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.
In the recent decade, deep eutectic solvents (DESs) have occupied a strategic place in green chemistry research. This paper discusses the application of DESs as functionalization agents for multi-walled carbon nanotubes (CNTs) to produce novel adsorbents for the removal of 2,4-dichlorophenol (2,4-DCP) from aqueous solution. Also, it focuses on the application of the feedforward backpropagation neural network (FBPNN) technique to predict the adsorption capacity of DES-functionalized CNTs. The optimum adsorption conditions that are required for the maximum removal of 2,4-DCP were determined by studying the impact of the operational parameters (i.e., the solution pH, adsorbent dosage, and contact time) on the adsorption capacity of the produced adsorbents. Two kinetic models were applied to describe the adsorption rate and mechanism. Based on the correlation coefficient (R2) value, the adsorption kinetic data were well defined by the pseudo second-order model. The precision and efficiency of the FBPNN model was approved by calculating four statistical indicators, with the smallest value of the mean square error being 5.01 × 10-5. Moreover, further accuracy checking was implemented through the sensitivity study of the experimental parameters. The competence of the model for prediction of 2,4-DCP removal was confirmed with an R2 of 0.99.
Climate change poses an escalating threat to the safety of high-hazard embankment dams, increases flood discharge impacting dam overtopping risk by altering the hydrological load of the original dam designed capacity. This paper's primary aims are to evaluate climate change's influence on extreme rainfall events and their impact on dam safety and to assess the overtopping risk of Batu Dam under various climate scenarios. This study focusses on assessing the overtopping risk of Batu Dam in Malaysia, utilizing regional climate model projections from the Coupled Model Intercomparison Project 5 (CMIP5) spanning 2020 to 2100. Three Representative Concentration Pathways (RCPs)-RCP4.5, RCP6.0, and RCP8.5 as the scenario and divide into 3 period of study: early century (2020-2046), mid (2047-2073) and late-century (2074-2100) evaluated with hydrological analysis to access the dam safety. Using the Linear Scaling Method (LSM), we corrected the bias projection rainfall data from three Regional Climate Models (RCMs) for the RCPs. Future Probable Maximum Precipitation (PMP) was estimated using statistical analysis techniques developed by the National Hydraulic Research Institute of Malaysia (NAHRIM). Additionally, Rainfall Intensity-Duration-Frequency (IDF) curves were updated based on climate scenarios outlined in the Hydrological Procedure 2021 and the associated Climate Change Factors. The HEC-HMS hydrological model was employed to simulate PMF and IDF for ARIs ranging from 1 to 100,000 years, providing a comprehensive analysis of risks under future climatic conditions. Across all future climate scenarios, inflow events were projected to exceed the dam design inflow, with RCP8.5 indicating the highest inflow values, particularly later in the century, highlighting probability of overtopping risks. Late-century projections show inflow for ARI 50 under RCP8.5 exceeding PMF by 20%, while mid-century RCP6.0 results indicate a 15% higher inflow for ARI 50000. Early-century RCP4.5 shows a 10% increase for ARI 100000 compared to PMF. The study advocates adaptive dam safety management and flood protection measures. This research provides crucial insights for embankment dam owners, stressing the urgent need to address Batu Dam's vulnerability to extreme flooding amidst climate change and emphasizing proactive measures to fortify critical infrastructure and protect downstream communities.
Solar energy is a major type of renewable energy, and its estimation is important for decision-makers. This study introduces a new prediction model for solar radiation based on support vector regression (SVR) and the improved particle swarm optimization (IPSO) algorithm. The new version of algorithm attempts to enhance the global search ability for the PSO. In practice, the SVR method has a few parameters that should be determined through a trial-and-error procedure while developing the prediction model. This procedure usually leads to non-optimal choices for these parameters and, hence, poor prediction accuracy. Therefore, there is a need to integrate the SVR model with an optimization algorithm to achieve optimal choices for these parameters. Thus, the IPSO algorithm, as an optimizer is integrated with SVR to obtain optimal values for the SVR parameters. To examine the proposed model, two solar radiation stations, Adana, Antakya and Konya, in Turkey, are considered for this study. In addition, different models have been tested for this prediction, namely, the M5 tree model (M5T), genetic programming (GP), SVR integrated with four different optimization algorithms SVR-PSO, SVR-IPSO, Genetic Algorithm (SVR-GA), FireFly Algorithm (SVR-FFA) and the multivariate adaptive regression (MARS) model. The sensitivity analysis is performed to achieve the highest accuracy level of the prediction by choosing different input parameters. Several performance measuring indices have been considered to examine the efficiency of all the prediction methods. The results show that SVR-IPSO outperformed M5T and MARS.