Intelligent feature selection for neural regression : techniques and applications

Feature Selection (FS) and regression are two important technique categories in Data Mining (DM). In general, DM refers to the analysis of observational datasets to extract useful information and to summarise the data so that it can be more understandable and be used more efficiently in terms of storage and processing. FS is the technique of selecting a subset of features that are relevant to the development of learning models. Regression is the process of modelling and identifying the possible relationships between groups of features (variables). Comparing with the conventional techniques, Intelligent System Techniques (ISTs) are usually favourable due to their flexible capabilities for handling real‐life problems and the tolerance to data imprecision, uncertainty, partial truth, etc. This thesis introduces a novel hybrid intelligent technique, namely Sensitive Genetic Neural Optimisation (SGNO), which is capable of reducing the dimensionality of a dataset by identifying the most important group of features. The capability of SGNO is evaluated with four practical applications in three research areas, including plant science, civil engineering and economics. SGNO is constructed using three key techniques, known as the core modules, including Genetic Algorithm (GA), Neural Network (NN) and Sensitivity Analysis (SA). The GA module controls the progress of the algorithm and employs the NN module as its fitness function. The SA module quantifies the importance of each available variable using the results generated in the GA module. The global sensitivity scores of the variables are used determine the importance of the variables. Variables of higher sensitivity scores are considered to be more important than the variables with lower sensitivity scores. After determining the variables’ importance, the performance of SGNO is evaluated using the NN module that takes various numbers of variables with the highest global sensitivity scores as the inputs. In addition, the symbolic relationship between a group of variables with the highest global sensitivity scores and the model output is discovered using the Multiple‐Branch Encoded Genetic Programming (MBE‐GP). A total of four datasets have been used to evaluate the performance of SGNO. These datasets involve the prediction of short‐term greenhouse tomato yield, prediction of longitudinal dispersion coefficients in natural rivers, prediction of wave overtopping at coastal structures and the modelling of relationship between the growth of industrial inputs and the growth of the gross industrial output. SGNO was applied to all these datasets to explore its effectiveness of reducing the dimensionality of the datasets. The performance of SGNO is benchmarked with four dimensionality reduction techniques, including Backward Feature Selection (BFS), Forward Feature Selection (FFS), Principal Component Analysis (PCA) and Genetic Neural Mathematical Method (GNMM). The applications of SGNO on these datasets showed that SGNO is capable of identifying the most important feature groups of in the datasets effectively and the general performance of SGNO is better than those benchmarking techniques. Furthermore, the symbolic relationships discovered using MBE‐GP can generate performance competitive to the performance of NN models in terms of regression accuracies

Projecting Climate Dependent Coastal Flood Risk With a Hybrid Statistical Dynamical Model

ABSTRACT: Numerical models for tides, storm surge, and wave runup have demonstrated ability to accurately define spatially varying flood surfaces. However these models are typically too computationally expensive to dynamically simulate the full parameter space of future oceanographic, atmospheric, and hydrologic conditions that will constructively compound in the nearshore to cause both extreme event and nuisance flooding during the 21st century. A surrogate modeling framework of waves, winds, and tides is developed in this study to efficiently predict spatially varying nearshore and estuarine water levels contingent on any combination of offshore forcing conditions. The surrogate models are coupled with a time-dependent stochastic climate emulator that provides efficient downscaling for hypothetical iterations of offshore conditions. Together, the hybrid statistical-dynamical framework can assess present day and future coastal flood risk, including the chronological characteristics of individual flood and wave-induced dune overtopping events and their changes into the future. The framework is demonstrated at Naval Base Coronado in San Diego, CA, utilizing the regional Coastal Storm Modeling System (CoSMoS; composed of Delft3D and XBeach) as the dynamic simulator and Gaussian process regression as the surrogate modeling tool. Validation of the framework uses both in-situ tide gauge observations within San Diego Bay, and a nearshore cross-shore array deployment of pressure sensors in the open beach surf zone. The framework reveals the relative influence of large-scale climate variability on future coastal flood resilience metrics relevant to the management of an open coast artificial berm, as well as the stochastic nature of future total water levels.This work was funded by the Strategic Environmental Research Development Program (DOD/SERDP RC-2644). Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. F. J. Mendez, A. Rueda, and L. Cagigal acknowledge the partial funding from the Spanish Ministry of Science and Innovation, project Beach4cast PID2019-107053RB-I00. The authors thank the Scripps Center for Coastal Studies for their efforts to deploy, recover, and process surf zone pressure sensor data used as validation in this study. The authors thank Melisa Menendez for sharing GOW2 hindcast data for Southern California. The authors thank the sea-level rise projection authors for developing and making the sea-level rise projections available, multiple funding agencies for supporting the development of the projections, and the NASA Sea-Level Change Team for developing and hosting the IPCC AR6 Sea-Level Projection Tool

Data mining using intelligent systems : an optimized weighted fuzzy decision tree approach

Data mining can be said to have the aim to analyze the observational datasets to find relationships and to present the data in ways that are both understandable and useful. In this thesis, some existing intelligent systems techniques such as Self-Organizing Map, Fuzzy C-means and decision tree are used to analyze several datasets. The techniques are used to provide flexible information processing capability for handling real-life situations. This thesis is concerned with the design, implementation, testing and application of these techniques to those datasets. The thesis also introduces a hybrid intelligent systems technique: Optimized Weighted Fuzzy Decision Tree (OWFDT) with the aim of improving Fuzzy Decision Trees (FDT) and solving practical problems. This thesis first proposes an optimized weighted fuzzy decision tree, incorporating the introduction of Fuzzy C-Means to fuzzify the input instances but keeping the expected labels crisp. This leads to a different output layer activation function and weight connection in the neural network (NN) structure obtained by mapping the FDT to the NN. A momentum term was also introduced into the learning process to train the weight connections to avoid oscillation or divergence. A new reasoning mechanism has been also proposed to combine the constructed tree with those weights which had been optimized in the learning process. This thesis also makes a comparison between the OWFDT and two benchmark algorithms, Fuzzy ID3 and weighted FDT. SIx datasets ranging from material science to medical and civil engineering were introduced as case study applications. These datasets involve classification of composite material failure mechanism, classification of electrocorticography (ECoG)/Electroencephalogram (EEG) signals, eye bacteria prediction and wave overtopping prediction. Different intelligent systems techniques were used to cluster the patterns and predict the classes although OWFDT was used to design classifiers for all the datasets. In the material dataset, Self-Organizing Map and Fuzzy C-Means were used to cluster the acoustic event signals and classify those events to different failure mechanism, after the classification, OWFDT was introduced to design a classifier in an attempt to classify acoustic event signals. For the eye bacteria dataset, we use the bagging technique to improve the classification accuracy of Multilayer Perceptrons and Decision Trees. Bootstrap aggregating (bagging) to Decision Tree also helped to select those most important sensors (features) so that the dimension of the data could be reduced. Those features which were most important were used to grow the OWFDT and the curse of dimensionality problem could be solved using this approach. The last dataset, which is concerned with wave overtopping, was used to benchmark OWFDT with some other Intelligent Systems techniques, such as Adaptive Neuro-Fuzzy Inference System (ANFIS), Evolving Fuzzy Neural Network (EFuNN), Genetic Neural Mathematical Method (GNMM) and Fuzzy ARTMAP. Through analyzing these datasets using these Intelligent Systems Techniques, it has been shown that patterns and classes can be found or can be classified through combining those techniques together. OWFDT has also demonstrated its efficiency and effectiveness as compared with a conventional fuzzy Decision Tree and weighted fuzzy Decision Tree

Estimation of nearshore wave transmission for submerged breakwaters using a data-driven predictive model

The functional design of submerged breakwaters is still developing, particularly with respect to modelling of the nearshore wave field behind the structure. This paper describes a method for predicting the wave transmission coefficients behind submerged breakwaters using machine learning algorithms. An artificial neural network using the radial-basis function approach has been designed and trained using laboratory experimental data expressed in terms of non-dimensional parameters. A wave transmission coefficient calculator is presented, based on the proposed radial-basis function model. Predictions obtained by the radial-basis function model were verified by experimental measurements for a two dimensional breakwater. Comparisons reveal good agreement with the experimental results and encouraging performance from the proposed model. Applying the proposed neural network model for predictions, guidance is given to appropriately calculate wave transmission coefficient behind two dimensional submerged breakwaters. It is concluded that the proposed predictive model offers potential as a design tool to predict wave transmission coefficients behind submerged breakwaters. A step-by-step procedure for practical applications is outlined in a user-friendly form with the intention of providing a simplified tool for preliminary design purposes. Results demonstrate the model’s potential to be extended to three dimensional, rough, permeable structures

Wave overtopping and crown wall stability of cube and Cubipod-armored mound breakwaters

[EN] The influence of the type of armor on wave overtopping on mound breakwaters is usually represented by the roughness factor. However, different values of roughness factor for the same armor unit are given in the literature. Thus, the roughness factor depends not only on the type of armor, number of layers and permeability but also on the formula and database considered. In the present thesis, a new methodology based on bootstrapping techniques is developed and applied to characterize the roughness factors for different armor units. Differences up to 20% appeared when comparing the optimum roughness factors with those given in the literature. Armor porosity greatly affects the roughness factor and the armor stability: higher armor porosities reduce wave overtopping as well as hydraulic stability. Therefore, armor porosity values usually recommended in the literature should be used to avoid damage during lifetime. Formulas with few variables are easy to apply but they allow the roughness factor to absorb the information not explicitly included in the formula. However, the CLASH neural network avoids this problem and gives excellent estimation for wave overtopping on mound breakwaters. In this thesis, a new formula which emulates the behavior of the CLASH neural network is developed. The new formula has 16 parameters, six dimensionless input variables (Rc/Hm0, Ir, Rc/h, Gc/Hm0, Ac/Rc and a toe berm variable based on Rc/h) and two reduction factors (¿f and ¿ß). The new formula is built-up after systematic simulations using the CLASH neural network and provides the lowest prediction error. Wave overtopping on mound breakwaters can be minimized by increasing the crest freeboard, usually with a concrete crown wall. Crown walls must resist wave loads and armor earth pressure to be stable. In the present study, small-scale test results with cube- and Cubipod-armored mound breakwaters are used to develop a new estimator for calculating horizontal and up-lift forces from waves. The new formulas include four dimensionless input variables (¿f Ru0.1%/Rc, (Rc-Ac)/Ch, ¿(L_m/G_c ) and Fc/Ch) and the crown wall geometry. The roughness factor selected for overtopping prediction is used to consider the type of armor. Up-lift forces decreased sharply with increasing foundation levels. The new formulas provide the lowest error when predicting wave forces on crown walls.[ES] La influencia del tipo de elemento del manto sobre le rebase de diques en talud se caracteriza habitualmente mediante el factor de rugosidad (¿f). Sin embargo, en la literatura existen diferentes valores del factor de rugosidad para el mismo tipo de elemento. El factor de rugosidad no depende solo del tipo de elemento, número de capas y permeabilidad del núcleo sino también de la formulación y de la base de datos empleada. En la presente tesis se desarrolla y aplica una nueva metodología basada en técnicas de bootstrapping para caracterizar estadísticamente el factor de rugosidad de diferentes elementos (entre ellos el Cubípodo) sobre diferentes formulaciones de rebase. Se observan diferencias de hasta el 20% entre los factores de rugosidad óptimos y los que se proporcionan en la literatura. La porosidad del manto afecta notablemente al factor de rugosidad pero también a la estabilidad del manto; mayores porosidades proporcionan menor rebase pero también menor estabilidad hidráulica. Por ello, las porosidades de diseño recomendadas deben emplearse para evitar daños durante la vida útil. Fórmulas con pocas variables de entrada son sencillas de emplear pero absorben a través del factor de rugosidad toda la información que no se incluye explícitamente en las variables de entrada. En cambio, la red neuronal de CLASH evita en gran medida estos inconvenientes y al mismo tiempo proporciona excelentes para estimar el rebase sobre diques en talud convencionales. En la presente tesis se ha desarrollado una fórmula explícita que permite emular el comportamiento de la red neuronal de CLASH. La nueva fórmula posee 16 parámetros, seis variables de entrada (Rc/Hm0, ¿0,-1, Rc/h, Gc/Hm0, Ac/Rc y una variable para representar a la berma de pie basada en Rc/h) y dos factores de reducción (¿f y ¿ß). La nueva fórmula se construye en base a simulaciones controladas empleando la red neuronal de CLASH y proporciona el menor error en la predicción de rebase sobre diques en talud de entre los estimadores estudiados. Una de las maneras más efectivas de disminuir el rebase sobre diques en talud es incrementar la cota de coronación mediante un espaldón de hormigón. Estas estructuras sufren el impacto del oleaje y deben ser diseñadas para resistirlo. En la presente tesis se han empleado ensayos de laboratorio de cubos y Cubípodos para desarrollar una nueva fórmula que permita calcular las fuerzas horizontales y verticales del oleaje sobre el espaldón. Las nuevas fórmulas incluyen la influencia de cuatro variables adimensionales (¿f Ru0.1%/Rc, (Rc-Ac)/Ch, ¿(L_m/G_c ) y Fc/Ch) y de la geometría del espaldón. Incluyen la influencia del tipo de elemento mediante el factor de rugosidad al igual que las fórmulas de rebase. Las fuerzas verticales disminuyen significativamente con el aumento de la cota de cimentación. Las nuevas fórmulas proporcionan el menor error de predicción sobre los registros de laboratorio analizados.[CA] La influència del tipus d'element del mantell principal en l'ultrapassament dics en talús és caracteritza habitualment mitjançant el factor de rugositat (¿f). En canvi, en la literatura existeixen diferents valors del factor de rugositat per al mateix tipus d'element. Així doncs, el factor de rugositat no depèn només del tipus d'element, nombre de capes i permeabilitat del nucli però també de la formulació i de la base de dades utilitzada. En la present tesi es desenvolupa i aplica una nova metodologia basada en tècniques de bootstrapping per a caracteritzar estadísticament el factor de rugositat de diferent elements (entre ells el Cubípode) utilitzant diferents formulacions d'ultrapassament. S'observen diferències fins al 20% entre els factors de rugositat òptims i els que apareixen en la literatura. La porositat del mantell afecta notablement el factor de rugositat però també a l'estabilitat del mantell; majors porositats proporcionen menor ultrapassament però també menor estabilitat hidràulica. Per això, les porositats de disseny recomanades deuen emprar-se per a evitar danys durant la vida útil. Formules amb poques variables d'entrada són senzilles d'utilitzar però absorbeixen mitjançant el factor del factor de rugositat tota la informació que no s'inclou de manera explícita en les variables d'entrada. D'altra banda, la xarxa neuronal de CLASH evita en gran mesura aquests inconvenients i al mateix temps proporciona excel·lents resultats per a estimar l'ultrapassament sobre els dics en talús convencionals. En la present tesi s'ha desenvolupat una formulació explícita que permet emular el comportament de la xarxa neuronal de CLASH. La nova formulació té 16 paràmetres, sis variables d'entrada (Rc/Hm0, Ir, Rc/h, Gc/Hm0, Ac/Rc i una variable per a representar la berma de peu basada en Rc/h) i dos factors de reducció (¿f y ¿ß). La nova fórmula es construeix mitjançant simulacions controlades amb la xarxa neuronal de CLASH i proporciona el menor error en la predicció de l'ultrapassament sobre dics en talús de entre els estimadors analitzats. Una de les maneres més efectives de disminuir l'ultrapassament sobre dics en talús és incrementar la cota de coronació mitjançant un espatller de formigó. Aquestes estructures sofreixen l'impacte de les ones i deuen ser dissenyades per a resistir. En la present tesi, s'utilitzen assajos de laboratori de cubs i Cubípodes per a desenvolupar una nova formulació per a calcular les forces horitzontals i verticals causades per l'onatge en l'espatller. Les noves fórmules inclouen la influència de quatre variables adimensionals (¿f Ru0.1%/Rc, (Rc-Ac)/Ch, ¿(L_m/G_c ) y Fc/Ch) i de la geometria de l'espatller. Inclouen la influència del tipus d'element mitjançant el factor de rugositat al igual que les fórmules d'ultrapassament. Les forces verticals disminueixen significativament amb l'augment de la cota de cimentació. Les noves fórmules proporcionen el menor error en la predicció sobre els registres de laboratori analitzats.Molines Llodra, J. (2016). Wave overtopping and crown wall stability of cube and Cubipod-armored mound breakwaters [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/62178TESI

Application of SWASH to determine overtopping during storm events in the port of Ericeira and its introduction into HIDRALERTA system

The assessment of wave-induced overtopping in coastal areas is fundamental for the implementation of local safety measures, such as coastal defence structures. Coastal flooding forecast systems have gained importance in coastal areas in recent years to ensure the safety of people and goods and to reduce damage caused by wave overtopping during storm events. In Portugal, no fully operational early warning system exists. This dissertation is a contribution to the project To-SEAlert, which aims at increasing the efficiency and reliability of the wave overtopping and flooding forecast system HIDRALERTA, developed by the Portuguese National Laboratory of Civil Engineering. The main goal of this study was to test the capability of the numerical model SWASH to be implemented in the HIDRALERTA system for the Ericeira harbour prototype. In order to achieve this goal, the model was first applied to simulate a testcase and to conduct a sensitivity analysis. For Ericeira harbour, the model was calibrated for storms with different wave conditions and for two breakwater profiles with the approach currently implemented in HIDRALERTA, the neural network NN_OVERTOPPING2. The main work consisted in the calibration of the Manning coefficient for the two breakwater profiles with armour layers of Antifer cubes and Tetrpods. Five expressions for the determination of the Manning coefficient were developed: one equation for the profile with Antifer cubes under normal wave attack and four for the profile of Tetrapods under (i) normal wave attack, (ii) oblique wave attack with incident angles between 15 and 50°, (iii) oblique wave attack with incident angles between 15 and 30° and (iv) oblique wave attack with incident angles between 30 and 50°. The results showed that the SWASH model is sensitive to changes in grid size, the number of simulated waves and in bottom friction. SWASH is capable of matching the discharges estimated by NN_OVERTOPPING2 with a calibrated Manning coefficient. The developed expressions showed small errors between the calculated and calibrated Mannings and revealed that the incident wave angle has an influence on the Manning coefficient and must be included in the simulations. Despite an underestimation of the overtopping discharge in some cases (with respect to the ones estimated by NN_OVERTOPPING2) the SWASH model was found to deliver overall good results when applied with Manning coefficients calculated by the developed expressions and capable of being implemented in HIDRALERTA.A estimativa dos galgamentos e das inundações associadas em zonas costeiras é essencial para a implementação de medidas de segurança a nível local, tais como estruturas de defesa costeira. Os sistemas de previsão de inundações costeiras têm vindo a adquirir maior reconhecimento nos últimos anos como ferramenta de apoio durante eventos de tempestade, tanto para a garantia da segurança de pessoas e bens, como para a redução de danos associados à ocorrência de galgamentos. Presentemente, em Portugal, não existe ainda nenhum sistema de previsão de galgamentos e inundações associadas completamente operacional. O trabalho apresentado nesta dissertação é um contributo para o projeto To-SEAlert, que tem como objetivo a inclusão de um conjunto de ferramentas/metodologias de modo a aumentar a eficiência, a fiabilidade e a robustez do sistema de previsão de galgamentos e inundação HIDRALERTA, desenvolvido pelo Laboratório Nacional de Engenharia Civil. O objetivo principal do presente trabalho foi testar a capacidade do modelo numérico SWASH para ser implementado no sistema HIDRALERTA, no protótipo do porto da Ericeira. De forma a atingir esse objetivo, o modelo numérico SWASH foi primeiramente aplicado na simulação de um caso de teste da bibliografia, para o qual foi também conduzida uma análise de sensibilidade. Na aplicação ao porto da Ericeira, o modelo foi calibrado para dois perfis do quebra-mar para tempestades com diferentes condições de agitação incidente. Essa calibração foi efetuada comparando os resultados do modelo numérico SWASH com os resultados da ferramenta neuronal NN_OVERTOPPING2 (ferramenta implementada atualmente no sistema HIDRALERTA). O trabalho principal consistiu na calibração do coeficiente de Manning para os dois perfis do quebra-mar, cujos mantos superiores são constituídos por diferentes tipos de blocos de betão: cubos Antifer e Tetrápodes. Foram desenvolvidas cinco expressões para a determinação do coeficiente de Manning: uma expressão para o perfil com cubos Antifer e para ondas com incidência normal à estrutura, e quatro para o perfil com Tetrápodes, sob (i) ondas com incidência perpendicular à estrutura, (ii) ondas incidentes obliquamente à estrutura, com ângulos entre 15 e 50°, (iii) ondas incidentes obliquamente à estrutura, com ângulos entre 15 e 30°, (iv) ondas incidentes obliquamente à estrutura, com ângulos entre 30 e 50°. Os resultados demonstraram que o modelo SWASH é sensível a variações no espaçamento da malha, no número de ondas simuladas e no atrito de fundo. Com a utilização de um coeficiente de Manning calibrado, o modelo SWASH foi capaz de reproduzir os caudais médios de galgamento estimados pela ferramenta NN_OVERTOPPING2. Os coeficientes de Manning calculados através das expressões desenvolvidas deram origem a pequenos erros quando comparados com os coeficientes de Manning calibrados. As expressões desenvolvidas revelaram que o ângulo de incidência da onda tem influência no coeficiente de Manning e deverá ser incluído nas simulações. No geral, o modelo SWASH conduziu a bons resultados com a utilização dos coeficientes de Manning calculados através das expressões desenvolvidas, apesar de subvalorizar o caudal médio de galgamento (face ao estimado pelo NN_OVERTOPPING2) em algumas condições de agitação incidente. Como conclusão, considera-se que o modelo numérico SWASH tem potencialidade para ser implementado no sistema HIDRALERTA.I would also like to acknowledge the To-SEAlert and EW-Coast projects for giving me the opportunity to contribute a small part to their work