Prediction of Water Quality Classification of the Kelantan River Basin, Malaysia, Using Machine Learning Techniques

Abstract

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.

Drought Forecasting: A Review and Assessment of the Hybrid Techniques and Data Pre-Processing

“…The natural logarithm was utilised to normalise the data to decrease the impact of multicollinearity between input variables [89]. In addition, Z-score normalisation is a traditional standardisation approach that uses the mean (µ) and standard deviation (σ) to standardise parameters [90]; it is calculated using Equation (1).…”

Environmental Suitability for Sea Ranching Harvest Type of Sandfish Sea Cucumber Holothuria Scabra Jaeger 1833 in Karimunjawa National Park, Indonesia

“…Water environment data before further analysis was performed by standardization. Standardization is a method of simplifying calculations where standardization is a dimensional expression that is converted to a non-dimensional expression and becomes a scale [24]. Conventional standardization methods were used to standardize parameters using standard deviation [25,24].…”

“…Standardization is a method of simplifying calculations where standardization is a dimensional expression that is converted to a non-dimensional expression and becomes a scale [24]. Conventional standardization methods were used to standardize parameters using standard deviation [25,24]. The data normalization technique used a range of 1 to 3 to convert all data from different scales to the standard scale [24].…”

“…Conventional standardization methods were used to standardize parameters using standard deviation [25,24]. The data normalization technique used a range of 1 to 3 to convert all data from different scales to the standard scale [24]. Furthermore, the literature blinded the data normalization results in the suitability matrix form.…”

A Review of Hybrid Soft Computing and Data Pre-Processing Techniques to Forecast Freshwater Quality’s Parameters: Current Trends and Future Directions

“…ML has been applied for a long time and has received considerable attention over the last few years. It can handle a huge volume of data and permit non-linear constructions by utilizing complex mathematical calculations [24]. Additionally, ML are categorised as unsupervised and supervised learning.…”