Ordered Logit regression model for predicting magnitude of flood along Foma River Kwara Nigeria

Flood is a natural disaster that has become a major concern to the Nigerian government. Despite the numerous hazards caused by the flood, little attention has been directed towards evaluating the flood hazards through the river condition and vulnerability components along the river areas. Hence, this study examines the river condition and vulnerability components to determine the cross-sectional variables in predicting the magnitude of flood along Foma River areas. Data extracted from Geographic Information System (GIS) and site observations were used in generating the cross-sectional variables along the river areas. From the dataset, eight crosssectional variables were obtained including 530 structures of Foma River. The Ordered Logit Regression (OLR) Models were built to predict the magnitude of flood. The model was evaluated using average values of accuracy, precision, recall, and F1-score which were derived from the 10-fold cross validation procedure. The F1-score was able to harmonize and reduce the errors in regulating the imbalanced class distributions. It was also revealed that river watersheds, structure vulnerable status, vulnerable structures along the river, locations of bridges and culverts, sizes occupied by bridges and culverts, and river pollution are significantly contributing to the magnitude of the flood along the Foma River. This study produced a complementary approach to flood prediction along the Foma River, as well as provided the Nigerian government and practitioners a new source of information in addressing problems related to river flooding in Nigeria

Machine Learning Based Data Driven Modelling of Time Series of Power Plant Data

Accurate modeling and simulation of data collected from a power plant system are important factors in the strategic planning and maintenance of the unit. Several non-linearities and multivariable couplings are associated with real-world plants. Therefore, it becomes almost impossible to model the system using conventional mathematical equations. Statistical models such as ARIMA, ARMA are potential solutions but their linear nature cannot very well t a system with non-linear, multivariate time series data. Recently, deep learning methods such as Arti cial Neural Networks (ANNs) have been extensively applied for time series forecasting. ANNs in contrast to stochastic models such as ARIMA can uncover the non-linearities present underneath the data. In this thesis, we analyze the real-time temperature data obtained from a nuclear power plant, and discover the patterns and characteristics of the sensory data. Principal Component Analysis (PCA) followed by Linear Discriminant Analysis (LDA) is used to extract features from the time series data; k-means clustering is applied to label the data instances. Finite state machine representation formulated from the clustered data is then used to model the behaviour of nuclear power plants using system states and state transitions. Dependent and independent parameters of the system are de ned based on co-relation among themselves. Various forecasting models are then applied over multivariate time-stamped data. We discuss thoroughly the implementation of a key architecture of neural networks, Long Short-Term Neural Networks (LSTMs). LSTM can capture nonlinear relationships in a dynamic system using its memory connections. This further aids them to counter the problem of back-propagated error decay through memory blocks. Poly-regression is applied to represent the working of the plant by de ning an association between independent and dependent parameters. This representation is then used to forecast dependent variates based on the observed values of independent variates. Principle of sensitivity analysis is used for optimisation of number of parameters used for predicting. It helps in making a compromise between number of parameters used and level of accuracy achieved in forecasting. The objective of this thesis is to examine the feasibility of the above-mentioned forecasting techniques in the modeling of a complex time series of data, and predicting system parameters such as Reactor Temperature and Linear Power based on past information. It also carries out a comparative analysis of forecasts obtained in each approach

Improved Self-Adaptive Control Parameters in Differential Evolution Algorithm for Complex Numerical Optimization

芝浦工業大学201

A SA-ANN-Based Modeling Method for Human Cognition Mechanism and the PSACO Cognition Algorithm

Artificial neural networks (ANNs) are the important approaches for researching human cognition process. However, current ANNs-based cognition methods cannot address the problems of complex information understanding and fault-tolerant learning. Here we present a modeling method for cognition mechanism based on a simulated annealing–artificial neural network (SA-ANN). Firstly, the relationship between SA processing procedure and cognition knowledge evolution is analyzed, and a SA-ANN-based inference model is set up. Then, based on the inference model, a Powell SA with combinatorial optimization (PSACO) algorithm is proposed to improve the clustering efficiency and recognition accuracy for the cognition process. Finally, three groups of numerical instances for knowledge clustering are provided, and three comparative experiments are performed by self-developed cognition software. The simulated results show that the proposed method can increase the convergence rate by more than 20%, compared with the back-propagation (BP), SA, and restricted Boltzmann machines based extreme learning machine (RBM-ELM) algorithms. The comparative cognition experiments prove that the method can obtain better performances of information understanding and fault-tolerant learning, and the cognition accuracies for original sample, damaged sample, and transformed sample can reach 99.6%, 99.2%, and 97.1%, respectively