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...

The LS-SVM (least square support vector machines) is applied to solve the practical problems of small samples and non-linear prediction better and it is suitable for the DGA in power transformers. The selection of the parameters, impact on the result of the diagnosis greatly, so it is necessary to optimize these parameters. The parameters of Support Vector Machine are optimized using GA (Geneti...

Fault diagnosis is a challenging topic for complex industrial systems due to the varying environments such find themselves in. In order improve performance of fault diagnosis, this study designs novel approach by using particle swarm optimization (PSO) with wavelet mutation and least square support (LSSVM). The implementation entails following three steps. Firstly, original signals are decompos...

Accurate forecast of water demand is one of the main problems in developing management strategy for the optimal control of water supply system. In this paper, a hybrid model which combines empirical mode decomposition (EMD) and least square support vector machine (LSSVM) model is proposed to forecast water demand. This hybrid is formulated specifically to address in modelling water demand that ...

Air temperature is an essential climatic component particularly in water resources management and other agro-hydrological/meteorological activities planning This paper examines the prediction capability of three machine learning models, least square support vector (LSSVM), group method data handling neural network (GMDHNN) classification regression trees (CART) air forecasting using monthly Ast...

With the rapid development of the photovoltaic industry, fault monitoring is becoming an important issue in maintaining the safe and stable operation of a solar power station. In order to diagnose the fault types of photovoltaic array, a fault diagnosis method that is based on the Least Squares Support Vector Machine (LSSVM) in the Bayesian framework is put forward. First, based on the elaborat...

The use of machine learning tools in medical diagnosis is increasing gradually. This is mainly because the effectiveness of classification and recognition systems has improved in a great deal to help medical experts in diagnosing diseases. This study aims at diagnosing Liver Disorder with a new hybrid machine learning method. By hybridizing LSSVM with Fuzzy Weighting Pre-processing, a method wa...

In recent years, with the deepening of China’s electricity sales side reform and electricity market opening up gradually, the forecasting of electricity consumption (FoEC) becomes an extremely important technique for the electricity market. At present, how to forecast the electricity accurately and make an evaluation of results scientifically are still key research topics. In this paper, we pro...