Urban flooding is estimated to cause £270 million pounds worth of damage each year in England and Wales alone. There has, therefore, been a clear need to develop improved methods of identifying intervention strategies to reduce flood risk in urban environments. This paper describes ground-work performed towards evaluating the relative suitability of several algorithms applied to multi-objective optimisation of flood risk intervention strategies in an urban drainage network. An effective methodology is described for reducing an array of return period/duration rainfall files to a minimum, and it is described how this methodology makes possible comparisons of optimisation algorithms. This work has been undertaken as part of a STREAM-IDC EngD project which is a collaborative effort between the University of Exeter, and HR Wallingford

Features extraction has a fundamental value in enhancing the scalability and adaptability n of medical image processing framework. The outcome of this stage has a tremendous effect on the reliability of the medical application being developed, particularly disease classification and prediction. The challenging side of features extraction frameworks, in relation to medical images, is influenced by the anatomical and morphological structure of the image which requires a powerful extraction system that highlights high- and low- level features. The complementary of both feature types reinforces the medical image content-based retrieval and allows to access visible structures as well as an in-depth understanding of related deep hidden components. Several existing techniques have been used towards extracting high- and low-level features separately, including Deep Learning based approaches. However, the fusion of these features remains a challenging task. Towards tackling the drawback caused by the lack of features combination and enhancing the reliability of features extraction methods, this paper proposes a new hybrid features extraction framework that focuses on the fusion and optimal selection of high- and low-level features. The scalability and reliability of the proposed method is achieved by the automated adjustment of the final optimal features based on real-time scenarios resulting an accurate and efficient medical images disease classification. The proposed framework has been tested on two different datasets to include BraTS and Retinal sets achieving an accuracy rate of 97% and 98.9%, respectively.

Optimisation algorithms could potentially provide extremely valuable guidance towards improved intervention strategies and/or designs for water systems. The application of these algorithms in this domain has historically been hindered by the extreme computational cost of performing hydraulic modelling of water systems. This is because running an optimisation algorithm generally involves running a very large number of simulations of the system being optimised. In this paper, a novel optimisation approach is described, based upon the ‘learning evolution model for multi-objective optimisation’ algorithm. This approach uses deep learning artificial neural network meta-models to reduce the number of simulations of the water system required, without reducing the accuracy of the optimisation results. This is then compared to an industry standard optimisation approach, showing results with increased speed of convergence and equivalent or improved accuracy. Therefore, demonstrating that this approach is suitable for use in highly computationally demanding areas such as water systems optimisation.