208 research outputs found
Understanding riverine hydroecological response to climate change : development of a coupled modelling framework
Full text linkDescribed as the most essential natural resource, rivers rank amongst those ecosystems
most sensitive to climate change. The 2018 Brisbane Declaration highlights the pressing
need to consider the resultant hydroecological impact. To this end, this thesis looks to
develop a coupled hydrological-hydroecological modelling framework, an exciting first
step under the new research agenda.
Initially, the focus lies on developing current understanding of the hydroecological relationship through consideration of potential delays in hydroecological response, alongside
refinement of current modelling practice. There follows consideration of whether hydrological models can preserve ecologically relevant characteristics of the flow regime, as
determined through hydroecological modelling efforts. Limiting factors are identified and
an alternative hydrological modelling approach established. A holistic depiction of uncertainty is central to all developments.
The framework is developed with reference to a principal case study, the groundwater-fed River Nar, Norfolk; validation of the component models is achieved through additional
case-studies. The hydrological model, forced with climate change projections, is used to
simulate changes in the flow regime. This output then serves as input to the coupled
hydroecological model. It is thus possible to assess the impact of climate change on hydroecological response in a quantitative manner.
Given data limitations, the framework is best suited to applications at the regional scale or
by flow regime type. Its importance lies in the potential to inform water resources adaptation, as well as advancing the fields of hydroecological and hydrological modelling.
Scope for further research centres around the wider socio-economic context, as recommended under the Brisbane Declaration
Application of machine learning in operational flood forecasting and mapping
Full text linkConsidering 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)
Enhancing energy demand forecasting and data imputation using deep learning : an integrated approach
Full text linkThis PhD thesis introduces an integrated approach that leverages deep learning
techniques to advance household electricity demand forecasting and data imputation within the UK energy sector. The research focuses on creating a novel system
incorporating state-of-the-art machine learning solutions for electricity demand processing and prediction. The study involves data collection from appropriate electricity demand datasets, conducting comprehensive exploratory data analysis to uncover
underlying patterns. A framework is established to process these datasets, encompassing data imputation, outlier handling, transformations, and feature scaling.
A novel missing value imputation model is developed, employing a Transformer neural network and a K-means clustering algorithm to address missing data effectively.
Subsequently, a forecasting framework for short-term residential load prediction is
presented. This modelling framework integrates a Bayesian optimisation strategy,
feature decomposition techniques, feature engineering, and percentile-based bias correction algorithms with a CNN-LSTM network to enhance prediction accuracy.
The research contributes significantly to the field of household electricity demand
forecasting and data imputation by offering a scalable and transferable framework.
The application of these methodologies yields valuable insights, not only for the
UK energy sector but also for broader applications, enabling precise predictions
and efficient demand data processing. The findings promote energy efficiency and
sustainable energy management practices.Engineering and Physical Sciences Research Council (EPSRC) funding
Noise-induced cooperative dynamics and its control
Full text linkEThOS - Electronic Theses Online ServiceGBUnited Kingdo
Developing a new design approach to estimate design flow rate in non-residential buildings
Full text linkMaking an accurate estimation of peak water demand in buildings is essential for
engineers and designers in order to ensure proper sizing of water supply systems, storage
tanks, boilers, and booster pumps. Over recent years, the amount of potable water used in
buildings has reduced considerably as a result of the prevalence of water-efficient
appliances and a heightened awareness of the need to conserve water. This has, in part,
led to oversizing of water supply networks; a phenomenon that has given cause for
concern to those responsible for the design of building plumbing systems. This oversizing
problem does not only result in a material and financial cost, it also has negative health
consequences.
In the UK, despite a clear reduction in consumption at end-use points, traditional design
approaches are still used for determining design flow rate. Although different design
methods have been presented in various British standards and guidance documents, all
use the Loading Unit (LU) approach, which is based on the application of probabilistic
techniques, to estimate the design flow for both residential and non-residential buildings.
In recent studies, the focus has generally been on residential buildings and there has been
little, if any, research to assess the validity of current design methods for non-residential
buildings. This study, therefore, focuses on developing a new design approach to estimate
demand flow in non-residential buildings.
This research starts by providing background information on the water situation in the
UK and discusses the reasons for oversizing and its consequences. Water demand is also
discussed, as is water conservation, per capita water consumption and the demand from
micro-components. In addition, the history of system design and the most commonly used
UK design approaches are discussed. After undertaking a critical review and
comprehensive investigation of statistical methods and recent studies used to estimate
demand flow, a new design methodology for estimating water demand, specifically for
non-residential buildings, is introduced. This has also allowed for the presentation of a
new stochastic model, namely the Water Demand Estimation Model (WDEM).
The model is underpinned by the interaction between users and the provision of sanitary
appliances in conjunction with the generation of a comprehensive range of probabilities
to capture all possible simultaneous uses of appliances. The Monte Carlo technique has been applied to calculate flow rate values based on a given number of users. A specific
type of non-residential building i.e. the ‘workplace’ has been selected for application of
the model and for which new design equations have been derived. Taking into account
the water saving appliances used in modern plumbing systems, five design equations have
been derived based on efficiency levels of corresponding appliances. In order to validate
the model and to assess its accuracy, high quality flow rate data was gathered from three
case study buildings. The effectiveness of the WDEM and its impact on the oversizing of
water systems has been confirmed by comparing simulated, measured and design flow
rates. The results show that the simulated demand is very close to the measured flow rate,
and that its use results in a significant reduction of design flow rate compared to those
determined by using current design codes.
The main outcome of this study is hence a novel approach for the estimation of demand
flow for non-residential buildings and a set of design equations to estimate the
simultaneous demand flow rate for workplaces. This new approach will be of value to all
engineers and designers who seek to establish a more accurate estimation of water
demand in buildings
Developing a probabilistic tool for assessing the risk of overheating in buildings for future climates
Full text linkThe effect of projected climate change on building performance is currently a growing research area. Building designers and architects are becoming more concerned that buildings designed for the current climatemight not provide adequate working and living environments in the coming decades. Advice is needed to guide how existing buildings might be adapted to cope with this future climate, as well as guidance for new building design to reduce the chances of the building failing in the future. The Low Carbon Futures Project, as part of the Adaptation and Resilience to Climate Change (ARCC) programme in the UK, is looking at methods ofintegrating the latest climate projections from the UK Climate Impact Programme (UKCIP) into building simulation procedures. The main obstacle to this objective is that these projections are probabilistic in nature; potentially thousands of equally-probably climate-years can be constructed that describe just a single scenario. The project is therefore developing a surrogate procedure that will use regression techniques to assimilate this breadth of climate information into the building simulation process
Isolation and Identification of Crude Triacontanol from Rice Bran Wax
Full text linkIn present investigation crude triacontanol was isolated and identified from rice bran wax. Triacontanol was isolated by saponification and extraction method. The obtained mixture is crude Triacontanol. It was analyzed by Gas Chromatography (GC) and melting point method. Purity of triacontanol was 13.33%. 1Department of Botany, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad (M.S.), India; 2Department of Botany, University of Pune, Pune (M.S.), India* Corresponding Author, Email: [email protected] Cite This Article As: Sandhya Jaybhay, Pankaj Chate and Avinash Ade. 2010. Isolation and Identification of Crude Triacontanol from Rice Bran Wax. J. Exp. Sci. 1(2): 26
Towards an overheating risk tool for building design
Full text linkPurpose – The work set out to design and develop an overheating risk tool using the UKCP09 climate projections that is compatible with building performance simulation software. The aim of the tool is to exploit the Weather Generator and give a reasonably accurate assessment of a building's performance in future climates, without adding significant time, cost or complexity to the design team's work.Methodology/approach – Because simulating every possible future climate is impracticable, the approach adopted was to use principal component analysis to give a statistically rigorous simplification of the climate projections. The perceptions and requirements of potential users were assessed through surveys, interviews and focus groups.Findings – It is possible to convert a single dynamic simulation output into many hundreds of simulation results at hourly resolution for equally probable climates, giving a population of outcomes for the performance of a specific building in a future climate, thus helping the user choose adaptations that might reduce the risk of overheating. The tool outputs can be delivered as a probabilistic overheating curve and feed into a risk management matrix. Professionals recognized the need to quantify overheating risk, particularly for non-domestic buildings, and were concerned about the ease of incorporating the UKCP09 projections into this process. The new tool has the potential to meet these concerns.Originality/value – The paper is the first attempt to link UKCP09 climate projections and building performance simulation software in this way and the work offers the potential for design practitioners to use the tool to quickly assess the risk of overheating in their designs and adapt them accordingly
India's National Population Policy (2000): An Evaluation
No full textprofessional paper for the fulfillment of the Masters of Public Policy degreeThis paper examines the quality of India’s family planning practice under the National Population Policy (2000) or NPP-2000. The intent of NPP-2000 is to eliminate unmet contraceptive needs by providing high quality reproductive healthcare. In particular, the NPP-2000 aims to address flaws in healthcare infrastructure and to achieve a total fertility rate of 2.1 births per woman by 2010. Unfortunately, the implementation difficulties of past years persist in the era of NPP-2000. Indian families are subject to poorly-trained healthcare personnel and insufficient medical supplies, among other setbacks. Using interviews with family planning professionals and data from quantitative
and qualitative studies, the following analysis exposes widespread variation in the quality of family planning practice. Additionally, the author proposes strategies to address unmet contraceptive needs in northern states and among disadvantaged populations.Agrawal, Sandhya. (2009). India's National Population Policy (2000): An Evaluation. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/50283
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