Hybrid Vector Autoregression Feedforward Neural Network with Genetic Algorithm Model for Forecasting Space-Time Pollution Data

Abstract

The exposure rate to air pollution in most urban cities is really a major concern because it results to a life-threatening consequence for human health and wellbeing. Furthermore, the accurate estimation and continuous forecasting of pollution levels is a very complicated task. In this paper, one of the space-temporal models, a vector autoregressive (VAR) with neural network (NN) and genetic algorithm (GA) was proposed and enhanced. The VAR could tackle the issue of multivariate time series, NN for nonlinearity, and GA for parameter estimation determination. Therefore, the model could be used to make predictions, such as the information of series and location data. The applied methods were on the pollution data, including NOX, PM2.5, PM10, and SO2 in Taipei, Hsinchu, Taichung, and Kaohsiung. The metaheuristics genetic algorithm was used to enhance the proposed methods during the experiments. In conclusion, the VAR-NN-GA gives a good accuracy when metric evaluation is used. Furthermore, the methods can be used to determine the phenomena of 10 years air pollution in Taiwan.

Modeling Industrial Energy Demand in Relation to Subsector Manufacturing Output and Climate Change: Artificial Neural Network Insights

“…Second, lack of development of the ANN model and comparison with other models. Based on the goal of this research is to use ANN as an alternative to industrial energy demand modeling, the ANN approach used is a basic model with no further development, such as the use of a hybrid model [49,52,63] or adopting more models, both parametric and nonparametric [21]. As a result, the issue for further research is to address the limitations of this study, such as using larger datasets, employing a hybrid model, and employing more diverse estimation techniques so that the estimation results obtained are much better.…”

“…ANN as an alternative to statistical modeling and forecasting has been widely used today because, in some literature, it shows better performance when compared to the regression model [45,47,48]. In addition, ANN is also used to identify, model, and predict complex systems in various cases [49][50][51][52][53], including that was recently used to forecast cryptocurrency volatility [54,55]. To the best of our knowledge, no previous studies have used the ANN approach to model industrial energy demand and its relationship to subsector manufacturing output and climate change in Taiwan.…”

Albatross analytics a hands-on into practice: statistical and data science application

“…That's also feasible by believing that the model encompasses a wide variety of distributions. We are leaving future studies to combine h-likelihood in the deep learning [39,40,[55][56][57][58][59], and using this framework towards spatial and remote sensing [60][61][62][63][64], hybrid forecasting [65][66][67][68][69][70][71][72][73][74][75][76][77][78][79][80], and more advanced disease detection cases using image detection [81][82][83][84][85][86][87][88][89][90]. = 0 .…”

An Improved Equilibrium Optimizer Algorithm and Its Application in LSTM Neural Network

“…The neural networks (NN) model is a prominent example of such nonlinear and nonparametric models that do not make assumptions about the parametric form of the functional relationship between the variables. Several studies have been done relating to the application of NN, such as Caraka and Chen used vector autoregressive (VAR) to improve NN for space-time pollution date forecasting modeling [28], then they used VAR-NN-PSO model to predicting PM 2.5 in air [29]. Suhartono and Prastyo applied Generalized Space-Time Autoregressive (GSTAVR) to FFNN for forecasting oil production [30].…”