Effect of dimension reduction on prediction performance of multivariate nonlinear time series

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Long-term prediction of wind speed in La Serena City (Chile) using hybrid neural network-particle swarm algorithm

An artificial neural network was used for forecasting of long-term wind speed data (24 and 48 hours ahead) in La Serena City (Chile). In order to obtain a more effective correlation and prediction, a particle swarm algorithm was implemented to update the weights of the network. 43800 data points of wind speed were used (years 2003- 2007), and the past values of wind speed, relative humidity, and air temperature were used as input parameters, considering that these meteorogical parameters are more readily available around the globe. Several neural network architectures were studied, and the optimum architecture was determined by adding neurons in systematic form and evaluating the root mean square error (RMSE) during the learning process. The results show that the meteorological variables used as input parameters, have influential effects on the good training and predicting capabilities of the chosen network, and that the hybrid neural network can forecast the hourly wind speed with acceptable accuracy, such as: RMSE=0.81 [m·s−1], MSE=0.65 [m·s−1] 2 and R2 =0.97 for 24-hours-ahead wind speed prediction, and RMSE=0.78, MSE=0.634 [m·s−1] 2 and R2 =0.97 for 48-hours-ahead wind speed prediction.Una red neuronal artificial fue utilizada para la predicción de datos de la velocidad de viento a largo plazo (24 y 48 horas en adelanto) en la Ciudad de La Serena (Chile). Para obtener una efectiva correlación y predición, se implementó una optimización de enjambre de particulas para actualizar los pesos de la red. Se emplearon 43800 datos de velocidad de viento (años 2003-2007), y los valores pasados de velocidad del viento, humedad relativa y temperatura del aire fueron utilizados como parámetros de entrada, considerando que estos parámetros meteorológicos se encuentran fácilmente disponibles en todo el mundo. Se estudiaron varias arquitecturas de redes neuronales y la arquitectura optima se determine añadiendo neuronas de forma sistemática y evaluando la raíz del error cuadrático medio (RMSE) durante el proceso de aprendizaje. Los resultados muestran que las variables meteorológicas utilizadas como parámetros de entrada, tienen un efecto positivo sobre el correcto entrenamiento y capacidades predictivas de la red, y que la red neural híbrida puede pronosticar la velocidad del viento horaria con una precisión aceptable, como un RMSE=0.81 [m·s−1], MSE=0.65 [m·s−1] 2 y R2 =0.97 para la predicción de la velocidad del viento de 24 horas en adelanto, y un RMSE=0.78, MSE=0.634 [m·s−1] 2 and R2 =0.97 para la predicción de la velocidad del viento de 48 horas en adelanto

Predicting complex system behavior using hybrid modeling and computational intelligence

“Modeling and prediction of complex systems is a challenging problem due to the sub-system interactions and dependencies. This research examines combining various computational intelligence algorithms and modeling techniques to provide insights into these complex processes and allow for better decision making. This hybrid methodology provided additional capabilities to analyze and predict the overall system behavior where a single model cannot be used to understand the complex problem. The systems analyzed here are flooding events and fetal health care. The impact of floods on road infrastructure is investigated using graph theory, agent-based traffic simulation, and Long Short-Term Memory deep learning to predict water level rise from river gauge height. Combined with existing infrastructure models, these techniques provide a 15-minute interval for making closure decisions rather than the current 6-hour interval. The second system explored is fetal monitoring, which is essential to diagnose severe fetal conditions such as acidosis. Support Vector Machine and Random Forest were compared to identify the best model for classification of fetal state. This model provided a more accurate classification than existing research on the CTG. A deep learning forecasting model was developed to predict the future values for fetal heart rate and uterine contractions. The forecasting and classification algorithms are then integrated to evaluate the future condition of the fetus. The final model can predict the fetal state 4 minutes ahead to help the obstetricians to plan necessary interventions for preventing acidosis and asphyxiation. In both cases, time series predictions using hybrid modeling provided superior results to existing methods to predict complex behaviors”--Abstract, page iv

Hybrid dragonfly algorithm with neighbourhood component analysis and gradient tree boosting for crime rates modelling

In crime studies, crime rates time series prediction helps in strategic crime prevention formulation and decision making. Statistical models are commonly applied in predicting time series crime rates. However, the time series crime rates data are limited and mostly nonlinear. One limitation in the statistical models is that they are mainly linear and are only able to model linear relationships. Thus, this study proposed a time series crime prediction model that can handle nonlinear components as well as limited historical crime rates data. Recently, Artificial Intelligence (AI) models have been favoured as they are able to handle nonlinear and robust to small sample data components in crime rates. Hence, the proposed crime model implemented an artificial intelligence model namely Gradient Tree Boosting (GTB) in modelling the crime rates. The crime rates are modelled using the United States (US) annual crime rates of eight crime types with nine factors that influence the crime rates. Since GTB has no feature selection, this study proposed hybridisation of Neighbourhood Component Analysis (NCA) and GTB (NCA-GTB) in identifying significant factors that influence the crime rates. Also, it was found that both NCA and GTB are sensitive to input parameter. Thus, DA2-NCA-eGTB model was proposed to improve the NCA-GTB model. The DA2-NCA-eGTB model hybridised a metaheuristic optimisation algorithm namely Dragonfly Algorithm (DA) with NCA-GTB model to optimise NCA and GTB parameters. In addition, DA2-NCA-eGTB model also improved the accuracy of the NCA-GTB model by using Least Absolute Deviation (LAD) as the GTB loss function. The experimental result showed that DA2-NCA-eGTB model outperformed existing AI models in all eight modelled crime types. This was proven by the smaller values of Mean Absolute Percentage Error (MAPE), which was between 2.9195 and 18.7471. As a conclusion, the study showed that DA2-NCA-eGTB model is statistically significant in representing all crime types and it is able to handle the nonlinear component in limited crime rate data well

Toxicogenomics : a transcriptomics approach to assess the toxicity of 4-nitrophenol to sachharomyces cerevisiae

PhD ThesisSince the industrial revolution there has been a significant increase in the production, use and release of man-made chemicals (xenobiotics) into the environment. This is cause for concern because the toxicity of some xenobiotics are unknown, consequently there is an increased need for high throughput sensitive assays that can be used to detect and evaluate the toxicity of xenobiotics. The advent of transcriptomics has provided scientists with a sensitive, accurate high throughput method to measure gene expression in response to chemicals (toxicogenomics). The aim of this work was to investigate the effects of the widely distributed xenobiotic and model organic pollutant, 4-nitrophenol on gene expression in the model eukaryote Saccharomyces cerevisiae. This would assess if this chemical had more subtle effects on cells than previous traditional biochemistry studies revealed and to see if certain genes could be used to develop a specific microarray test to detect the presence of 4-nitrophenol in the environment. Traditional growth inhibition tests were used to ascertain the toxicity of 4-nitrophenol to S. cerevisiae. Traditional tests were used to establish EC10 & EC50 concentrations in standard defined media (SDM). Subsequently S. cerevisiae were exposed to 10 & 39 mg/l 4-nitrophenol in SDM and samples taken for expression profiling when conditions were optimal, one, two and three hours after 4-nitrophenol exposure. qRT-PCR was used to validate the gene expression results. Approximately 600 genes were increased in expression and ˜600 genes were decreased in expression at 10 & 39 4-nitrophenol. Genes associated with RNA processing, ribosome formation, mitochondrial biogenesis, and respiratory activity were differentially expressed. Time series analysis showed 4-nitrophenol caused damage to cell walls and membranes as inferred from increased expression of genes for cell wall and membrane synthesis (DCW1, GRE2). This resulted in hypo-osmotic stress (increased expression of SLN1, & AQY2) and decreased expression of genes involved in cell replication (MDY2, PAN3). At 39 mg/l 4-nitrophenol expression of additional drug resistance genes increased after one (PDR3, PDR15, PDR16), two (PDR3, PDR15) and three (PDR5) hour’s exposure. After two hours cells had respiration deficiencies shown by; increased expression of RIM2 a mitochondrial carrier protein, which rescues respiration deficient cells, and decreased production of mitochondrial oxidoreductases. Fourteen iron homeostasis genes were increased in expression and iron requiring cytochromes and oxidoreductases were decreased in expression alongside glucose transporter encoding genes. The results showed respiration was reduced and implicated an increased requirement for iron. Expression of general Environmental Stress Response (ESR) genes initially decreased (one hour of exposure to 39 mg/l 4-nitrophenol). However, three hours after the addition of 4-nitrophenol expression of ESR genes increased. ESR genes are known to be repressed for up to two hours after chemical exposure, and are known to be involved in respiration. The results in this study show reduced respiration is temporary. Increased expression of genes involved in respiration and growth after three hours show that treated cells have adapted to 4-nitrophenol presence. Only two iron homeostasis genes were increased in expression after three hours exposure to 39 mg/l 4-nitrophenol showing iron concentrations inside the cell have stabilised. Exposure to 4-nitrophenol resulted in hypo-osmotic stress, probably caused by membrane damage. This led to decreased intracellular iron concentrations and increased oxidative stress, iron availability directly controls expression of ESR genes and oxidoreductases and may explain the effects seen on mitochondrial respiratory activity and the general stress response observed. The study confirms biochemical results which have shown 4-nitrophenol damages cell membranes and reduces respiration, and implicates iron deficiency in playing a role in this process. It also shows that at sub lethal concentrations cells can adapt their respiration and growth to survive in the presence of 4-nitrophenol.Natural Environment Research Council (NERC) AstraZeneca COGEME (Manchester University