Water Quality Index Using Modified Random Forest Technique: Assessing Novel Input Features

Wong, Wen Yee; Al-Ani, Ayman; Hasikin‬, Khairunnisa; Khairuddin, Anis Salwa Mohd; Razak, Sarah Abdul; Hizaddin, Hanee F.; Mokhtar, Mohd Istajib; Azizan, Muhammad Mokhzaini

doi:10.32604/cmes.2022.019244

Cited by 5 publications

(8 citation statements)

References 46 publications

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“…R 2 is the squared correlation between the predicted and actual datasets in regression models. The proportion variation of the results inferred by predictor factors is measured ( Wong et al, 2022a ). It determines the strength of the relationship between the model and the dependent variable.…”

Section: Methodsmentioning

confidence: 99%

“…AI is crucial because it is one of the foundational technologies in the age of data and digitalization, especially in IR4.0. AI is frequently employed in the field and research of medical and environmental sustainability research ( Jamaludin et al, 2022 ; Mammoottil et al, 2022 ; Teoh et al, 2022 ; Woan Ching et al, 2022 ; Wong et al, 2022a ; Wong et al, 2022b ; Yeoh et al, 2021 ). As smart cities emerge, this article proposed the viability of AI in providing technological solutions to urban environmental problems, particularly the forecasting and control of urban air quality.…”

Section: Literature Reviewsmentioning

confidence: 99%

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Artificial intelligence-assisted air quality monitoring for smart city management

Neo

Hasikin‬

Lai

et al. 2023

PeerJ Computer Science

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Background The environment has been significantly impacted by rapid urbanization, leading to a need for changes in climate change and pollution indicators. The 4IR offers a potential solution to efficiently manage these impacts. Smart city ecosystems can provide well-designed, sustainable, and safe cities that enable holistic climate change and global warming solutions through various community-centred initiatives. These include smart planning techniques, smart environment monitoring, and smart governance. An air quality intelligence platform, which operates as a complete measurement site for monitoring and governing air quality, has shown promising results in providing actionable insights. This article aims to highlight the potential of machine learning models in predicting air quality, providing data-driven strategic and sustainable solutions for smart cities. Methods This study proposed an end-to-end air quality predictive model for smart city applications, utilizing four machine learning techniques and two deep learning techniques. These include Ada Boost, SVR, RF, KNN, MLP regressor and LSTM. The study was conducted in four different urban cities in Selangor, Malaysia, including Petaling Jaya, Banting, Klang, and Shah Alam. The model considered the air quality data of various pollution markers such as PM2.5, PM10, O3, and CO. Additionally, meteorological data including wind speed and wind direction were also considered, and their interactions with the pollutant markers were quantified. The study aimed to determine the correlation variance of the dependent variable in predicting air pollution and proposed a feature optimization process to reduce dimensionality and remove irrelevant features to enhance the prediction of PM2.5, improving the existing LSTM model. The study estimates the concentration of pollutants in the air based on training and highlights the contribution of feature optimization in air quality predictions through feature dimension reductions. Results In this section, the results of predicting the concentration of pollutants (PM2.5, PM10, O3, and CO) in the air are presented in R2 and RMSE. In predicting the PM10 and PM2.5concentration, LSTM performed the best overall high R2values in the four study areas with the R2 values of 0.998, 0.995, 0.918, and 0.993 in Banting, Petaling, Klang and Shah Alam stations, respectively. The study indicated that among the studied pollution markers, PM2.5,PM10, NO2, wind speed and humidity are the most important elements to monitor. By reducing the number of features used in the model the proposed feature optimization process can make the model more interpretable and provide insights into the most critical factor affecting air quality. Findings from this study can aid policymakers in understanding the underlying causes of air pollution and develop more effective smart strategies for reducing pollution levels.

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Section: Methodsmentioning

confidence: 99%

Section: Literature Reviewsmentioning

confidence: 99%

Artificial intelligence-assisted air quality monitoring for smart city management

Neo

Hasikin‬

Lai

et al. 2023

PeerJ Computer Science

Self Cite

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“…Many experiments have tested the performance of the random forest approach in feature selection. For example, Wong et al [20] establish the energy consumption prediction model using the random forest feature selection algorithm. Compared with the single machine learning algorithm, the prediction performance of the subset selected by random forest features as input has been dramatically improved.…”

Section: Feature Selection Based On Random Forestsmentioning

confidence: 99%

Prediction of the Wastewater’s pH Based on Deep Learning Incorporating Sliding Windows

Xu¹,

Zou²,

Wang³

2023

Computer Systems Science and Engineering

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To protect the environment, the discharged sewage's quality must meet the state's discharge standards. There are many water quality indicators, and the pH (Potential of Hydrogen) value is one of them. The natural water's pH value is 6.0-8.5. The sewage treatment plant uses some data in the sewage treatment process to monitor and predict whether wastewater's pH value will exceed the standard. This paper aims to study the deep learning prediction model of wastewater's pH. Firstly, the research uses the random forest method to select the data features and then, based on the sliding window, convert the data set into a time series which is the input of the deep learning training model. Secondly, by analyzing and comparing relevant references, this paper believes that the CNN (Convolutional Neural Network) model is better at nonlinear data modeling and constructs a CNN model including the convolution and pooling layers. After alternating the combination of the convolutional layer and pooling layer, all features are integrated into a full-connected neural network. Thirdly, the number of input samples of the CNN model directly affects the prediction effect of the model. Therefore, this paper adopts the sliding window method to study the optimal size. Many experimental results show that the optimal prediction model can be obtained when alternating six convolutional layers and three pooling layers. The last full-connection layer contains two layers and 64 neurons per layer. The sliding window size selects as 12. Finally, the research has carried out data prediction based on the optimal CNN deep learning model. The predicted pH of the sewage is between 7.2 and 8.6 in this paper. The result is applied in the monitoring system platform of the "Intelligent operation and maintenance platform of the reclaimed water plant."

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“…Furthermore, Wong et al [6] conducted a comprehensive exploration and analysis of 17 novel input features. Their goal was to formulate an enhanced water quality index (WQI) capable of adapting to the land use activities surrounding the river.…”

Section: Introductionmentioning

confidence: 99%

WaQuPs: A ROS-Integrated Ensemble Learning Model for Precise Water Quality Prediction

Firdiani,

Mandala,

Adiwijaya

et al. 2023

Applied Sciences

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Water presents challenges in swiftly and accurately assessing its quality due to its intricate composition, diverse sources, and the emergence of new pollutants. Current research tends to oversimplify water quality, categorizing it as potable or not, despite its complexity. To address this, we developed a water quality prediction system (WaQuPs), a sophisticated solution tackling the intricacies of water quality assessment. WaQuPs employs advanced machine learning, including an ensemble learning model, categorizing water quality into nuanced levels: potable, lightly polluted, moderately polluted, and heavily polluted. To ensure rapid and precise dissemination of information, WaQuPs integrates an Internet of Things (IoT)-based communication protocol for the efficient delivery of detected water quality results. In its development, we utilized advanced techniques, such as random oversampling (ROS) for dataset balance. We used a correlation coefficient to select relevant features for the ensemble learning algorithm based on the Random Forest algorithm. Further enhancements were made through hyperparameter tuning to improve the prediction accuracy. WaQuPs exhibited impressive metrics, achieving an accuracy of 83%, precision of 82%, recall of 83%, and an F1-score of 82%. Comparative analysis revealed that WaQuPs with the Random Forest model outperformed both the XGBoost and CatBoost models, confirming its superiority in predicting water quality.

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Water Quality Index Using Modified Random Forest Technique: Assessing Novel Input Features

Cited by 5 publications

References 46 publications

Artificial intelligence-assisted air quality monitoring for smart city management

Artificial intelligence-assisted air quality monitoring for smart city management

Prediction of the Wastewater’s pH Based on Deep Learning Incorporating Sliding Windows

WaQuPs: A ROS-Integrated Ensemble Learning Model for Precise Water Quality Prediction

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