3 research outputs found
Stacked Autoencoders for Outlier Detection in Over-the-Horizon Radar Signals
Detection of outliers in radar signals is a considerable challenge in maritime surveillance applications. High-Frequency Surface-Wave (HFSW) radars have attracted significant interest as potential tools for long-range target identification and outlier detection at over-the-horizon (OTH) distances. However, a number of disadvantages, such as their low spatial resolution and presence of clutter, have a negative impact on their accuracy. In this paper, we explore the applicability of deep learning techniques for detecting deviations from the norm in behavioral patterns of vessels (outliers) as they are tracked from an OTH radar. The proposed methodology exploits the nonlinear mapping capabilities of deep stacked autoencoders in combination with density-based clustering. A comparative experimental evaluation of the approach shows promising results in terms of the proposed methodology’s performance
Application of machine learning in operational flood forecasting and mapping
Considering the computational effort and expertise required to simulate 2D
hydrodynamic models, it is widely understood that it is practically impossible to run these
types of models during a real-time flood event. To allow for real-time flood forecasting
and mapping, an automated, computationally efficient and robust data driven modelling
engine - as an alternative to the traditional 2D hydraulic models - has been proposed. The
concept of computationally efficient model relies heavily on replacing time consuming
2D hydrodynamic software packages with a simplified model structure that is fast,
reliable and can robustly retains sufficient accuracy for applications in real-time flood
forecasting, mapping and sequential updating.
This thesis presents a novel data-driven modelling framework that uses rainfall data from
meteorological stations to forecast flood inundation maps. The proposed framework takes
advantage of the highly efficient machine learning (ML) algorithms and also utilities the
state-of-the-art hydraulic models as a system component. The aim of this research has
been to develop an integrated system, where a data-driven rainfall-streamflow forecasting
model sets up the upstream boundary conditions for the machine learning based
classifiers, which then maps out multi-step ahead flood extents during an extreme flood
event.
To achieve the aim and objectives of this research, firstly, a comprehensive investigation
was undertaken to search for a robust ML-based multi-step ahead rainfall-streamflow
forecasting model. Three potential models were tested (Support Vector Regression
(SVR), Deep Belief Network (DBN) and Wavelet decomposed Artificial Neural Network
(WANN)). The analysis revealed that SVR-based models perform most efficiently in
forecasting streamflow for shorter lead time. This study also tested the portability of
model parameters and performance deterioration rates.
Secondly, multiple ML-based models (SVR, Random Forest (RF) and Multi-layer
Perceptron (MLP)) were deployed to simulate flood inundation extents. These models
were trained and tested for two geomorphologically distinct case study areas. In the first
case of study, of the models trained using the outputs from LISFLOOD-FP hydraulic
model and upstream flow data for a large rural catchment (Niger Inland Delta, Mali). For
the second case of study similar approach was adopted, though 2D Flood Modeller
software package was used to generate target data for the machine learning algorithms
and to model inundation extent for a semi-urban floodplain (Upton-Upon-Severn, UK).
In both cases, machine learning algorithms performed comparatively in simulating
seasonal and event based fluvial flooding.
Finally, a framework was developed to generate flood extent maps from rainfall data
using the knowledge learned from the case studies. The research activity focused on the
town of Upton-Upon-Severn and the analysis time frame covers the flooding event of
October-November 2000. RF-based models were trained to forecast the upstream
boundary conditions, which were systematically fed into MLP-based classifiers. The
classifiers detected states (wet/dry) of the randomly selected locations within a floodplain
at every time step (e.g. one hour in this study). The forecasted states of the sampled
locations were then spatially interpolated using regression kriging method to produce
high resolution probabilistic inundation (9m) maps. Results show that the proposed data
centric modelling engine can efficiently emulate the outcomes of the hydraulic model
with considerably high accuracy, measured in terms of flood arrival time error, and
classification accuracy during flood growing, peak, and receding periods.
The key feature of the proposed modelling framework is that, it can substantially reduce
computational time, i.e. ~14 seconds for generating flood maps for a flood plain of ~4
km2
at 9m spatial resolution (which is significantly low compared to a fully 2D
hydrodynamic model run time)