Forecasting PM10 levels in Sri Lanka: A comparative analysis of machine learning models PM10

Mampitiya, Lakindu; Rathnayake, Namal; Hoshino, Yukinobu; Rathnayake, Upaka

doi:10.1016/j.hazadv.2023.100395

Cited by 9 publications

(2 citation statements)

References 28 publications

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“…The literature on machine learning approaches showcases the promising findings on environmental engineering applications 38 , 41 , 44 , 45 . The research works carried out by Mampitiya et al 38 , showcased the applicability of AI in related environmental engineering problems.…”

Section: Study Area and Datasetmentioning

confidence: 99%

Artificial intelligence to predict soil temperatures by development of novel model

Mampitiya,

Rozumbetov,

Rathnayake

et al. 2024

Sci Rep

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Soil temperatures at both surface and various depths are important in changing environments to understand the biological, chemical, and physical properties of soil. This is essential in reaching food sustainability. However, most of the developing regions across the globe face difficulty in establishing solid data measurements and records due to poor instrumentation and many other unavoidable reasons such as natural disasters like droughts, floods, and cyclones. Therefore, an accurate prediction model would fix these difficulties. Uzbekistan is one of the countries that is concerned about climate change due to its arid climate. Therefore, for the first time, this research presents an integrated model to predict soil temperature levels at the surface and 10 cm depth based on climatic factors in Nukus, Uzbekistan. Eight machine learning models were trained in order to understand the best-performing model based on widely used performance indicators. Long Short-Term Memory (LSTM) model performed in accurate predictions of soil temperature levels at 10 cm depth. More importantly, the models developed here can predict temperature levels at 10 cm depth with the measured climatic data and predicted surface soil temperature levels. The model can predict soil temperature at 10 cm depth without any ground soil temperature measurements. The developed model can be effectively used in planning applications in reaching sustainability in food production in arid areas like Nukus, Uzbekistan.

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Section: Study Area and Datasetmentioning

confidence: 99%

Artificial intelligence to predict soil temperatures by development of novel model

Mampitiya,

Rozumbetov,

Rathnayake

et al. 2024

Sci Rep

Self Cite

View full text Add to dashboard Cite

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“…However, CGBR is based on gradient boosting, which can reduce the bias error of the model and thus quickly avoid model overfitting. Recently, machine learning methods using gradient boosting, named LightGBM, XGBoost, and CGBR, have been applied to solve several time series problems: particulate matter estimation (Mampitiya et al 2024), oil formation volume forecasting (Kharazi Esfahani et al 2023), wind power prediction (Ponkumar et al 2023), and stock price prediction (Hartanto et al 2023). The main difference among these three models is the use of the tree growth technique.…”

Section: Proposed New Ensemble Modelmentioning

confidence: 99%

Short-duration prediction of urban storm-water levels using the residual-error ensemble correction technique

Guo,

Chen

2024

Journal of Hydroinformatics

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Predicting water levels in urban storm-water sewer systems is a critical study that could provide vital information to help reduce the risk of flooding. This study proposed a new ensemble model based on the integration of a meta-learner model, residual-error corrections, and a multiple-output framework. To achieve the meta-learner model, three multiple-output data-driven-based (MOD) sewer flooding models employing support vector regression (SVR), k-nearest neighbor regression (KNR), and categorical gradient boosting regression (CGBR) techniques were constructed and applied to predict the short-duration evolution of water levels at seven storm-water gauging sites in Taipei city, Taiwan, considering 10-min datasets spanning nearly 6 years (2016–2021). The Bayesian optimization algorithm was utilized in the training phases for all the models to avoid overfitting or underfitting. Enhancing the analysis of feature importance was also conducted to explore model interpretability based on the SHapley Additive exPlanation (SHAP) algorithm. The outputs of storm-water management model (SWMM) were used as benchmark solutions. For the model validation phase, the proposed integrated model improved the lead-time-averaged Nash–Sutcliffe efficiency of single KNR, SVR, and CGBR models by 174.5, 42.4, and 69.4%, respectively, showing that the proposed accurate model could be useful for urban flood warning systems.

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A new optimized hybrid approach combining machine learning with WRF-CHIMERE model for PM10 concentration prediction

Chelhaoui,

El Ass,

Lachatre

et al. 2024

Model. Earth Syst. Environ.

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Forecasting PM10 levels in Sri Lanka: A comparative analysis of machine learning models PM10

Cited by 9 publications

References 28 publications

Artificial intelligence to predict soil temperatures by development of novel model

Artificial intelligence to predict soil temperatures by development of novel model

Short-duration prediction of urban storm-water levels using the residual-error ensemble correction technique

A new optimized hybrid approach combining machine learning with WRF-CHIMERE model for PM10 concentration prediction

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