Abstract:The Department of Irrigation and Drainage (DID) Malaysia and Meteorological Malaysia Department (MMD) has been measured the flood characteristics benchmark which included water level, area inundation, peak inundation, peak discharge, volume of flow and duration of flooding. In terms of water levels, DID have introduced three categories of critical level stages namely normal, alert and danger levels. One of the rivers detected by DID that had reached danger level is Sungai Dungun located at Dungun district, Terengganu. The aim of this study is to find suitable prediction model of water level with input variables monthly rainfall, rate of evaporation, temperature and relative humidity taken from the same catchment at Dungun River using Neural Networks based Nonlinear Time Series Regression methods which are Backpropagation Neural Network (BPNN) and nonlinear autoregressive models with exogenous inputs (NARX) networks. The variables selection criteria procedures are also developed to select a significant explanatory variable. In addition, the process of pre-processing data such as treatment of missing data has been made on the original data collected by DID and MMD. The methods are compared to obtain the best model for prediction water level in Dungun River. Based on the experiments, the NARX model with five predictor variables is the best model compared to BPNN. In addition, treatment of missing data using mean and OLR approach produced comparable results for this case study.
Duplicate record detection is important for data preprocessing and cleaning. Artificial Bee Colony (ABC) is one of the most recently introduced algorithms based on the intelligent foraging behavior of a honey bee swarm. Our approach to duplicate detection is the use of ABC algorithm for generating the optimal similarity measure to decide whether the data is duplicate or not. In the training phase, ABC algorithm is used to generate the optimal similarity measure. Once the optimal similarity measure obtained, the deduplication of remaining datasets is done with the help of optimal similarity measure generated from the ABC algorithm. We have used Restaurant and Cora datasets to analyze the proposed algorithm and the performance of the proposed algorithm is compared against the genetic programming technique with the help of evaluation metrics.
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