ArticleInternational journal of environmental research and public health2021
Handling Complex Missing Data Using Random Forest Approach for an Air Quality Monitoring Dataset: A Case Study of Kuwait Environmental Data (2012 to 2018).
Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Unravelling the spine-gut-pain connection: chronic musculoskeletal pain and digestive dysfunction in a large-scale population-based cohort of 110,685 individuals.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- Evaluating the relationship between heat waves and urban heat islands in tropical cities: a case study of Kuala Lumpur and George Town.Scientific reports · 2026Article
- Integrating Artificial Intelligence in Environmental Monitoring: A Paradigm Shift in Data-Driven Sustainability.EcoHealth · 2026Review
- Bioinspired Deep Neural Networks for Predicting Income-Reporting Discontinuities in the Chilean Student Loan Program.Biomimetics (Basel, Switzerland) · 2026Article
- Random survival forests for the analysis of recurrent events for right-censored data, with or without a terminal event.BMC medical research methodology · 2025Article
- Nationwide Machine Learning-Ensemble PMEnvironment & health (Washington, D.C.) · 2025Article
- Selective mortality during famine and plague events in medieval London.Scientific reports · 2025Article
- Using Machine Learning to Predict Treatment Outcome in a Concatenated Dataset of Youth Anxiety Treatments.Child psychiatry and human development · 2025Article
- Unravelling the association of glycosylated haemoglobin A1c, blood pressure, and LDL-cholesterol (ABC) with all-cause mortality in Type 2 diabetes patients: insights from a middle-income country.Journal of diabetes and metabolic disorders · 2025Article
- Unsupervised Clustering of Patients Undergoing Thoracoscopic Ablation Identifies Relevant Phenotypes for Advanced Atrial Fibrillation.Diagnostics (Basel, Switzerland) · 2025Article
- Predictive modeling of climate change impacts using Artificial Intelligence: a review for equitable governance and sustainable outcome.Environmental science and pollution research international · 2025Review
- DynamicSeq2SeqXGB for PM2.5 imputation in extremely sparse environmental monitoring networks.PloS one · 2025Article
- Spatio-temporal characterization of PM10 concentration across Abu Dhabi Emirate (UAE).Heliyon · 2024Article
- Optimizing cardiovascular disease mortality prediction: a super learner approach in the tehran lipid and glucose study.BMC medical informatics and decision making · 2024Article
- Targeted therapy using polymyxin B hemadsorption in patients with sepsis: a post-hoc analysis of the JSEPTIC-DIC study and the EUPHRATES trial.Critical care (London, England) · 2023Article
- Evaluation of roadside air quality using deep learning models after the application of the diesel vehicle policy (Euro 6).Scientific reports · 2022Article
- Discrete Missing Data Imputation Using Multilayer Perceptron and Momentum Gradient Descent.Sensors (Basel, Switzerland) · 2022Article
- Ambient Air Pollution and Kawasaki Disease in Korean Children: A Study of the National Health Insurance Claim Data.Journal of the American Heart Association · 2022Article
- An interpretable machine learning model based on a quick pre-screening system enables accurate deterioration risk prediction for COVID-19.Scientific reports · 2021Article
- A survey on missing data in machine learning.Journal of big data · 2021Article
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3 authors.
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Abstract
In environmental research, missing data are often a challenge for statistical modeling. This paper addressed some advanced techniques to deal with missing values in a data set measuring air quality using a multiple imputation (MI) approach. MCAR, MAR, and NMAR missing data techniques are applied to the data set. Five missing data levels are considered: 5%, 10%, 20%, 30%, and 40%. The imputation method used in this paper is an iterative imputation method, missForest, which is related to the random forest approach. Air quality data sets were gathered from five monitoring stations in Kuwait, aggregated to a daily basis. Logarithm transformation was carried out for all pollutant data, in order to normalize their distributions and to minimize skewness. We found high levels of missing values for NO2 (18.4%), CO (18.5%), PM10 (57.4%), SO2 (19.0%), and O3 (18.2%) data. Climatological data (i.e., air temperature, relative humidity, wind direction, and wind speed) were used as control variables for better estimation. The results show that the MAR technique had the lowest RMSE and MAE. We conclude that MI using the missForest approach has a high level of accuracy in estimating missing values. MissForest had the lowest imputation error (RMSE and MAE) among the other imputation methods and, thus, can be considered to be appropriate for analyzing air quality data.
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