Predicting River Pollution Using Random Forest Decision Tree with GIS Model: A Case Study of MMORS, Philippines

Victoriano, Jayson M.; Lacatan, Luisito Lolong; Vinluan, Albert A.

doi:10.18178/ijesd.2020.11.1.1222

Cited by 11 publications

(5 citation statements)

References 8 publications

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“…The results showed that the RF model outperformed the SMO-SVM model in most cases and had higher prediction accuracy for water quality indicators. Victoriano [24] used dissolved oxygen (DO), pH, biochemical oxygen demand (BOD), total suspended solids (TSS), nitrate, phosphate, and coliform as attribute values to construct a predictive model using the random forest decision tree algorithm. The model was applied to predict the pollution level of rivers in the MMORS River in Bulacan Province, Philippines.…”

Section: Random Forestmentioning

confidence: 99%

Comparative Study of Algorithms Used in Water Pollution Prevention and Control

Wang,

Liu

2023

Frontiers in Artificial Intelligence and Applications

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Water pollution prevention and control is crucial to ensure the safety of water environment and human health, and various types of algorithms play an important role in it. We introduce the history and algorithm overview of various algorithms in water pollution prevention and control, analyze the current research status and recent research results in this field, compare and evaluate the advantages and disadvantages of various algorithms, and focus on the following algorithms: neural network, convolutional neural network, decision tree, random forest, naive Bayes, SVM, K-Means, and AdaBoost. Through the comparative analysis of these algorithms, we hope to provide a more effective method for water pollution prevention and control.

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Section: Random Forestmentioning

confidence: 99%

Comparative Study of Algorithms Used in Water Pollution Prevention and Control

Wang,

Liu

2023

Frontiers in Artificial Intelligence and Applications

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“…Random forest (RF): In 2001, Breiman introduced a random forest technique to categorise machine learning [34]. The random forest approach had a substantial influence on artificial intelligence, computer development and machine learning [35].…”

Section: Ensemble Regressionmentioning

confidence: 99%

Survival Prediction of Glioma Patients from Integrated Radiology and Pathology Images Using Machine Learning Ensemble Regression Methods

Rathore

Khan

Ali³

et al. 2022

Applied Sciences

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Gliomas are tumors of the central nervous system, which usually start within the glial cells of the brain or the spinal cord. These are extremely migratory and diffusive tumors, which quickly expand to the surrounding regions in the brain. There are different grades of gliomas, hinting about their growth patterns and aggressiveness and potential response to the treatment. As part of routine clinical procedure for gliomas, both radiology images (rad), such as multiparametric MR images, and digital pathology images (path) from tissue samples are acquired. Each of these data streams are used separately for prediction of the survival outcome of gliomas, however, these images provide complimentary information, which can be used in an integrated way for better prediction. There is a need to develop an image-based method that can utilise the information extracted from these imaging sequences in a synergistic way to predict patients’ outcome and to potentially assist in building comprehensive and patient-centric treatment plans. The objective of this study is to improve survival prediction outcomes of gliomas by integrating radiology and pathology imaging. Multiparametric magnetic resonance imaging (MRI), rad images, and path images of glioma patients were acquired from The Cancer Imaging Archive. Quantitative imaging features were extracted from tumor regions in rad and path images. The features were given as input to an ensemble regression machine learning pipeline, including support vector regression, AdaBoost, gradient boost, and random forest. The performance of the model was evaluated in several configurations, including leave-one-out, five-fold cross-validation, and split-train-test. Moreover, the quantitative performance evaluations were conducted separately in the complete cohort (n = 171), high-grade gliomas (HGGs), n = 75, and low-grade gliomas (LGGs), n = 96. The combined rad and path features outperformed individual feature types in all the configurations and datasets. In leave-one-out configuration, the model comprising both rad and path features was successfully validated on the complete dataset comprising HGFs and LGGs (R=0.84 p=2.2×10−16). The Kaplan–Meier curves generated on the predictions of the proposed model yielded a hazard ratio of 3.314 [95%CI:1.718−6.394], log−rank(P)=2×10−4 on combined rad and path features. Conclusion: The proposed approach emphasizes radiology experts and pathology experts’ clinical workflows by creating prognosticators upon ‘rad’ radiology images and digital pathology ‘path’ images independently, as well as combining the power of both, also through delivering integrated analysis, that can contribute to a collaborative attempt between different departments for administration of patients with gliomas.

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“…The RF model predicted BOD better than the M5 model tree at Karun River in Ahvaz station, Iran (Golabi et al, 2020). Victoriano et al (2020) developed an RF‐based GIS model to forecast the Marilao‐Meycauayan‐Obando River System (MMORS)'s pollution level from coliform, phosphate, DO, TSS, nitrate, pH, and BOD in the province of Bulacan, Philippines.…”

Section: Introductionmentioning

confidence: 99%

Comparison of the performance of decision tree (DT) algorithms and extreme learning machine (ELM) model in the prediction of water quality of the Upper Green River watershed

Anmala

Venkateswarlu

2021

Water Environment Research

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Stream waters play a crucial role in catering to the world's needs with the required quality of water. Due to the discharges of wastewater from the various point and nonpoint sources, most of the watersheds are contaminated easily. The Upper Green River watershed in Kentucky, USA, is one such watershed that is contaminated over the years due to the runoff from rural areas and agricultural lands and combined sewer overflows (CSOs) from urban areas. Monitoring and characterizing the water quality status of streams in such watersheds has become of great importance, with multivariate statistical techniques such as regression, factor analysis, cluster analysis, and artificial intelligence methods such as artificial neural networks (ANNs). The water quality parameters, namely, fecal coliform (FC), turbidity, pH, and conductivity have been predicted quantitatively using ANNs to understand the water quality status of streams in the Upper Green River watershed elsewhere. In this study, a novel attempt has been made to predict the status of the quality of the Green River water with the predictive capabilities of a few decision tree (DT) algorithms such as classification and regression tree (CART) model, multivariate adaptive regression splines (MARS) model, random forest (RF) model, and extreme learning machine (ELM) model. The RF model's performance is better in predicting FC, turbidity, and pH than CART models in training and testing phases. Relatively, MARS and ELM models did better in testing though the performance is poorer in training. For example, we obtain the RMSE values of 2206, 2532, 1533, and 1969 using RF, CART, MARS, and ELM for FC in testing. A good correlation has been observed between conductivity and temperature, precipitation, and land‐use factors for the MARS model. Overall, DT models are helpful in understanding, interpreting the outcomes, and visualizing the results compared with the other models. Practitioner points The prediction of stream water quality parameters using decision trees is explored. The climate and land use parameters are used as input parameters to the modeling. The DT models of CART, MARS, RF, and ANNs such as ELM are explored to predict stream water quality. The RF model shows stable results compared with CART, MARS, and ELM for the data explored. Apart from the R2 value, RMSE and MAE indicate the effectiveness of DTs in prediction.

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Predicting River Pollution Using Random Forest Decision Tree with GIS Model: A Case Study of MMORS, Philippines

Cited by 11 publications

References 8 publications

Comparative Study of Algorithms Used in Water Pollution Prevention and Control

Comparative Study of Algorithms Used in Water Pollution Prevention and Control

Survival Prediction of Glioma Patients from Integrated Radiology and Pathology Images Using Machine Learning Ensemble Regression Methods

Comparison of the performance of decision tree (DT) algorithms and extreme learning machine (ELM) model in the prediction of water quality of the Upper Green River watershed

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