Vehicle Emission Prediction Using Remote Sensing Data And Machine Learning Techniques
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Vehicle Emission Prediction Using Remote Sensing Data and Machine Learning Techniques
More researchers are using remote sensing technology to measure real-world, on-road automobile emissions of nitric oxide (NO), one of the most important and frequently studied pollutants. Partnered with the National Institute of Water and Atmospheric Research (NIWA) in New Zealand, we aim to establish a robust NO emission factor prediction model using remote sensing data to forecast future emissions. We have conducted this research using real-world data that were collected over a 11-year span between 2005 and 2015. The experimental results have shown that the vehicle emission patterns are continuously changing and the relevance of remote sensing data for future predictions decays as they get older. We propose a three-step machine learning approach to establish this model. We use quantile regression forest (QRF) as the base algorithm and use random forests variable importance measure to validate and interpret the features. We have found empirically, the model is more accurate than models that are based on three other algorithms: linear regression, linear model based recursive partitioning, random forest. Lastly, we have extracted human-interpretable prediction rules from our quantile regression forest based model, using the decision based rule extraction algorithm. The rules are useful to generalise prediction logic from a black-box model such as our QRF based model.
Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing
As computer and space technologies have been developed, geoscience information systems (GIS) and remote sensing (RS) technologies, which deal with the geospatial information, have been rapidly maturing. Moreover, over the last few decades, machine learning techniques including artificial neural network (ANN), deep learning, decision tree, and support vector machine (SVM) have been successfully applied to geospatial science and engineering research fields. The machine learning techniques have been widely applied to GIS and RS research fields and have recently produced valuable results in the areas of geoscience, environment, natural hazards, and natural resources. This book is a collection representing novel contributions detailing machine learning techniques as applied to geoscience information systems and remote sensing.
Intelligent Human Centered Computing
Author: Siddhartha Bhattacharyya
language: en
Publisher: Springer Nature
Release Date: 2025-04-30
This book features high-quality research papers presented at the Second Doctoral Symposium on Human Centered Computing (HUMAN 2024), jointly organized by Computer Society of India, Kolkata Chapter and Sister Nivedita University, West Bengal, on March 30, 2024. This book discusses the topics of modern human centered computing and its applications. The book showcases the fusion of human sciences (social and cognitive) with computer science (human–computer interaction, signal processing, machine learning, and ubiquitous computing).