formation of depositional bars. The study models four return- period events (10-, 25-, 50-, and 100-year floods) to compare inundation patterns with and without the dam. Findings show that dam removal would restore natural flow connectivity but also increase the risk of channel erosion and bank instability due to higher sediment mobility. While data limitations exist, the results highlight key considerations for river restoration, including flood risk management and ecological resilience. This research provides a preliminary yet data-driven approach to understanding dam removal consequences, supporting informed environmental management and long- term restoration planning for the Ocklawaha River.

In this study, a method for degrading PET-waste plastic bottles using ZnCl 2 :Urea as a catalyst was developed, resulting in high conversion (87%). The terephthalic acid obtained from the degradation of Waste PET Bottles (WPTs) was combined with copper and zinc salts to synthesize bimetallic metal–organic frameworks (MOF). The effectiveness of a bimetallic Cu-Zn(BDC)-MOF in catalyzing the reduction reaction of organic pollutant dyes (OPDs) was investigated, and the degradation efficiency of individual dyes was optimized, achieving over 95% degradation within 6–12 min under optimal conditions. Various techniques, including FT-IR, XRD, FE-SEM, EDS, and TEM were used to characterize the synthesized MOF. Results showed that the catalytic activity of Cu-Zn-MOF in reduction reaction of OPDs was enhanced by increasing the copper content. The reaction kinetics were investigated following pseudo-first-order kinetics with rate constants of 0.581, 0.43, 0.37, and 0.30 min −1 for Methylene Blue (MB), Methyl Orange (MO), 4-Nitrophenol (4-NP), and 4-Nitroaniline (4-NA), respectively. The investigations revealed that the produced catalyst exhibited excellent stability and recoverability, while its activity remained well-preserved even after undergoing three reuse cycles.

As the demand for nonrenewable natural resources, such as aggregate, is increasing worldwide, new production of artificial aggregate should be developed. Artificial lightweight aggregate can bring advantages to the construction field due to its lower density, thus reducing the dead load applied to the structural elements. In addition, application of artificial lightweight aggregate in lightweight concrete will produce lower thermal conductivity. However, the production of artificial lightweight aggregate is still limited. Production of artificial lightweight aggregate incorporating waste materials or pozzolanic materials is advantageous and beneficial in terms of being environmentally friendly, as well as lowering carbon dioxide emissions. Moreover, additives, such as geopolymer, have been introduced as one of the alternative construction materials that have been proven to have excellent properties. Thus, this paper will review the production of artificial lightweight aggregate through various methods, including sintering, cold bonding, and autoclaving. The significant properties of artificial lightweight aggregate, including physical and mechanical properties, such as water absorption, crushing strength, and impact value, are reviewed. The properties of concrete, including thermal properties, that utilized artificial lightweight aggregate were also briefly reviewed to highlight the advantages of artificial lightweight aggregate.

A new daily water balance model is developed and tested in this paper. The new model has a similar model structure to the existing probability distributed rainfall runoff models (PDM), such as HyMOD. However, the model utilizes a new distribution function for soil water storage capacity, which leads to the SCS (Soil Conservation Service) curve number (CN) method when the initial soil water storage is set to zero. Therefore, the developed model is a unification of the PDM and CN methods and is called the PDM–CN model in this paper. Besides runoff modeling, the calculation of daily evaporation in the model is also dependent on the distribution function, since the spatial variability of soil water storage affects the catchment-scale evaporation. The generated runoff is partitioned into direct runoff and groundwater recharge, which are then routed through quick and slow storage tanks, respectively. Total discharge is the summation of quick flow from the quick storage tank and base flow from the slow storage tank. The new model with 5 parameters is applied to 92 catchments for simulating daily streamflow and evaporation and compared with AWMB, SACRAMENTO, and SIMHYD models. The performance of the model is slightly better than HyMOD but is not better compared with the 14-parameter model (SACRAMENTO) in the calibration, and does not perform as well in the validation period as the 7-parameter model (SIMHYD) in some areas, based on the NSE values. The linkage between the PDM–CN model and long-term water balance model is also presented, and a two-parameter mean annual water balance equation is derived from the proposed PDM–CN model.

Sediment discharge in rivers is among the most important water and environmental engineering issues. The present study employed the published reliable field data set and three white-box data-driven methods, including the group method of data handling (GMDH), gene expression programming (GEP), and the multivariate adaptive regression splines (MARS) approach for modeling sediment discharge in rivers (Qs). In addition, the artificial neural network (ANN) model, known as the popular and widely used black-box data-driven model, was employed for modeling sediment discharge. The performance of the proposed methods was explored by statistical measures, scatter plots, and Taylor and Violin plots. The main feature of white-box data-driven models is that they provide explicit mathematical expressions for the prediction of Qs. The outcomes of the proposed methods provide better and more competitive results than the earlier study conducted using the model tree (MT) approach. Statical measurements and graphical plots indicated that all proposed methods have similar results for the prediction of sediment discharge. However, GMDH and GEP were more accurate than the MARS and ANN models. For the overall evaluation of the proposed models, the ranking mean (RM) method was used. This method showed that the GMDH model with RM = 1.86 had better performance in estimating sediment discharge, followed by the GEP with RM = 2.29, MARS with RM = 2.86, and ANN with RM = 3. It is worth mentioning that simple, explicit mathematical expressions generated by GMDH and MARS were more straightforward calculations for estimating sediment discharge compared to GEP and ANN.

Forecasting river flow is an important stage in reservoir operation, urban water management, and water resource optimization. The goal of this research is to forecast daily river flows for two intermittent and ephemeral rivers. Based on the antecedent river flow, the forecasting approach used stochastic (AR, ARIMA, and SARIMA) and machine learning (ML) techniques. The ML methods consist of three shallow learning models (NARnet, OS-ELM, and NAR-GMDH) and three deep learning models (LSTM, CNN-GRU, and stacked-LSTM). The precision of all the models in the ephemeral river was higher the intermittent river considering both base flow ( R 2 ave  = 0.94 vs. 0.87) and peak flow. The study was also extended to forecasting peak river flow values, demonstrating in the superiority of deep learning (RMSE ave  = 7.6 m 3 /s) over the shallow (RMSE ave  = 8.9 m 3 /s) and stochastic (RMSE ave  = 9.1 m 3 /s). Applied models acted similarly in forecasting peak flow in both rivers due to substantial variations in the floods. Moreover, the results demonstrated that the deep learning group models surpass stochastic and shallow learning group models in terms of five evaluation criteria, including RMSE, MAE, mean bias error, correlation coefficient, and agreement index for both rivers. In general, the results indicate that the CNN-GRU outperforms the other models in terms of river flow forecasting and is suggested as a viable model for learning the complicated behavior of streamflow.

Due to the extraordinary properties for heavy-duty applications, there has been a great deal of interest in the utilization of waste material via geopolymerization technology. There are various advantages offered by this geopolymer-based material, such as excellent stability, exceptional impermeability, self-refluxing ability, resistant thermal energy from explosive detonation, and excellent mechanical performance. An overview of the work with the details of key factors affecting the heavy-duty performance of geopolymer-based material such as type of binder, alkali agent dosage, mixing design, and curing condition are reviewed in this paper. Interestingly, the review exhibited that different types of waste material containing a large number of chemical elements had an impact on mechanical performance in military, civil engineering, and road application. Finally, this work suggests some future research directions for the the remarkable of waste material through geopolymerization to be employed in heavy-duty application.

The accurate estimation of the soil permeability coefficient ( k ) is essential for civil engineering projects. This study employed an experimental dataset and effective parameters to model soil permeability. The specific particle sizes of soil, including d10, d50, and d60, and void ratio (e), were used as input parameters for the estimation of k . The artificial neural network (ANN) method was employed, along with two widely used metaheuristic algorithms, including particle swarm optimization (PSO) and genetic algorithm (GA), to enhance the accuracy of ANN. The proposed models were evaluated for accuracy and performance using statistical metrics as well as graphical analyses. The hybridization models outperformed the standalone ANN model. In addition, ANN-PSO was more accurate than ANN-GA. The ANN-PSO model demonstrated the highest accuracy during both the training and testing stages. In the testing stage, the ANN-PSO model achieved a coefficient of determination (R 2 ) of 0.987, a root mean square error (RMSE) of 0.0003, and a performance index (PI) of 1.72. This was followed by the ANN-GA model, which recorded an R 2 of 0.977, an RMSE of 0.0004, and a PI of 1.60, and the ANN model with R 2  = 0.861, RMSE = 0.0010, and PI = 1.40. The d10 variable was also found to be the most important factor for estimating k by using cosine amplitude sensitivity analysis and the SHAP method based on the ANN-PSO model.

There is a burgeoning interest in the development of geopolymers as sustainable construction materials and incombustible inorganic polymers. However, geopolymers show quasi-brittle behavior. To overcome this weakness, hundreds of researchers have focused on the development, characterization, and implementation of geopolymer-reinforced fibers for a wide range of applications for light geopolymers concrete. This paper discusses the rapidly developing geopolymer-reinforced fibers, focusing on material and geometrical properties, numerical simulation, and the effect of fibers on the geopolymers. In the section on the effect of fibers on the geopolymers, a comparison between single and hybrid fibers will show the compressive strength and toughness of each type of fiber. It is proposed that interfacial bonding between matrix and fibers is important to obtain better results, and interfacial bonding between matrix and fiber depends on the type of material surface contact area, such as being hydrophobic or hydrophilic, as well as the softness or roughness of the surface.

Ground-level ozone (O3) is one of the most significant forms of air pollution around the world due to its ability to cause adverse effects on human health and environment. Understanding the variation and association of O3 level with its precursors and weather parameters is important for developing precise forecasting models that are needed for mitigation planning and early warning purposes. In this study, hourly air pollution data (O3, CO, NO2, PM10, NmHC, SO2) and weather parameters (relative humidity, temperature, UVB, wind speed and wind direction) covering a ten year period (2003–2012) in the selected urban areas in Malaysia were analyzed. The main aim of this research was to model O3 level in the band of greatest solar radiation with its precursors and meteorology parameters using the proposed predictive models. Six predictive models were developed which are Multiple Linear Regression (MLR), Feed-Forward Neural Network (FFANN), Radial Basis Function (RBFANN), and the three modified models, namely Principal Component Regression (PCR), PCA-FFANN, and PCA-RBFANN. The performances of the models were evaluated using four performance measures, i.e., Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Index of Agreement (IA), and Coefficient of Determination (R2). Surface O3 level was best described using linear regression model (MLR) with the smallest calculated error (MAE = 6.06; RMSE = 7.77) and the highest value of IA and R2 (0.85 and 0.91 respectively). The non-linear models (FFANN and RBFANN) fitted the observed O3 level well, but were slightly less accurate compared to MLR. Nonetheless, all the unmodified models (MLR, ANN, and RBF) outperformed the modified-version models (PCR, PCA-FFANN, and PCA-RBFANN). Verification of the best model (MLR) was done using air pollutant data in 2018. The MLR model fitted the dataset of 2018 very well in predicting the daily O3 level in the specified selected areas with the range of R2 values of 0.85 to 0.95. These indicate that MLR can be used as one of the reliable methods to predict daytime O3 level in Malaysia. Thus, it can be used as a predictive tool by the authority to forecast high ozone concentration in providing early warning to the population.