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

Estimating Air Pollution Levels Using Machine Learning

Air pollution has emerged as a substantial concern, especially in developing countries worldwide. An important aspect of this issue is the presence of PM2.5. Air pollutants with a diameter of 2.5 or less micrometers are known as PM2.5. Due to their size, these particles are a serious health risk and can quickly infiltrate the lungs, leading to a variety of health problems. Due to growing concerns about air pollution, technology like automatic air quality measurement can offer beneficial assistance for both personal and business decisions. This research suggests an ensemble machine learning model that can efficiently replace the standard air quality estimation techniques, which need several instruments and setup and have large financial expenditures for equipment acquisition and maintenance

WQVP: An API enabled Open Data Machine Learning based Solution for Water Quality Visualization and Prediction

Water is an essential component required by living bodies for their survival. In today’s world, most of the water utilization is done by human beings. Due to this, there is a lot of adverse impact on water bodies. As human consumption of water increases, their pollution also increases. In order to control pollution impact and take measures to reduce water pollution, several methods have been proposed by researchers. Water Quality Index measures are one such method being adopted and used to measure harmful constituents of water. In recent times initiatives have been taken by international and national governing bodies to provide data through Open Data Initiatives that can be publicly made available. This data fetched in real time through APIs can be used for providing data analysis to naïve natives of the place with better understanding features like visualizations. Machine learning based techniques have proved to be a great tool for providing unsupervised learning in this area. We have implemented an API enabled Open Data Machine Learning based Solution for Water Quality Visualization and Prediction for Australian Rivers

Optimized machine learning model for air quality index prediction in major cities in India

Industrial advancements and utilization of large amount of fossil fuels, vehicle pollution, and other calamities increases the Air Quality Index (AQI) of major cities in a drastic manner. Major cities AQI analysis is essential so that the government can take proper preventive, proactive measures to reduce air pollution. This research incorporates artificial intelligence in AQI prediction based on air pollution data. An optimized machine learning model which combines Grey Wolf Optimization (GWO) with the Decision Tree (DT) algorithm for accurate prediction of AQI in major cities of India. Air quality data available in the Kaggle repository is used for experimentation, and major cities like Delhi, Hyderabad, Kolkata, Bangalore, Visakhapatnam, and Chennai are considered for analysis. The proposed model performance is experimentally verified through metrics like R-Square, RMSE, MSE, MAE, and accuracy. Existing machine learning models, like k-nearest Neighbor, Random Forest regressor, and Support vector regressor, are compared with the proposed model. The proposed model attains better prediction performance compared to traditional machine learning algorithms with maximum accuracy of 88.98% for New Delhi city, 91.49% for Bangalore city, 94.48% for Kolkata, 97.66% for Hyderabad, 95.22% for Chennai and 97.68% for Visakhapatnam city

Implementing heuristic-based multiscale depth-wise separable adaptive temporal convolutional network for ambient air quality prediction using real time data

In many emerging nations, rapid industrialization and urbanization have led to heightened levels of air pollution. This sudden rise in air pollution, which affects global sustainability and human health, has become a significant concern for citizens and governments. While most current methods for predicting air quality rely on shallow models and often yield unsatisfactory results, our study explores a deep architectural model for forecasting air quality. We employ a sophisticated deep learning structure to develop an advanced system for ambient air quality prediction. We utilize three publicly available databases and real-world data to obtain accurate air quality measurements. These four datasets undergo a data cleaning to yield a consolidated, cleaned dataset. Subsequently, the Fused Eurasian Oystercatcher-Pathfinder Algorithm (FEO-PFA)—a dual optimization method combining the Eurasian Oystercatcher Optimizer (EOO) and Pathfinder Algorithm (PFA)—is applied. This method aids in selecting weighted features, optimizing weights, and choosing the most relevant attributes for optimal results. These optimal features are then incorporated into the Multiscale Depth-wise Separable Adaptive Temporal Convolutional Network (MDS-ATCN) for the ambient Air Quality Prediction (AQP) process. The variables within MDS-ATCN are further refined using the proposed FEO-PFA to enhance predictive accuracy. An empirical analysis is performed to compare the efficacy of our proposed model with traditional methods, underscoring the superior effectiveness of our approach. The average cost function is reduced to 5.5%, the MAE to 28%, and the RMSE to 14% by the suggested method, according to the performance research conducted with regard to all datasets