Machine learning-based forecasting of potability of drinking water through adaptive boosting model

Dalal, Surjeet; Onyema, Edeh Michael; Romero, Carlos Andrés Tavera; Ndufeiya-Kumasi, Lauritta Chinazaekpere; Maryann, Didiugwu Chizoba; Nnedimkpa, Ajima Judith; Bhatia, Tarandeep Kaur

doi:10.1515/chem-2022-0187

Cited by 14 publications

(5 citation statements)

References 51 publications

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“…In this regard, several recent studies related to the current problem were selected. The study [4] used an ensemble model for water prediction, while the study [5] used an ANN for water prediction. Similarly, models from previous studies regarding water quality prediction were implemented on the current dataset.…”

Section: Resultsmentioning

confidence: 99%

“…Dalal et al [4] was conducted to introduce data-driven artificial intelligence techniques from a large number of water samples to develop a new ensemble model of machine learning algorithms to accurately predict water quality, including the application of AdaBoost and its comparison with existing models. The methods used are logistic regression XGBoost, multilayer perceptron, ensemble model, and chi-square automatic interaction detector (CHAID).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Innovation has come to the rescue with the development of machine learning models designed to accelerate the process of water quality assessments. Notably, research conducted by Dalal et al [4] and research conducted by Rustam et al [5] has provided machine learning models with impressive predictive performance, characterized by high accuracy rates.…”

Section: Introductionmentioning

confidence: 99%

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Forecasting water quality through machine learning and hyperparameter optimization

Elvin,

Wibowo

2024

IJEECS

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Forecasting water quality through machine learning and hyperparameter optimization is a research endeavor aimed at enhancing the water quality prediction process. The primary goal of this study is to employ various machine learning algorithms for water quality prediction and to refine existing models from previous research. The paper encompasses a comprehensive literature review of previous water quality prediction studies and introduces novel theoretical insights. The research employs a classic machine learning problem-solving approach, predominantly utilizing the extreme gradient boost (XGBoost) algorithm. Additionally, it evaluates other machine learning algorithms, including the random forest (RF) classifier, decision tree (DT) classifier, adaptive boosting (AdaBoost) classifier, support vector machine (SVM), Naïve Bayes, and extra tree classifier for comparison. The evaluation process utilizes a classification report, providing insights into the precision, recall, f1-score, and accuracy of each machine learning model. Notably, the XGBoost model exhibits superior performance, achieving an impressive 97.06% accuracy. Precision stands at 94.22%, recall at 81.5%, and F1-score at 87.4%. These results represent a significant advancement over prior water quality prediction models, emphasizing the potential of machine learning and hyperparameter optimization to enhance water quality forecasting in environmental monitoring.

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Section: Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Forecasting water quality through machine learning and hyperparameter optimization

Elvin,

Wibowo

2024

IJEECS

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“…The study presents a machine learning-based model using adaptive boosting technique to categorize and evaluate the quality rate of drinking water [56].…”

Section: Xgboost Tree Ann Ensemble Modelmentioning

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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“…This step is crucial to a machine learning algorithm's ability to make accurate predictions. Therefore, optimizing hyperpa-rameters is the most challenging aspect of constructing ML models [43][44][45][46][47][48][49][50]. Typically, the hyperparameters of these machine learning algorithms are preset [51].…”

Section: Proposed Nature-inspired-based Hyperparameter Optimization I...mentioning

confidence: 99%

Predicting Fraud in Financial Payment Services through Optimized Hyper-Parameter-Tuned XGBoost Model

Dalal

Seth²,

Rădulescu

et al. 2022

Mathematics

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Online transactions, medical services, financial transactions, and banking all have their share of fraudulent activity. The annual revenue generated by fraud exceeds $1 trillion. Even while fraud is dangerous for organizations, it may be uncovered with the help of intelligent solutions such as rules engines and machine learning. In this research, we introduce a unique hybrid technique for identifying financial payment fraud by combining nature-inspired-based Hyperparameter tuning with several supervised classifier models, as implemented in a modified version of the XGBoost Algorithm. At the outset, we split out a sample of the full financial payment dataset to use as a test set. We use 70% of the data for training and 30% for testing. Records that are known to be illegitimate or fraudulent are predicted, while those that raise suspicion are further investigated using a number of machine learning algorithms. The models are trained and validated using the 10-fold cross-validation technique. Several tests using a dataset of actual financial transactions are used to demonstrate the effectiveness of the proposed approach.

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Machine learning-based forecasting of potability of drinking water through adaptive boosting model

Cited by 14 publications

References 51 publications

Forecasting water quality through machine learning and hyperparameter optimization

Forecasting water quality through machine learning and hyperparameter optimization

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

Predicting Fraud in Financial Payment Services through Optimized Hyper-Parameter-Tuned XGBoost Model

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