scholarly journals A Hybrid Neural Network–Particle Swarm Optimization Informed Spatial Interpolation Technique for Groundwater Quality Mapping in a Small Island Province of the Philippines

Toxics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 273
Author(s):  
Kevin Lawrence M. De De Jesus ◽  
Delia B. Senoro ◽  
Jennifer C. Dela Dela Cruz ◽  
Eduardo B. Chan
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Heavy Metal ◽  
Particle Swarm Optimization ◽  
Spatial Interpolation ◽  
Swarm Optimization ◽  
Heavy Metal Concentrations ◽  
Interpolation Methods ◽  
Interpolation Technique ◽  
Spatial Interpolation Methods

Water quality monitoring demands the use of spatial interpolation techniques due to on-ground challenges. The implementation of various spatial interpolation methods results in significant variations from the true spatial distribution of water quality in a specific location. The aim of this research is to improve mapping prediction capabilities of spatial interpolation algorithms by using a neural network with the particle swarm optimization (NN-PSO) technique. Hybrid interpolation approaches were evaluated and compared by cross-validation using mean absolute error (MAE) and Pearson's correlation coefficient (R). The governing interpolation techniques for the physicochemical parameters of groundwater (GW) and heavy metal concentrations were the geostatistical approaches combined with NN-PSO. The best methods for physicochemical characteristics and heavy metal concentrations were observed to have the least MAE and R values, ranging from 1.7 to 4.3 times and 1.2 to 5.6 times higher than the interpolation technique without the NN-PSO for the dry and wet season, respectively. The hybrid interpolation methods exhibit an improved performance as compared to the non-hybrid methods. The application of NN-PSO technique to spatial interpolation methods was found to be a promising approach for improving the accuracy of spatial maps for GW quality.

Regional optimal allocation for reducing waste loads via artificial neural network and particle swarm optimization: a case study of ammonia nitrogen in Harbin, northeast China

2014 ◽  
Vol 70 (7) ◽  
pp. 1211-1219 ◽  
Author(s):  
Ying Zhao ◽  
Liang Guo ◽  
Yi Wang ◽  
Peng Wang
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Particle Swarm Optimization ◽  
Northeast China ◽  
Particle Swarm ◽  
Quality Standard ◽  
Ammonia Nitrogen ◽  
Swarm Optimization ◽  
Water Quality Standard

Cutting external waste loads can improve water quality. Allocation for reducing waste loads should consider changing variables, such as river flows and pollutant emissions. A particle swarm optimization (PSO) method and coupling artificial neural network (ANN) models have been applied to optimize reduction rates of ammonia nitrogen (NH3-N) loads from sewage outlets in Harbin, northeast China. For the planned water quality functional section (WQFS), the NH3-N concentration is related to emitted pollutant loads and can be well predicted by ANN linkage models. Further, NH3-N load reduction rates of all outlets are optimized by PSO with the water quality standard target. The highest NH3-N concentrations occur in January and February, a typical low-flow period in Harbin. The results delivered optimum NH3-N reduction rates for the five outlets, for January and February 2011. All predicted NH3-N concentrations after the reduction meet the water quality standard. The results indicate that the outlet with the highest NH3-N load has the biggest reduction rate in each WQFS, and outlets in the WQFS with higher background NH3-N concentrations need to cut more NH3-N loads. Decision-makers should not only focus on the outlet with the highest NH3-N emission load, but also ensure that the NH3-N concentration of upper WQFS meets the water quality goal.

Analysis of Feature Reduction Techniques for Online News Popularity Prediction

2018 ◽  
Vol 4 (10) ◽  
pp. 6
Author(s):  
Shivangi Bhargava ◽  
Dr. Shivnath Ghosh
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Naive Bayes ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Online News ◽  
Feature Reduction ◽  
Support Vector ◽  
Swarm Optimization

News popularity is the maximum growth of attention given for particular news article. The popularity of online news depends on various factors such as the number of social media, the number of visitor comments, the number of Likes, etc. It is therefore necessary to build an automatic decision support system to predict the popularity of the news as it will help in business intelligence too. The work presented in this study aims to find the best model to predict the popularity of online news using machine learning methods. In this work, the result analysis is performed by applying Co-relation algorithm, particle swarm optimization and principal component analysis. For performance evaluation support vector machine, naïve bayes, k-nearest neighbor and neural network classifiers are used to classify the popular and unpopular data. From the experimental results, it is observed that support vector machine and naïve bayes outperforms better with co-relation algorithm as well as k-NN and neural network outperforms better with particle swarm optimization.

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