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.

Training an imbalanced dataset can cause classifiers to overfit the majority class and increase the possibility of information loss for the minority class. Moreover, accuracy may not give a clear picture of the classifier’s performance. This paper utilized decision tree (DT), support vector machine (SVM), artificial neural networks (ANN), K-nearest neighbors (KNN) and Naïve Bayes (NB) besides ensemble models like random forest (RF) and gradient boosting (GB), which use bagging and boosting methods, three sampling approaches and seven performance metrics to investigate the effect of class imbalance on water quality data. Based on the results, the best model was gradient boosting without resampling for almost all metrics except balanced accuracy, sensitivity and area under the curve (AUC), followed by random forest model without resampling in term of specificity, precision and AUC. However, in term of balanced accuracy and sensitivity, the highest performance was achieved by random forest with a random under-sampling dataset. Focusing on each performance metric separately, the results showed that for specificity and precision, it is better not to preprocess all the ensemble classifiers. Nevertheless, the results for balanced accuracy and sensitivity showed improvement for both ensemble classifiers when using all the resampled dataset.

According to the World Health Organization (WHO), approximately 2 billion people worldwide use drinking water sources that are contaminated with faeces. This is a serious issue since contaminated water may lead to certain waterborne diseases such as cholera, hepatitis A, dysentery, jaundice, and typhoid fever. Therefore, many researchers around the world are interested in studying the water quality. One of the most commonly used approaches is by using machine learning. Machine learning approach has grabbed the interest of many researchers since the last several years due to its power to compute complicated mathematical computations on big data analysis. Therefore, this study explored the correlation between different water quality parameters and Water Quality Index (WQI) in water quality studies that used machine learning by using a meta-analysis approach. This study used estimated variance, heterogeneity index, Chi-squared heterogeneity test and the random effects model. Based on the selected articles, pH, dissolved oxygen (DO) and biochemical oxygen demand (BOD) are the parameters commonly used in water quality studies which use a machine learning approach. This study found that pH is the best chemical factor which greatly affects the Water Quality Index since it has the highest mean correlation and lowest estimated variance due to sampling error. The result showed that the correlation between pH and WQI are heterogeneous across studies based on the Chi-squared of heterogeneity, Q and heterogeneity index, I² value. The 95% confidence interval of effect summary supports the findings that the correlation of pH is different among the studies. This study also found that there is no evidence of publication bias using Egger and Begg’s test. Therefore, in order to ensure good water quality supply, the local authorities and government agencies should give more attention to this parameter since pH of water plays an important role in determining the water quality status.

Kota Bharu city in Kelantan, Malaysia was reported with the highest cases of coronavirus disease 2019 (COVID-19) among other districts. Kota Bharu is the capital city of Kelantan, which acts as the administrative, commercial, and financial areas. A large population pool may become a potential carrier for disease transmission to become an epidemic. However, the impact of population density on the COVID-19 outbreak in Malaysia is still unknown and undiscovered. Therefore, this study investigates the impact of population density on COVID-19 as a potential virus transmission carrier using linear regression models. The chances of formulating new strategies for combating COVID-19 are higher when the driver of transmission potential is identified. This study shows that the highest value of infected area density is in Kota Bharu (0.76), while the infected risk area was highest in Jeli (0.33). This study found that there is a strong relationship between COVID-19 infection cases in Kelantan and population density (R² which is 0.845). Therefore, high population density was identified as a potential driver of transmission of COVID-19 outbreak. Understanding the potential drivers of the disease in a local setting is very important for better preparation and management. The outcome of the study can aid in the development of a new analytical model for strategic planning of Zero COVID-19 for securing the public health and wellness, both social and economic, by researchers, scientists, planners, resource managers, and decision-makers.

To date, much of the information on the clouded leopard study were more focus on Sunda clouded leopard. With the advance of technology, the use of camera trap had enhanced the opportunity for all researchers to study more deeply on species of interest in more intensely larger scale and cost benefit. This paper presents the density estimate of clouded leopard in Gunung Basor-Stong Utara Forest Complex (GB-SUFC). A total of 19 individuals of clouded leopard (14 males, 5 unknown) had been identified. Based on the spatial explicit capture-recapture model (SECR). The estimate density of the clouded leopard in GB-SUFC was (D^±SE) 1.15±0.35 adult clouded leopard/100km². Results from this study will serve as an important baseline on the population density of clouded leopard in GB-SUFC and provides more information on clouded leopard population and ecology especially in Peninsular Malaysia and could also potentially guide and assist the authorities in formulating crucial conservation plans for this threatened species.