AbstractsOther

A variety of environmental problems increasingly attract academic research in order to protect ecosystems and minimise negative effects on human health. Advanced computational environmental analysis technologies have the potential to detect, monitor and perhaps effectively control these problems. However, computational environmental analysis is a complex and difficult problem to solve. The problems associated with environmental analysis are the underlying data. These data are collected from sub-optimal positions in urban and rural areas. The limited number of monitoring stations in the network means that we are collecting enough samples over time, however, insufficient data samples are collected across the monitored area. We determined the difficulty associated with computational analysis of environmental data via a critical review of existing approaches in the literature. Our review confirmed the biggest problem was associated with data collection, including noise introduced into the data stream, the big data problem in the form of an endless data stream, and missing data samples caused by ineffective equipment or poor placement of the monitoring equipment. In this thesis, we document our research into computational environmental methods for addressing land usage and air quality problems. The detection of land use change is a process of identifying differences in the state of a phenomenon by observing time-lapsed landscape imagery. Motivated by a simple neural pattern recognition mechanism, we propose a novel “one-step-more” incremental learning change detection method. In this method, an agent discovers knowledge from the first image using pixel-level incremental learning. When we detect changes in the subsequent image, the discovered knowledge model is updated and ready for the next change detection iteration. This is what we have called the “one-step more” incremental learning method. Powered by incremental data modelling techniques, the system demonstrates the capability of continuously detecting time sequenced imagery. Additionally, the method is shown to be computationally inexpensive when initializing and updating the change detection model. Land encroachment monitoring is essential to assist the economic growth, sustainable resource use and environmental protection of a city. We investigate land encroachment on public parks in the area of Auckland New Zealand, in which the proposed “one-step-more” method employed to analyse 26 Auckland parks. The obtained average region of interest (ROI) detection accuracy is 99.91% on five popular park related objects i.e. fences, houses, parks, trees and roads. The effectiveness of the proposed method is demonstrated on four categories of encroachment: periimanent land cover & use, and temporary & physical boundary encroachment. We document two detailed and comprehensive investigations of computational methods for air quality analysis. The first investigation is indoor emission source detection and the second is outdoor air quality prediction. Emission…