Water Quality Assessment Klang River Water Treatment Plants

Mohiyaden, Hairun Aishah; Sidek, Lariyah Mohd; Hayder, Gasim; Noh, Md. Nasir Md.

doi:10.14419/ijet.v7i4.35.23075

Cited by 3 publications

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are six typical physicochemical water quality parameters used; BOD, COD, DO, ammoniacal nitrogen (NH 3 -N), SS and pH value [3]. Second process is the calculation of sub-index value for each selected parameter where specific best fitted equations were developed to transform the measured water quality value to a non-dimensional sub-index value [4,33,34] The third process is to identify parameters weighting factor where each parameter is assigned to have different weight value as per expert panel opinions. According to the WQI Eq.…”

Section: Water Quality Indexmentioning

confidence: 99%

Developing an ensembled machine learning model for predicting water quality index in Johor River Basin

Sidek,

Mohiyaden,

Marufuzzaman

et al. 2024

Environ Sci Eur

View full text Add to dashboard Cite

Currently, the Water Quality Index (WQI) model becomes a widely used tool to evaluate surface water quality for agriculture, domestic and industrial. WQI is one of the simplest mathematical tools that can assist water operator in decision making in assessing the quality of water and it is widely used in the last years. The water quality analysis and prediction is conducted for Johor River Basin incorporating the upstream to downstream water quality monitoring station data of the river. In this research, the numerical method is first used to calculate the WQI and identify the classes for validating the prediction results. Then, two ensemble and optimized machine learning models including gradient boosting regression (GB) and random forest regression (RF) are employed to predict the WQI. The study area selected is the Johor River basin located in Johor, Peninsular Malaysia. The initial phase of this study involves analyzing all available data on parameters concerning the river, aiming to gain a comprehensive understanding of the overall water quality within the river basin. Through temporal analysis, it was determined that Mg, E. coli, SS, and DS emerge as critical factors affecting water quality in this river basin. Then, in terms of WQI calculation, feature importance method is used to identify the most important parameters that can be used to predict the WQI. Finally, an ensemble-based machine learning model is designed to predict the WQI using three parameters. Two ensemble ML approaches are chosen to predict the WQI in the study area and achieved a R2 of 0.86 for RF-based regression and 0.85 for GB-based ML technique. Finally, this research proves that using only the biochemical oxygen demand (BOD), the chemical oxygen demand (COD) and percentage of dissolved oxygen (DO%), the WQI can be predicted accurately and almost 96 times out of 100 sample, the water class can be predicted using GB ensembled ML algorithm. Moving forward, stakeholders may opt to integrate this research into their analyses, potentially yielding economic reliability and time savings.

show abstract

Section: Water Quality Indexmentioning

confidence: 99%

Developing an ensembled machine learning model for predicting water quality index in Johor River Basin

Sidek,

Mohiyaden,

Marufuzzaman

et al. 2024

Environ Sci Eur

View full text Add to dashboard Cite

show abstract

“…RWTP practically use the concept of Integrated Fixed Activated Sludge (IFAS) which is commonly applied in the domestic and industrial wastewater system. [31][32][33][34][35][36]. RWTP operationally starts from water intake collection with trash screening and rubber dam.…”

Section: Introductionmentioning

confidence: 99%

Water Quality Index Score in River Water Treatment Plant at Upper Klang and Gombak River Catchment

Mohiyaden,

Sidek,

Hayder

et al. 2019

IJRTE

View full text Add to dashboard Cite

National River Water Plants are located along upper Klang and Gombak river catchment to purify the polluted river using direct contact methods. As the current water quality situation in the study area is poor due to the contribution of anthropogenic activities on the water quality degradation in these urban rivers, the investigation was performed using the Water Quality Index. This paper gives the overall performance of RWTP using Water Quality Index (WQI) calculation methods. The WQI act as the basis of environment assessment towards to river water quality classification under Malaysia National Water Quality Standards. As an overall result, 57 percent from the total effluents achieve target Class II and above and another 43 percent achieve Class III and below regardless of two (2) RWTPs are under target from the average monitoring; RWTP Sg Gisir and RWTP Sg Sentul. However, the result for RWTP Sg Sentul is not yet conclusive since the monitoring duration is less than 2 years. Certainly, RWTP Sg Gisir needs to be taken into consideration for more frequent maintenance of the RWTP or upgrading of the RWTP oxidation tank as suggested in several MBBR/IFAS operation. As to improve the RWTP performance to score higher WQI, the introduction of recycling sludge in the biological tank so it will be a shorter reaction time. Additionally, the RWTP owner should implement a frequent maintenance work into RWTP component especially clarifier, sludge collector, biological oxidation tank and rubbish trap collector.

show abstract

Prediction of River Water Treatment Plant Operational Performances using Optimization Approach in Artificial Neural Network Model

Mohiyaden,

Sidek,

Hayder

2024

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

Uncontrolled development at the upstream of the river catchment have led to detrimental effect to the environment, including degradation of river water quality. River Water Treatment Plant (RWTP) technology was introduced to reduce the contamination loading into the river water system, worldwide. The technology offers the best biological treatment process including simplicity and stable removal efficiency. However, the plant performance plan is difficult task to predict, thus might have influence the operational control. Recently, artificial neural network (ANN) models have been widely applied in environmental engineering area due to the ability to skip the complexity process to assume of the unknown variables compare to conventional physical based model. In this study, the results of 3-yrs performance using ANN of RWTP were developed. Feed-forward back-propagation using Levenberg–Marquardt (trainlm) used as for this predictive approach. The ideal configuration involves utilizing the tangent sigmoid transfer function (Tansig) in the hidden layer and a linear transfer function (Purelin) in the output layer, with 25 neurons. This configuration yields an R2 value of 0.963 and the most least mean square error (MSE) of 30.39. From the comparison between two model (bio-kinetic and ANN), performance indicator for ANN model shows the best and the most optimum model. Ultimately, RWTP optimization using black-box model ANN is more reliable and timesaving as compared to conventional bio-kinetic model. The development of the proposed model can be implemented and used for various water quality improvement facilities and predict the effluent target parameter in RWTP with higher degree of accuracy.

show abstract

Water Quality Assessment Klang River Water Treatment Plants

Cited by 3 publications

References 18 publications

Developing an ensembled machine learning model for predicting water quality index in Johor River Basin

Developing an ensembled machine learning model for predicting water quality index in Johor River Basin

Water Quality Index Score in River Water Treatment Plant at Upper Klang and Gombak River Catchment

Prediction of River Water Treatment Plant Operational Performances using Optimization Approach in Artificial Neural Network Model

Contact Info

Product

Resources

About