Prediction of Quality of Water According to a Random Forest Classifier

Alomani, Shahd Maadi; Alhawiti, Najd Ibrahim; Alhakamy, A’aeshah

doi:10.14569/ijacsa.2022.01306105

Cited by 4 publications

(2 citation statements)

References 27 publications

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“…Developed a Random Forest model using PySpark classification to predict the potability of river water based on ten different features [57]. The dataset for the study was adopted from Kaggle.…”

Section: Rf With Pysparkmentioning

confidence: 99%

Relevance of Machine Learning Techniques in Water Infrastructure Integrity and Quality: A Review Powered by Natural Language Processing

García,

Leiva-Araos,

Diaz-Saavedra

et al. 2023

Applied Sciences

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Water infrastructure integrity, quality, and distribution are fundamental for public health, environmental sustainability, economic development, and climate change resilience. Ensuring the robustness and quality of water infrastructure is pivotal for sectors like agriculture, industry, and energy production. Machine learning (ML) offers potential for bolstering water infrastructure integrity and quality by analyzing extensive data from sensors and other sources, optimizing treatment protocols, minimizing water losses, and improving distribution methods. This study delves into ML applications in water infrastructure integrity and quality by analyzing English-language articles from 2015 onward, compiling a total of 1087 articles. Initially, a natural language processing approach centered on topic modeling was adopted to classify salient topics. From each identified topic, key terms were extracted and utilized in a semi-automatic selection process, pinpointing the most relevant articles for further scrutiny, while unsupervised ML algorithms can assist in extracting themes from the documents, generating meaningful topics often requires intricate hyperparameter adjustments. Leveraging the Bidirectional Encoder Representations from Transformers (BERTopic) enhanced the study’s contextual comprehension in topic modeling. This semi-automatic methodology for bibliographic exploration begins with a broad topic categorization, advancing to an exhaustive analysis of each topic. The insights drawn underscore ML’s instrumental role in enhancing water infrastructure’s integrity and quality, suggesting promising future research directions. Specifically, the study has identified four key areas where ML has been applied to water management: (1) advancements in the detection of water contaminants and soil erosion; (2) forecasting of water levels; (3) advanced techniques for leak detection in water networks; and (4) evaluation of water quality and potability. These findings underscore the transformative impact of ML on water infrastructure and suggest promising paths for continued investigation.

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Section: Rf With Pysparkmentioning

confidence: 99%

Relevance of Machine Learning Techniques in Water Infrastructure Integrity and Quality: A Review Powered by Natural Language Processing

García,

Leiva-Araos,

Diaz-Saavedra

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…The most commonly used method of water quality assessment is laboratory analysis, which is time-consuming and expensive to evaluate water quality through laboratory and statistical analysis. These analyses require collecting samples and transporting them to laboratories for testing, which complicates practical use [5]. In order to find a more convenient way to detect water pollution, the focus of this paper is to investigate the performance of machine learning algorithms in water quality prediction.…”

Section: Introductionmentioning

confidence: 99%