Prediction and parameter optimization are effective methods for mine personnel to control blast-induced ground vibration. However, the challenge of prediction lies in multi-factor multi-effect nature open-pit blasting. This study proposes a hybrid intelligent model predict vibrations using least-squares support vector machine (LSSVM) optimized by particle swarm algorithm (PSO). Meanwhile, multi...

Least squares support vector machine (LSSVM) is a learning algorithm based on statistical theory. Itsadvantages include robustness and calculation simplicity, it has good performance in the data processingof small samples. The LSSVM model lacks sparsity unable to handle large-scale problem, this articleproposes an method mixture kernel sparse This reduces theinitial training set sub-dataset usi...

In order to effectively solve the problems of low prediction accuracy and calculation efficiency existing methods for estimating economic loss in a subway station engineering project due rainstorm flooding, new intelligent model is developed using sparrow search algorithm (SSA), least-squares support vector machine (LSSVM) mean impact value (MIV) method. First, this study, 11 input variables ar...

Successful river flow time series forecasting is a primary goal and an essential procedure required in the planning and water resources management. River flow data are important for engineers to design, build and operate various water projects and development. The monthly river flow data taken from Department of Irrigation and Drainage, Malaysia are used in this study. This study aims to develo...

This paper proposes an effective model based on the least squares support vector machines (LSSVM) and the particle swarm optimization (PSO), termed PSO-LSSVM, for prediction of natural gas consumption, as an important energy resource. The salient feature of mapping nonlinear data into high dimension feature space, distinguishes LS-SVM as a powerful approach for forecasting and estimation. Optim...

Aimed at the submarine uncertainties and model’s impreciseness, an integrated RS-Chaos-LSSVM model is put forward because of nonlinearity and time-variability of AUV heading control system. The LSSVM is a typical nonlinear regression modeling based on small sample. Its parameters are optimized by Chaos algorithm. It is to gain the optimal model and get the higher accurate. By using Rough theory...

The internal temperature of the pigsty has a great impact on pigs. Keeping in within certain range is pressing problem environmental control. current regulation method based mainly manual and simple automatic There rarely intelligent control, such direct methods have problems as low control accuracy, high energy consumption untimeliness, which can easily lead to occurrence heat stress condition...

Predicting reservoir water levels helps manage droughts and floods. level is complex because it depends on factors such as climate parameters human intervention. Therefore, predicting needs robust models. Our study introduces a new model for levels. An extreme learning machine, the multi-kernel least square support vector machine (MKLSSVM), developed to predict of in Malaysia. The also novel op...

The accurate prediction of significant wave height (SWH) offers major safety improvements for coastal and ocean engineering applications. However, the phenomenon is nonlinear nonstationary, which makes any work a non-straightforward task. aim research presented in this paper to improve predicted via hybrid algorithm. Firstly, an empirical mode decomposition (EMD) used preprocess data, are decom...