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

A Survey on Particle Swarm Optimization for Association Rule Mining

Association rule mining (ARM) is one of the core techniques of data mining to discover potentially valuable association relationships from mixed datasets. In the current research, various heuristic algorithms have been introduced into ARM to address the high computation time of traditional ARM. Although a more detailed review of the heuristic algorithms based on ARM is available, this paper differs from the existing reviews in that we expected it to provide a more comprehensive and multi-faceted survey of emerging research, which could provide a reference for researchers in the field to help them understand the state-of-the-art PSO-based ARM algorithms. In this paper, we review the existing research results. Heuristic algorithms for ARM were divided into three main groups, including biologically inspired, physically inspired, and other algorithms. Additionally, different types of ARM and their evaluation metrics are described in this paper, and the current status of the improvement in PSO algorithms is discussed in stages, including swarm initialization, algorithm parameter optimization, optimal particle update, and velocity and position updates. Furthermore, we discuss the applications of PSO-based ARM algorithms and propose further research directions by exploring the existing problems.publishedVersio