Journal article
Intelligent prediction of aliphatic and aromatic hydrocarbons in Caspian Sea sediment using a neural network based on particle swarm optimization
Petroleum Science and Technology, Vol.37(24), pp.2364-2373
17/12/2019
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Source: InCites
Abstract
In this paper an intelligent model is proposed to predict the amount of organic pollutants in Caspian Sea sediment based on a feed forward artificial neural network (ANN) optimized by particle swarm optimization (PSO) algorithm. Organic pollutants have carcinogenesis and mutagenesis properties which are derived from anthropogenic and natural sources. The PSO-ANN was developed by experimental data collected from different literature. The statistical parameters prove the satisfactory performance of the proposed PSO- ANN model. A good correlation was obtained between the predicted organic pollutants and the experimental data for test, train and validation data were 0.996, 0.997, 0.993, respectively.
Details
- Title
- Intelligent prediction of aliphatic and aromatic hydrocarbons in Caspian Sea sediment using a neural network based on particle swarm optimization
- Creators
- Javad Sayyad Amin - Department of Marine Industries, Caspian Sea Basin Research Center, University of GuilanHossein Rajabi Kuyakhi - Department of Chemical Engineering, University of GuilanAlireza Bahadori - Southern Cross University
- Publication Details
- Petroleum Science and Technology, Vol.37(24), pp.2364-2373
- Publisher
- Taylor & Francis
- Identifiers
- 991012926977302368
- Academic Unit
- School of Environment, Science and Engineering; National Centre for Flood Research; Faculty of Science and Engineering
- Language
- English
- Resource Type
- Journal article