More frequent and harsh storms coupled with sea level rise are affecting Canada’s sensitive coastlines. This research studies Isle Madame in Cape Breton, Nova Scotia which has been designated by Natural Resource Canada as a sea level rise vulnerable coastal community in Canada. The research models the spatial and temporal impacts of sea level rise from storm surge by focusing on identifying vulnerable areas in the community via geographical information systems (GIS) using ArcGIS, as well as modeling dynamic coastal damage via system dynamics using STELLA. The research evaluates the impacts in terms of the environmental, social, cultural, economic pillars that profile the coastal community for a series of modelled Storm Scenarios. This research synthesizes information from a variety of sources including the coastal ecology and natural resources, as well as human society and socioeconomic indicators included in the four mentioned pillars. The objective of the research is to determine vulnerable areas on Isle Madame susceptible to storm damage, and consequently, to improve local community knowledge and preparedness to more frequent harsh storms. This research therefore presents a dynamic model for the evaluation of storm impacts in Isle Madame designed with the goal to help the community ultimately to plan and implement a strategy to adapt to pending environmental change.

More frequent and harsh storms coupled with sea level rise are affecting Canada’s sensitive coastlines. This research studies Isle Madame in Cape Breton, Nova Scotia which has been designated by Natural Resource Canada as a sea level rise vulnerable coastal community in Canada. The research models the spatial and temporal impacts of sea level rise from storm surge by focusing on identifying vulnerable areas in the community via geographical information systems (GIS) using ArcGIS, as well as modeling dynamic coastal damage via system dynamics using STELLA. The research evaluates the impacts in terms of the environmental, social, cultural, economic pillars that profile the coastal community for a series of modelled Storm Scenarios. This research synthesizes information from a variety of sources including the coastal ecology and natural resources, as well as human society and socioeconomic indicators included in the four mentioned pillars. The objective of the research is to determine vulnerable areas on Isle Madame susceptible to storm damage, and consequently, to improve local community knowledge and preparedness to more frequent harsh storms. This research therefore presents a dynamic model for the evaluation of storm impacts in Isle Madame designed with the goal to help the community ultimately to plan and implement a strategy to adapt to pending environmental change.

More frequent and harsh storms coupled with sea level rise are affecting Canada’s sensitive coastlines. This research studies Isle Madame in Cape Breton, Nova Scotia which has been designated by Natural Resource Canada as a sea level rise vulnerable coastal community in Canada. The research models the spatial and temporal impacts of sea level rise from storm surge by focusing on identifying vulnerable areas in the community via geographical information systems (GIS) using ArcGIS, as well as modeling dynamic coastal damage via system dynamics using STELLA. The research evaluates the impacts in terms of the environmental, social, cultural, economic pillars that profile the coastal community for a series of modelled Storm Scenarios. This research synthesizes information from a variety of sources including the coastal ecology and natural resources, as well as human society and socioeconomic indicators included in the four mentioned pillars. The objective of the research is to determine vulnerable areas on Isle Madame susceptible to storm damage, and consequently, to improve local community knowledge and preparedness to more frequent harsh storms. This research therefore presents a dynamic model for the evaluation of storm impacts in Isle Madame designed with the goal to help the community ultimately to plan and implement a strategy to adapt to pending environmental change.

More frequent and harsh storms coupled with sea level rise are affecting Canada’s sensitive coastlines. This research studies Isle Madame in Cape Breton, Nova Scotia which has been designated by Natural Resource Canada as a sea level rise vulnerable coastal community in Canada. The research models the spatial and temporal impacts of sea level rise from storm surge by focusing on identifying vulnerable areas in the community via geographical information systems (GIS) using ArcGIS, as well as modeling dynamic coastal damage via system dynamics using STELLA. The research evaluates the impacts in terms of the environmental, social, cultural, economic pillars that profile the coastal community for a series of modelled Storm Scenarios. This research synthesizes information from a variety of sources including the coastal ecology and natural resources, as well as human society and socioeconomic indicators included in the four mentioned pillars. The objective of the research is to determine vulnerable areas on Isle Madame susceptible to storm damage, and consequently, to improve local community knowledge and preparedness to more frequent harsh storms. This research therefore presents a dynamic model for the evaluation of storm impacts in Isle Madame designed with the goal to help the community ultimately to plan and implement a strategy to adapt to pending environmental change.

<p> Acid rain continues to be a major environmental problem. Canada has been monitoring indicators of acid rain in various ecosystems since the 1970s. This project focuses on the analysis of a selected subset of data generated by the Turkey Lakes Watershed (TLW) monitoring program from 1980 to 1997. TLW consists of a series of connected lakes where 6 monitoring stations are strategically located to measure the input from an upper stream lake into a down stream lake. Segment regression models with AR(1) errors and unknown point of change are used to summarize the data. Relative likelihood based methods are applied to estimate the point of change. For pH, all the regression parameters except autocorrelation have been found to change significantly between the model segments. This was not the case for SO4 2- where a single model was found to be adequate. In addition pH has been found to have a moderate increasing trend and pronounced seasonality while SO4 2- showed a dramatic decreasing trend but little seasonality. Multivariate dimension reduction methods are used to provide an overall graphical summary of the changes in TLW water system. We also report the result of applying segment regression for the analysis of first two principal components in selected stations. The results show that the efforts of the Canadian and US governments to reduce the emission of SO2 have been successful in controlling the acid rain problem in Eastern Canada. The project ends with suggestions for various extensions of the present work.</p> / Thesis / Master of Science (MSc)

Forecasting of highway traffic is a common practice for real traffic information system, and is of vital importance to traffic management and control on highways. As a typical time-series forecasting task, we want to propose a deep learning model to map the historical sensory traffic values (e.g., speed, flow) to future traffic forecasts. Prevailing traffic forecasting methods focus on the graph representation of the urban road. However, compared to the dense connectivity of urban road networks, highway traffic flows normally run on road segments of serial topology. This indicates that the highway traffic flows do not have the same type of spatial interaction, therefore motivating us to resort to a new forecasting paradigm. While traffic patterns can be intuitively represented by spatial-temporal (ST) images, this study transforms the traffic forecasting task into the conditional image generation task. Our approach explores the inherent properties of ST-images from the perspectives of physical meaning and traffic dynamics. An innovative deep learning based architecture is designed to process the ST-image, and a diffusion model is trained to obtain traffic forecasts by generating future ST-image based on the historical STimages. We demonstrate the effectiveness of the architecture in processing ST-image through ablation studies and the effectiveness of the model through comparison with popular baseline models, i.e., LSTM and T-GCN. / Prognos av vägtrafik är en vanlig praxis för riktiga trafikinformationssystem och är av vital betydelse för trafikhantering och kontroll på motorvägar. Som en typisk tidsserieförutsägelseuppgift vill vi föreslå en djupinlärningsmodell för att kartlägga historiska sensoriska trafikvärden (t.ex. hastighet, flöde) till framtida trafikprognoser. Rådande trafikprognosmetoder fokuserar på grafrepresentationen av stadsvägar. Jämfört med den täta anslutningen av stadsvägnät, löper motorvägstrafik normalt på vägsegment med seriell topologi. Detta indikerar att motorvägstrafikflöden inte har samma typ av rumslig interaktion, vilket motiverar oss att använda en ny prognosparadigm. Medan trafikmönster intuitivt kan representeras av spatial-temporala (ST) bilder, omvandlar denna studie trafikprognosuppgiften till en uppgift för betingad bildgenerering. Vår metod utforskar de inneboende egenskaperna hos ST-bilder från perspektiven fysisk betydelse och trafikdynamik. En innovativ djupinlärningsbaserad arkitektur är utformad för att behandla STbilden, och en diffusionsmodell tränas för att erhålla trafikprognoser genom att generera framtida ST-bilder baserat på historiska ST-bilder. Vi demonstrerar effektiviteten hos arkitekturen genom avbränningsstudier och modellens effektivitet genom jämförelse med populära baslinjemodeller, dvs. LSTM och T-GCN.