Experimental study of the effect of high-strength clogging-resistant permeable pavement (CRP) on the runoff using a rainfall simulator.

Permeable pavements play an effective role in reducing runoff by decreasing the impermeable area. But, conventional permeable pavements suffer disadvantages such as low resistance. To address this, the 'high-strength clogging-resistant permeable pavement (CRP)' has been developed. The present study aimed to evaluate the performance of the CRP model with varying percentages of coverage (A) of 25, 50, and 100%, slopes (S) of 1, 3, and 5%, as well as rainfall intensities (I) of 45, 55, 70, 90, 170, and 200 mm/h. Based on the results, there was an increase in A from 50 to 100% at I = 90 mm/h, decreased runoff coefficient (C) of 18, 15, and 13% at S of 1, 3, and 5%, respectively. At the same I, increasing S from 1 to 5% increased the C coefficient in A of 0, 25, 50, and 100% by 3, 31, 32, and 39%, respectively. Due to the ever-increasing urbanization and the subsequent increase in impervious areas, the risk of severe floods has greatly increased. Therefore, providing solutions such as the CRP model can help reduce flood risks in urban areas. The findings of this research can be used as a guide in the design of high-strength clogging-resistant permeable pavements in urban areas.

Laboratory study of the effect of vegetation and gravel on runoff parameters under variable rainfall intensities.

The present study aims to evaluate the performance of the impervious surface as a control (O), sandy loam substrate (SL), gravel (G), gravel with geocell layer (GGE), rosemary (R), rosemary with geocell layer (RRE), turf (T), and turf with geocell layer (TGE) in the reduction of runoff volume, time-to-start runoff (TR), runoff coefficient (C), time-to end runoff (TER), peak flow rate (PF), time to peak (TP), and time base (TB) in the laboratory dimension under three different scenarios of rainfall intensity and two different slopes using a rainfall simulator. The results revealed a significant difference between the data at the level of 5% in all cases. Generally, three rainfall scenarios for all hydrological parameters TR, TER, TP, TB, C, and PF were classified into different groups. In all cases, GGE treatment performed better than that of the rest of the test groups in reducing runoff and cumulative volume. Further, treatments O and GGE experienced the highest and lowest flow rates, respectively. For a specific scenario of rainfall intensity and slope, the value of C is the lowest for GGE treatment. Finally, the implementation of geocell in the pavements was able to delay the time to start runoff.

A hybrid constrained coral reefs optimization algorithm with machine learning for optimizing multi-reservoir systems operation.

The continuous growing demand for water, prolonged periods of drought, and climatic uncertainties attributed mainly to climate change mean surface water reservoirs more than ever need to be managed efficiently. Several optimization algorithms have been developed to optimize multi-reservoir systems operation, mostly during severe dry/wet seasons, to mitigate extreme-events consequences. Yet, convergence speed, presence of local optimums, and calculation-cost efficiency are challenging while looking for the global optimum. In this paper, the problem of finding an efficient optimal operation policy in multi-reservoir systems is discussed. The complexity of the long-term operating rules and the reservoirs' upstream and downstream joint-demands projected in recursive constraints make this problem formidable. The original Coral Reefs Optimization (CRO) algorithm, which is a meta-heuristic evolutionary algorithm, and two modified versions have been used to solve this problem. Proposed modifications reduce the calculation cost by narrowing the search space called a constrained-CCRO and adjusting reproduction operators with a reinforcement learning approach, namely the Q-Learning method (i.e., the CCRO-QL algorithm). The modified versions search for the optimum solution in the feasible region instead of the entire problem domain. The models' performance has been evaluated by solving five mathematical benchmark problems and a well-known continuous four-reservoir system (CFr) problem. Obtained results have been compared with those in the literature and the global optimum, which Linear Programming (LP) achieves. The CCRO-QL is shown to be very calculation-cost-effective in locating the global optimum or near-optimal solutions and efficient in terms of convergence, accuracy, and robustness.

An improved model based on the support vector machine and cuckoo algorithm for simulating reference evapotranspiration.

Reference evapotranspiration (ET0) plays a fundamental role in irrigated agriculture. The objective of this study is to simulate monthly ET0 at a meteorological station in India using a new method, an improved support vector machine (SVM) based on the cuckoo algorithm (CA), which is known as SVM-CA. Maximum temperature, minimum temperature, relative humidity, wind speed and sunshine hours were selected as inputs for the models used in the simulation. The results of the simulation using SVM-CA were compared with those from experimental models, genetic programming (GP), model tree (M5T) and the adaptive neuro-fuzzy inference system (ANFIS). The achieved results demonstrate that the proposed SVM-CA model is able to simulate ET0 more accurately than the GP, M5T and ANFIS models. Two major indicators, namely, root mean square error (RMSE) and mean absolute error (MAE), indicated that the SVM-CA outperformed the other methods with respective reductions of 5-15% and 5-17% compared with the GP model, 12-21% and 10-22% compared with the M5T model, and 7-15% and 5-18% compared with the ANFIS model, respectively. Therefore, the proposed SVM-CA model has high potential for accurate simulation of monthly ET0 values compared with the other models.

Integrated support vector regression and an improved particle swarm optimization-based model for solar radiation prediction.

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.

Experimental and numerical investigation of the effect of different shapes of collars on the reduction of scour around a single bridge pier.

The scour phenomenon around bridge piers causes great quantities of damages annually all over the world. Collars are considered as one of the substantial methods for reducing the depth and volume of scour around bridge piers. In this study, the experimental and numerical methods are used to investigate two different shapes of collars, i.e, rectangular and circular, in terms of reducing scour around a single bridge pier. The experiments were conducted in hydraulic laboratory at university of Malaya. The scour around the bridge pier and collars was simulated numerically using a three-dimensional, CFD model namely SSIIM 2.0, to verify the application of the model. The results indicated that although, both types of collars provides a considerable decrease in the depth of the scour, the rectangular collar, decreases scour depth around the pier by 79 percent, and has better performance compared to the circular collar. Furthermore, it was observed that using collars under the stream's bed, resulted in the most reduction in the scour depth around the pier. The results also show the SSIIM 2.0 model could simulate the scour phenomenon around a single bridge pier and collars with sufficient accuracy. Using the experimental and numerical results, two new equations were developed to predict the scour depth around a bridge pier exposed to circular and rectangular collars.