A review of the inter-correlation of climate change, air pollution and urban sustainability using novel machine learning algorithms and spatial information science

Urban Climate ◽  
2021 ◽  
Vol 40 ◽  
pp. 100989
Author(s):  
Abdul-Lateef Balogun ◽  
Abdulwaheed Tella ◽  
Lavania Baloo ◽  
Naheem Adebisi
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Air Pollution ◽  
Information Science ◽  
Spatial Information ◽  
Learning Algorithms ◽  
Urban Sustainability ◽  
Machine Learning Algorithms ◽  
Spatial Information Science

PM2.5 Concentration Prediction for Air Pollution using Machine Learning Algorithms

2019 ◽  
Vol 28 (1) ◽  
pp. 349-354 ◽  
Author(s):  
Ahmed Samy Abd El Aziz Moursi ◽  
Marwa Shouman ◽  
Ezz El-din Hemdan ◽  
Nawal El-Fishawy
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Concentration Prediction ◽  
Pm2.5 Concentration

scholarly journals Modelling hydrological responses under climate change using machine learning algorithms – semi-arid river basin of peninsular India

2020 ◽  
Vol 3 (1) ◽  
pp. 481-498
Author(s):  
G. Sireesha Naidu ◽  
M. Pratik ◽  
S. Rehana
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Random Forest ◽  
River Basin ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Peninsular India ◽  
Hydrological Responses ◽  
Semi Arid

Abstract Catchment scale conceptual hydrological models apply calibration parameters entirely based on observed historical data in the climate change impact assessment. The study used the most advanced machine learning algorithms based on Ensemble Regression and Random Forest models to develop dynamically calibrated factors which can form as a basis for the analysis of hydrological responses under climate change. The Random Forest algorithm was identified as a robust method to model the calibration factors with limited data for training and testing with precipitation, evapotranspiration and uncalibrated runoff based on various performance measures. The developed model was further used to study the runoff response under climate change variability of precipitation and temperatures. A statistical downscaling model based on K-means clustering, Classification and Regression Trees and Support Vector Regression was used to develop the precipitation and temperature projections based on MIROC GCM outputs with the RCP 4.5 scenario. The proposed modelling framework has been demonstrated on a semi-arid river basin of peninsular India, Krishna River Basin (KRB). The basin outlet runoff was predicted to decrease (13.26%) for future scenarios under climate change due to an increase in temperature (0.6 °C), compared to a precipitation increase (13.12%), resulting in an overall reduction in water availability over KRB.

scholarly journals Air Quality Prediction by Classification of Supervised Machine Learning

2020 ◽  
Vol 9 (4) ◽  
pp. 516-521
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
User Interface ◽  
Air Quality ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Model Parameters ◽  
Quality Prediction ◽  
Air Quality Prediction

Generally, air pollution refer to the release of various pollutants into the air which are threatening the human health and planet as well. The air pollution is the major dangerous vicious to the humanity ever faced. It causes major damage to animals, plants etc., if this keeps on continuing, the human being will face serious situations in the upcoming years. The major pollutants are from the transport and industries. So, to prevent this problem major sectors have to predict the air quality from transport and industries .In existing project there are many disadvantages. The project is about estimating the PM2.5 concentration by designing a photograph based method. But photographic method is not alone sufficient to calculate PM2.5 because it contains only one of the concentration of pollutants and it calculates only PM2.5 so there are some missing out of the major pollutants and the information needed for controlling the pollution .So thereby we proposed the machine learning techniques by user interface of GUI application. In this multiple dataset can be combined from the different source to form a generalized dataset and various machine learning algorithms are used to get the results with maximum accuracy. From comparing various machine learning algorithms we can obtain the best accuracy result. Our evaluation gives the comprehensive manual to sensitivity evaluation of model parameters with regard to overall performance in prediction of air high quality pollutants through accuracy calculation. Additionally to discuss and compare the performance of machine learning algorithms from the dataset with evaluation of GUI based user interface air quality prediction by attributes.

scholarly journals IoT BASED AIR AND SOUND POLLUTION MONITIORING SYSTEM USING MACHINE LEARNING ALGORITHMS

2020 ◽  
Vol 2 (1) ◽  
pp. 13-25
Author(s):  
M.RAMANA REDDY
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Monitoring System ◽  
Naive Bayes ◽  
Learning Algorithms ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Pollution Monitoring ◽  
Sensor Data ◽  
Raspberry Pi

Air pollution is the largest environmental and public health challenge in the world today. Air pollution leads to adverse effects on human health, climate and ecosystem. Air is getting polluted because of release of Toxic gases by industries, vehicular emissions and increased concentration of harmful gases and particulate matter in the atmosphere. In order to overcome these issues an IoT based air and sound pollution monitoring system is designed. To design this monitoring system, machine learning algorithms K-NN and Naive Bayes are used. K-Nearest Neighbour and Naive Bayes are machine learning algorithms used to predict the status of pollution present in the environment. In this system, analog to digital converter, global service mobile communication, temperature sensor, humidity sensor, carbon monoxide and sound sensors are interfaced with raspberry pi using serial cable. The sensor data is uploaded in thinkspeak (IoT) and webpage. This data is compared with the trained data to check accuracy. To calculate the accuracy of both algorithms, Python code is developed using python software tool.

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