A deep learning approach for prediction of air quality index in a metropolitan city

A review on emerging artificial intelligence (AI) techniques for air pollution forecasting: Fundamentals, application and performance

Wireless Sensor Networks (WSN) consists of sensors used for sensing environmental conditions and many more applications in real world. Air pollution is a threat to the life of humans. To control the air pollution it is necessary to monitor the pollutant gases in periodically. Various air pollution monitoring systems using sensor network have been developed, deployed and tested in the literature. This paper presents a comparative study about the literature for air pollution monitoring systems based on the classification such as stationary air pollution monitoring systems, dynamic air pollution monitoring systems and pollution data analysis techniques. These pollution monitoring systems are compared based on the methodologies followed, microcontroller used, communication device used, pollutants analyzed using sensors, evaluation attributes, tested location and performance of the system. This paper also discusses the merits and demerits of the air pollution monitoring systems.

A Comprehensive Review of Wireless Sensor Networks Based Air Pollution Monitoring Systems

Statistically clustering air pollution can provide evidence of underlying spatial processes responsible for intensifying the concentration of contaminants. It may also lead to the identification of hotspots. The patterns can then be targeted to manage the concentration level of pollutants. In this regard, employing spatial autocorrelation indices as important tools is inevitable. In this study, general and local indices of Moran’s I and Getis-Ord statistics were assessed in their representation of the structural characteristics of carbon monoxide (CO) and fine particulate matter (PM2.5) polluted areas in Tehran, Iran, which is one of the most polluted cities in the world. For this purpose, a grid (200 m × 200 m) was applied across the city, and the inverse distance weighted (IDW) interpolation method was used to allocate a value to each pixel. To compare the methods of detecting clusters meaningfully and quantitatively, the pollution cleanliness index (PCI) was established. The results ascertained a high clustering level of the pollutants in the study area (with 99% confidence level). PM2.5 clusters separated the city into northern and southern parts, as most of the cold spots were situated in the north half and the hotspots were in the south. However, the CO hotspots also covered an area from the northeast to southwest of the city and the cold spots were spread over the rest of the city. The Getis-Ord’s PCI suggested a more polluted air quality than the Moran’s I PCI. The study provides a feasible methodology for urban planners and decision makers to effectively investigate and govern contaminated sites with the aim of reducing the harmful effects of air pollution on public health and the environment.

https://www.mdpi.com/2220-9964/6/9/270/pdf

An Assessment of Spatial Pattern Characterization of Air Pollution: A Case Study of CO and PM2.5 in Tehran, Iran

The prediction of groundwater quality is very important for the management of water resources and environmental activities. The present study has integrated a number of methods such as Geographic Information Systems (GIS) and Artificial Intelligence (AI) methodologies to predict groundwater quality in Kerman plain (including HCO3-, concentrations and Electrical Conductivity (EC) of groundwater). This research has investigated the abilities of Adaptive Neuro Fuzzy Inference System (ANFIS), the hybrid of ANFIS with Genetic Algorithm (GA), and Artificial Neural Network (ANN) techniques as well to predict the groundwater quality. Various combinations of monthly variability, namely rainfall and groundwater levels in the wells were used by two different neuro-fuzzy models (standard ANFIS and ANFIS-GA) and ANN. The results show that the ANFIS-GA method can present a more parsimonious model with a less number of employed rules (about 300% reduction in number of rules) compared to ANFIS model and improve the fitness criteria and so model efficiency at the same time (38.4% in R2 and 44% in MAPE). The study also reveals that groundwater level fluctuations and rainfall contribute as two important factors in predicting indices of groundwater quality.

http://jad.shahroodut.ac.ir/article_1086_d7ffa2f0daf71d6eb7945b2cef64f0ab.pdf

Adaptive Network-based Fuzzy Inference System-Genetic Algorithm Models for Prediction Groundwater Quality Indices: a GIS-based Analysis

Suspended particles have deleterious effects on human health and one of the reasons why Tehran is effected is its geographically location of air pollution. One of the most important ways to reduce air pollution is to predict the concentration of pollutants. This paper proposed a hybrid method to predict the air pollution in Tehran based on particulate matter less than 10 microns (PM10), and the information and data of Aghdasiyeh Weather Quality Control Station and Mehrabad Weather Station from 2007 to 2013. Generally, 11 inputs have been inserted to the model, to predict the daily concentration of PM10. For this purpose, Artificial Neural Network with Back Propagation (BP) with a middle layer and sigmoid activation function and its hybrid with Genetic Algorithm (BP-GA) were used and ultimately the performance of the proposed method was compared with basic Artificial Neural Networks along with (BP) Based on the criteria of - R 2 -, RMSE and MAE. The finding shows that BP-GA �� 2 = 0.54889 has higher accuracy and performance. In

http://jad.shahroodut.ac.ir/article_560_f1de11875bc661841af152fd207b422b.pdf

M Asghari Esfandani

Papers

Predicting air pollution in Tehran: Genetic algorithm and back propagation neural network