Machine Learning (ML) has been used for a long time and has gained wide attention over the last several years. It can handle a large amount of data and allow non-linear structures by using complex mathematical computations. However, traditional ML models do suffer some problems, such as high bias and overfitting. Therefore, this has resulted in the advancement and improvement of ML techniques, such as the bagging and boosting approach, to address these problems. This study explores a series of ML models to predict the water quality classification (WQC) in the Kelantan River using data from 2005 to 2020. The proposed methodology employed 13 physical and chemical parameters of water quality and 7 ML models that are Decision Tree, Artificial Neural Networks, K-Nearest Neighbors, Naïve Bayes, Support Vector Machine, Random Forest and Gradient Boosting. Based on the analysis, the ensemble model of Gradient Boosting with a learning rate of 0.1 exhibited the best prediction performance compared to the other algorithms. It had the highest accuracy (94.90%), sensitivity (80.00%) and f-measure (86.49%), with the lowest classification error. Total Suspended Solid (TSS) was the most significant variable for the Gradient Boosting (GB) model to predict WQC, followed by Ammoniacal Nitrogen (NH3N), Biochemical Oxygen Demand (BOD) and Chemical Oxygen Demand (COD). Based on the accurate water quality prediction, the results could help to improve the National Environmental Policy regarding water resources by continuously improving water quality.
Road accident has become a serious problem of concern due to the increasing trend of the occurrences in line with the increase in the number of registered vehicles in Malaysia year by year. Causes of road accidents may come from various factors. Information on the causes is important to increase knowledge in assisting various responsible bodies with the theory and framework for establishing appropriate regulation, policy as well as for intervention planning and purposes in controlling and managing road accidents problem. Thus, this study aims to produce a road accidents profile of Shah Alam based on secondary data from MIROS and PDRM using descriptive and inferential data analytics. The application of data visualization methodology and techniques have shown that among all states in Malaysia, Selangor having the highest frequency of road accidents. For the cases of Shah Alam, there exist a temporal time scale pattern of road accidents occurrence. More number of accidence happened during the day compared to night. The most critical time the accidence took place is during busy hours; between 8.00 to 10.00 am in the morning, during evening hours between 4.00 to 6.00 pm and night hours after 6.00 pm until 8.00 pm. The accidental death cases were found mostly come from motorbike with (90-250cc), followed by car and lorry and were found mostly occurred at straight-ahead road as well as municipal road. The most type of collision is straying, digressing, or skidding followed by back collision and side collision. The severity of accidents impact is found associated with drivers demographic factors in which accidental death cases involving male is higher than female with age group between 21 to 25 years old.
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