Side orifices are commonly installed in the side of a main channel to spill or divert some flow from source lateral channels. The aim present study is accurate estimation discharge coefficient for through triangular (Δ-shaped) by applying three data-driven models including support vector machine (SVM), least squares (LSSVM) and improved gravity search algorithm (LSSVM-GSA). was estimated utiliz...

For the limitation of traditional information fusion technology in the mine gas safety class predicition, an intelligent algorithm is proposed in which Genetic Algorithms is adopted to optimize the parameters of the least squares support vector machine and establishes a multi-sensor information fusion model GA-LSSVM which overcomes the subjectivity and blindness on parameters selection, and thu...

In this study, a novel least square support vector machine (LSSVM) model integrated with gradient-based optimizer (GBO) algorithm is introduced for the assessment of water quality (WQ) parameters. For purpose, three stations, including Ahvaz, Armand, and Gotvand in Karun river basin, have been selected to electrical conductivity (EC) total dissolved solids (TDS). First, prove superiority LSSVM-...

Langrangian Support Vector Machine (LSVM) and Least Squares Support Vector Machine (LSSVM) are two quick and effective classification methods. In this paper, we first introduce the mathematical models for LSVM and LSSVM and analyze their properties. In the nonlinear case, Sherman-Morrison-Woodbury identity is not used to compute the inversion of a matrix. According to block computation of a mat...

Predicting the price of electricity is crucial for operation power systems. Short-term forecasting deals with forecasts from an hour to a day ahead. Hourly-ahead offer expected prices market participants before hours. This especially useful effective bidding strategies where amount can be reviewed or changed Nevertheless, many existing models have relatively low prediction accuracy. Furthermore...

This study presents a least squares support vector machine (LSSVM) based displacement prediction model for health monitoring of concrete dams. LSSVM is a novel machine learning technique. The model can produce similar good generalization performance and learns faster than the basic support vector machines in engineering problems. The advantages such as high prediction accuracy, fast training sp...

A fault diagnosis method for sensor fault based on ensemble empirical mode decomposition (EEMD) energy entropy and optimized structural parameters least squares support vector machine (LSSVM) is put forward in this paper. Firstly, the original output fault signals are pretreatment with EEMD, and then the EEMD energy entropy is extracted as the fault feature vector. Then the radial basis functio...

As the construction of energy internet progresses, proportion residential electricity consumption in end-use is increasing, peak load on grid growing year year, and seasonal regional power supply tensions, mainly for consumption, have become common problems across country. Accurate forecasting can provide strong data support operation demand response incentive setting response. For accuracy sta...

Wind speed (WS) is an important factor in wind power generation. Because of this, drastic changes the WS make it challenging to analyze accurately. Therefore, this study proposed a novel framework based on stacking ensemble machine learning (SEML) method. The application for modeling was developed at sixteen stations Iran. SEML method consists two levels. In particular, eleven (ML) algorithms s...