support vector regression

Wastewater treatment plants (WWTPs) play a vital role in protecting public health and the environment, but the treatment process itself is energy-intensive. Among the various stages, pumping stations stand out as major energy consumers, responsible for lifting wastewater through the treatment cycle. Traditionally, these stations have relied on fixed-speed pumps, leading to inefficiencies when dealing with variable inflow rates – a common occurrence in WWTPs. Recent advancements in artificial intelligence (AI) offer promising solutions for optimizing pump control strategies and minimizing energy consumption. AI techniques like machine learning and optimization algorithms can analyze real-time data, predict future inflow trends, and dynamically adjust pump speeds accordingly. Studies have demonstrated the effectiveness of AI-based pump control in reducing energy use by significant margins. This research explores a novel AI-based approach combining the Grey Wolf Optimization (GWO) algorithm and Support Vector Regression (SVR) for optimizing pump speed control in WWTP pumping stations. GWO, inspired by grey wolf hunting behavior, identifies optimal pump operation parameters to minimize energy consumption. SVR, a machine learning technique, predicts future pump speed requirements based on real-time data and the optimized GWO parameters. Utilizing real-world data from Zahedan Refinery's pumping station, this study aims to validate the proposed GWO-SVR approach and assess its potential for reducing energy consumption in WWTPs, thereby enhancing operational efficiency and sustainability.In this section, an optimization model for a wastewater pump is developed and presented. The pump is situated in a reservoir where the inflow pattern is randomly generated based on a real-world measured dataset. The Zahedan Wastewater Treatment Plant is located on a 24-hectare land in the east of the city, designed to serve a population of about 900,000 people in the future. The project includes 17 kilometers of transmission lines, a reservoir, and a pumping station. The hourly inflow data to the treatment plant was measured for a pumping station with a constant cross-sectional area of the reservoir of 12 square meters, and a minimum and maximum height of 1 and 9 meters, respectively, in the year 1400 (2021). Based on the 12 month data of 1400, the maximum average wastewater flow rate was 6.99 liters per second in June, and the maximum standard deviation was 68.27 in February (Table 1)The optimization problem is formulated to minimize daily energy consumption for pumping equation(12).Two models are compared:• Classic Model: Fixed pump speed operation• Optimization Model: GWO-based variable speed control for optimized energy usageSVR is introduced to predict pump speed based on data obtained from the optimization model.Discussion and The GWO algorithm is implemented to identify optimal pump operation parameters (Table 3). The SVR model is then employed to predict pump speed based on these optimized parameters. The optimized model is compared to a fixed-speed system, demonstrating significant energy savings. Figures 4 to 6 show the changes between constant-speed (ηcs) and variable-speed (ηopt) performance based on α and β for different time windows of Zahedan station in 2021. With increasing inflow rates (decreasing α), the variable-speed pump performance improves over the constant-speed pump, widening the gap between ηopt and ηcs. Conversely, with decreasing inflow rates (increasing α), this gap narrows. The parameter β, which regulates pump speed, increases performance for both pump types when raised. These optimizations were performed for 30, 60, and 120 minute time windows in 2021, with the variable-speed pump outperforming the constant-speed pump in all cases. Across all time windows, increasing the inflow rate (smaller α) results in a larger energy saving (ε) for the variable-speed pump over the constant-speed pump. With increasing β, which allows higher pump speeds, ε decreases for the same inflow rates (constant α). This is because at higher β values, the variable-speed pump behaves more like a constant-speed pump. The correlation coefficients comparing these optimization results to simulations in 2021 are 0.9996-0.9999 for pump speed and 0.9976-0.9999 for energy, indicating the high accuracy of the SVR simulations and GWO optimization. Figure 10 compares the optimized GWO and simulated SVR results for pump speed in a 60-minute window, while Figure 11 shows the same for pump energy consumption. The close agreement between the optimization and simulation demonstrates the effectiveness of the proposed GWO-SVR model. This study investigated a novel AI-based approach combining the Grey Wolf Optimization (GWO) algorithm and Support Vector Regression (SVR) model for optimizing pump speed control in wastewater treatment plant pumping stations. The GWO-SVR model demonstrated significant energy savings in the variable-flow pumping station compared to a fixed-speed system. The SVR model accurately predicted optimal pump speeds based on real-time data and GWO-optimized parameters, enabling efficient, dynamic pump control. The combined GWO-SVR approach provides a practical solution for WWTP operators to optimize pump control, achieve substantial energy savings, and contribute to more sustainable and cost-effective wastewater treatment practices. Its successful implementation in existing pumping stations can drive improved operational efficiency and environmental sustainability.Keywords: Energy efficiency - simulation - pumping station - gray wolf algorithm - support vector regression

Bridges are exposed to various micro and small damages during their service life. Due to the importance and role of these structures in transportation on roads, timely detection of damages in them has a special place. This research presents an innovative and efficient method for diagnosing and estimating micro and small damages to bridges. The proposed method is based on the combination of spectral finite element and modal strain energy-based damage index to determine the location of damages in the first step. Then the support vector regression technique is used to estimate the severity of damages in the second step. A new eight-node element with spectral finite element characteristics is defined in OpenSees software. Then, micro and small damages are modeled in a single-span beam and a double-span steel beam with spring supports. In the first step, after modal analysis of the structures, the modal strain energy-based damage index is calculated to determine the location of the damages. In the second step, using the modal strain energy-based damage indices calculated in the previous step, the support vector regression networks are trained to estimate the severity of the damages. The results show very high accuracy and optimal performance of the proposed method.

Villages located in Isfahan province are one of the areas prone to the spread of cutaneous leishmaniasis, which is characterized by the occurrence of wounds on the skin. To predict the future prevalence of cutaneous leishmaniasis, Continuous monitoring of the spatial distribution of this disease is essential. Disease modeling was performed using two machine learning algorithms called support vector regression (SVR) and multilayer perceptron neural network (MLP). The performance of these algorithms is evaluated using the RMSE index. Analysis of the results shows that SVR algorithm with RMSE = 0.170 compared to MLP with RMSE = 0.348 has better performance. Environmental factors include temperature, humidity, precipitation, altitude and wind speed as independent variables and Estimation of leishmaniasis density was used as a dependent variable in the modeling process, Of which (70%) were used for model training and the remaining (30%) for model evaluation. The results of spatial analysis index showed that The distribution pattern of cutaneous leishmaniasis in the years 1397 to 1399 was clustered.

Due to the difficulties of conducting tests, especially in weak rocks and the cost of these experiments, by examining the relationships between their mechanical and physical properties can be provided and reduce the cost of tests to identify mechanical properties (Minaeian and Ahangari, 2013; Azadan and Ahangari, 2014).     In this study, petrographic, physical and mechanical experiments on 62 cores of shale and marl of Gurpi Formation were conducted in Godar-Khosh dam site, west of Iran. Non-destructive tests were performed on cores according to the ISRM standard. Physical properties such as water absorption, density and porosity of the samples were determined according to the ISRM standard. Also, uniaxial compressive strength (UCS) test according to the ASTM standard D2938 (ASTM, 1986) was performed. For each sample, the modulus of dynamic elasticity (Ed) and the dynamic Poisson ratio were calculated (Goodman, 1989). Using statistical analysis, artificial neural network (ANN( and  support vector regression (SVR) with radial base kernel function, several relationships for estimating UCS, Es and shear wave velocity were presented. The root mean square error (RMSE), the mean absolute percentage error (MAPE) and the variance account for (VAF) were also used to evaluate the results.

The aim of this research is to construct and validate an optical sensor with a multi-beam ratio technology and artificial intelligence-based modelling (MLP & SVR) for suspended sediment measuring. After implementation of the new technology, the performance of the device was evaluated in two stages including calibration and validation. To attain this, various experimental tests carried out in the hydraulic laboratory of Water Research Institute of the Ministry of Energy. Reference turbidity meter and total suspended solids (TSS) were used to test the performance of the OBS technology. In calibration stage, 70% of TSS data were used and the remaining 30% of data were used to validate optical technology. The plotted calibration curves show a very good correlation between the optical voltage recorded by the sensors and the suspended sediment concentration. Also, SVR & MLP models were employed to improv results of suspended load prediction. The performance of optical device and also optical device with intelligence models were evaluated through four statistical indices namely, Mean Absolute Percentage Error (MAPE), Root mean square Error (RMSE), Nash–Sutcliffe coefficient (NSE), correlation coefficient (R) and coefficient of determination (R2). The results of this stage showed that the intelligence modelling could result in improvements of suspended load reported by optical technology. The best improvements obtained by MLP-optical technology. The results showed that values of validation indicators for MLP model are equal to 0.023, 7.608, 0.99, 0.99 and 0.99 respectively, which indicates the proper performance of the technology.