A precise predictive model is needed to obtain a clear understanding of the changing dissolved oxygen content in outdoor crab ponds, to assess how to reduce risk and to optimize water quality management. The uncertainties in the data from multiple sensors are a significant factor when building a dissolved oxygen content prediction model. To increase prediction accuracy, a new hybrid dissolved o...

This paper explores the Support Vector Machine and Least Square Support Vector Machine models in stock forecasting. Three prevailing forecasting techniques General Autoregressive Conditional Heteroskedasticity (GARCH), Support Vector Regression (SVR) and Least Square Support Vector Machine (LSSVM) are combined with the wavelet kernel to form three novel algorithms Wavelet-based GARCH (WL_GARCH)...

This research aims to estimate the overflow capacity of a curved labyrinth using different intelligent prediction models, namely adaptive neural-fuzzy inference system, support vector machine, M5 model tree, least-squares machine and machine–bat algorithm (LSSVM-BA). A total 355 empirical data for 6 congressional models were extracted from results laboratory study on models. The parameters upst...

Network traffic is a typical time-series data with strong lag and aftereffect, for the existence of local optimum, time-consuming and other defects in the method for the currently determining number of lags, this paper presents a combination of network traffic prediction method (GS-GA-LSSVM). At first, using geo-statistics (GS) to quickly determine the optimal lag order of network traffic, then...

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...

The fast and accurate classification of surrounding rock mass is the basis for tunnel design construction has significant value in engineering applications. Therefore, this paper proposes a method classifying predicting based on particle swarm optimization (PSO)–least squares support vector machine (LSSVM). premise research that data acquired from digital drilling technology are divided into tr...

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...

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...