Development of flood prediction models using machine learning techniques

Flooding and flash flooding events damage infrastructure elements and pose a significant threat to the safety of the people residing in susceptible regions. There are some methods that government authorities rely on to assist in predicting these events in advance to provide warning, but such methodologies have not kept pace with modern machine learning. To leverage these algorithms, new models must be developed to efficiently capture the relationships among the variables that influence these events in a given region. These models can be used by emergency management personnel to develop more robust flood management plans for susceptible areas. The research investigates machine learning techniques to analyze the relationships between multiple variables influencing flood activities in Missouri. The first research contribution utilizes a deep learning algorithm to improve the accuracy and timelessness of flash flood predictions in Greene County, Missouri. In addition, a risk analysis study is conducted to advise the existing flash flood management strategies for the region. The second contribution presents a comparative analysis of different machine learning techniques to develop a classification model and predict the likelihood of flash flooding in Missouri. The third contribution introduces an ensemble of Long Short-Term Memory (LSTM) deep learning models used in conjunction with clustering to create virtual gauges and predict river water levels at unmonitored locations. The LSTM models predict river water levels 4 hours in advance. These outputs empower emergency management decision makers with an advanced warning to better implement flood management plans in regions of Missouri not served with river gauge monitoring --Abstract, page iv

Verification of Solutions to the Sensor Location Problem

Traffic congestion is a serious problem with large economic and environmental impacts. To reduce congestion (as a city planner) or simply to avoid congested channels (as a road user), one might like to accurately know the flow on roads in the traffic network. This information can be obtained from traffic sensors, devices that can be installed on roads or intersections to measure traffic flow. The sensor location problem is the problem of efficiently locating traffic sensors on intersections such that the flow on the entire network can be extrapolated from the readings of those sensors. I build on current research concerning the sensor location problem to develop conditions on a traffic network and sensor configuration such that the flow can be uniquely extrapolated from the sensors. Specifically, I partition the network by a method described by Morrison and Martonosi (2010) and establish a necessary and sufficient condition for uniquely extrapolatable flow on a part of that network that has certain flow characteristics. I also state a different sufficient but not necessary condition and include a novel proof thereof. Finally, I present several results illustrating the relationship between the inputs to a general network and the flow solution

Space-time exposure modelling of troposheric O3 in Europe

Exposure models need to be developed which can be applied at the continental scale, while still reflecting local variations in exposure conditions. Land use regression (LUR) has been widely adopted to describe the spatial variations in air pollutants over the longer term but not for short-term time-variable exposures. This study, therefore, aimed to develop and validate a space-time O3 model applicable to epidemiological studies investigating the health effects of short-term (e.g. daily) O3 exposures at the small-area scale. A geographical information system (GIS) was developed, incorporating data from 1211 O3 monitoring sites across Western Europe and a range of predictors, stored as 100m grids, including land cover, roads, topography and meteorology. The spatial model consisted of a LUR model representing the long-term average for years 2001-2007. The monitoring sites were classified, using multivariate statistical techniques, into 13 site types based on a set of descriptive indicators, then 13 temporal models represented by time functions were produced – one for each site type. These were linked to the spatial model using probability of group membership as a weighting factor. Finally, local meteorological data were incorporated to produce the full space-time model to predict daily concentrations for point locations. The spatial and temporal models were individually evaluated based on agreement with measurement data from a reserved subset of 20% of the monitoring sites. The performance of the spatial model was similar to other continental LUR models (R2=0.67; RMSE=7.64 μg/m3), while performance of the temporal models ranged from 0.3 to 0.5 (R2). Including local meteorological data into the full spatial-temporal model improved correlation with the concentrations measured at 30 monitoring sites in the Netherlands (R2= 0.42 without; R2=0.53 with meteorology). Modelling daily O3 over large areas at a fine spatial scale is possible using this approach. Overall model performance was further improved as the temporal period was aggregated to weekly or monthly. The model was applied to mothers in two birth cohorts in the European Study of Cohorts for Air Pollution Effects (ESCAPE) to provide daily O3 exposure estimates, which can be aggregated as needed to provide individualised exposures based on date of birth

Vehicle telematics for safer, cleaner and more sustainable urban transport:a review

Urban transport contributes more than a quarter of the global greenhouse gas emissionns that drive climate change; it also produces significant air pollution emissions. Furthermore, vehicle collisions kill and seriously injure 1.35 and 60 million people worldwide, respectively, each year. This paper reviews how vehicle telematics can contribute towards safer, cleaner and more sustainable urban transport. Collection methods are reviewed with a focus on technical challenges, including data processing, storage and privacy concerns. We review how vehicle telematics can be used to estimate transport variables, such as traffic flow speed, driving characteristics, fuel consumption and exhaustive and non-exhaustive emissions. The roles of telematics in the development of intelligent transportation systems (ITSs), optimised routing services, safer road networks and fairer insurance premia estimation are highlighted. Finally, we outline the potential for telematics to facilitate new-to-market urban mobility technologies, signalised intersections, vehicle-to-vehicle (V2V) communication networks and other internet-of-things (IoT) and internet-of-vehicles (IoV) technologies

Monitoring-based adaptive water level thresholds for bridge scour risk management

Riverbed scour is the leading cause of bridge failure worldwide. Recent developments in sensor technology for structures have resulted in more bridges being instrumented and monitored. However, alongside scour monitoring systems, there is a need of techniques to handle the data obtained and exploit them to inform the management of the bridge scour risk. This paper illustrates the development of a decision support system (DSS) for bridge scour risk management, which is based on a probabilistic framework for scour risk estimation, enhanced by real-time information from scour monitoring systems and in line with current risk procedures used by transport agencies. The proposed DSS provides bridge operators with adaptive measurement-informed water level thresholds triggering bridge closure to traffic under heavy floods. The application of the DSS is illustrated by considering a case study of three bridges at risk of scour managed by Transport Scotland. It is shown that information from scour sensors within the proposed DSS allows reducing the uncertainty in the scour estimates and yields adaptive water level thresholds triggering bridge closure to traffic that can differ significantly from those currently considered by transport agencies. This can ultimately result in a reduction of false alarms and unnecessary bridge closures

Traffic flow reconstruction by solving indeterminacy on traffic distribution at junctions

Abstract The knowledge of the real time traffic flow status in each segment of a whole road network in a city or area is becoming fundamental for a large number of smart services such as: routing, planning, dynamic tuning services, healthy walk, etc. Rescue teams, police department, and ambulances need to know with high precision the status of the network in real time. On the other hand, the costs to obtain this information either with direct measures meant to add instruments on the whole network or acquiring data from international providers such as Google, TomTom, etc. is very high. The traditional modeling and computing approaches are not satisfactory since they are based on many assumptions that typically are doomed to change over time, as it occurs with traffic distribution at junctions; in short they cannot cover the whole network with the needed precision. In this paper, the above problem has been addressed providing a solution granting any traffic flow reconstruction with high precision and solving the indeterminacy of traffic distribution at junctions for large networks. The identified solution can be classified as a stochastic relaxation technique and resulted affordable on a parallel architecture based on GPU. The result has been obtained in the framework of the Sii-Mobility national project on smart city transport systems in Italy, a very large research project, and it is at present exploited in a number of cities/regions across Europe and by a number of research projects (Snap4City, TRAFAIR) of the European Commission