Optimized Forecasting Air Pollution Model Based On Multi-Objective Staked Feature Selection Approach Using Deep Featured Neural Classifier

In recent days air pollution has been an essential issue affecting the environment nature leads to various natural causes. Especially the Covid-19 pandemic period has a variation environment changes due to vehicle controls and industrial facts at regular intervals. So air pollution has different scaling factors before and after the pandemic, period produces non-scaled data features. Many methodologies provide the differential solution to analyze the air quality measurements under various conditions to make warnings to avoid air pollution. By the impact of exiting forecasting, ML approaches do not provide the accuracy in precision levels because feature dependencies are non-relevant in high dimension nature. To create the best Air quality index, we need to improve the feature analysis and classification objectives to produce higher prediction performance. This paper proposes a new forecasting model based on the Multi-objective Staked Feature Selection Approach (MoSFS) using the Deep Featured Neural Classifier (DFNC) model to predict air pollution. Initially, the Successive Feature Defect Scaling Rate (SFDSR) was carried out Auto Regressive Integrated Moving Average (ARIMA) rate for finding variation dependencies. The multi-objective relational successive feature index was scaled using the Spider Herding Algorithm (SHA) to select the features based on these variations in feature limits. Then the chosen features get activated to logical activation function with Long Short Term Memory (LSTM) and trained with a Fuzzified Convolution Neural Network (F-CNN) to predict the class by variance. This resultant factor proves the performance of RMSE values attaining the best level to forecast the features and in precision rate produce higher performance in classification accuracy compared to the other system

Modelo predictivo de la variación de los niveles de contaminación en función del tráfico urbano utilizando herramientas de Inteligencia Artificial

Elaboración de un modelo predictivo de los niveles de contaminación, con el fin de identificar aquellas situaciones en las que el tráfico va a provocar que se superen determinados límites de contaminantes. El modelo se creará utilizando herramientas de Inteligencia Artificial

Artificial Neural Networks, Sequence-to-Sequence LSTMs, and Exogenous Variables as Analytical Tools for NO2 (Air Pollution) Forecasting: A Case Study in the Bay of Algeciras (Spain)

This study aims to produce accurate predictions of the NO2 concentrations at a specific station of a monitoring network located in the Bay of Algeciras (Spain). Artificial neural networks (ANNs) and sequence-to-sequence long short-term memory networks (LSTMs) were used to create the forecasting models. Additionally, a new prediction method was proposed combining LSTMs using a rolling window scheme with a cross-validation procedure for time series (LSTM-CVT). Two different strategies were followed regarding the input variables: using NO2 from the station or employing NO2 and other pollutants data from any station of the network plus meteorological variables. The ANN and LSTM-CVT exogenous models used lagged datasets of different window sizes. Several feature ranking methods were used to select the top lagged variables and include them in the final exogenous datasets. Prediction horizons of t + 1, t + 4 and t + 8 were employed. The exogenous variables inclusion enhanced the model's performance, especially for t + 4 (rho approximate to 0.68 to rho approximate to 0.74) and t + 8 (rho approximate to 0.59 to rho approximate to 0.66). The proposed LSTM-CVT method delivered promising results as the best performing models per prediction horizon employed this new methodology. Additionally, per each parameter combination, it obtained lower error values than ANNs in 85% of the cases