Machine Learning Techniques to Predict the Air Quality Using Meteorological Data in Two Urban Areas in Sri Lanka

Mampitiya, Lakindu Induwara; Rathnayake, Namal; Leon, Lee P.; Mandala, Vishwanadham; Azamathulla, Hazi Mohammad; Shelton, Sherly; Hoshino, Yukinobu; Rathnayake, Upaka

doi:10.3390/environments10080141

Cited by 30 publications

(5 citation statements)

References 32 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%

“…Moreover, due to the higher controllability of the models with the hyperparameters, the ANN models can be adapted to specific scenarios. Fine-tuning of the models leads to higher functionality of the model 41 . Ridge Regression—It is a state-of-the-art model that is developed to function over a case of overfitting and multicollinearity of the scenario.…”

Section: Study Area and Datasetmentioning

confidence: 99%

Section: Study Area and Datasetmentioning

confidence: 99%

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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%

Section: Study Area and Datasetmentioning

confidence: 99%

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Artificial intelligence to predict soil temperatures by development of novel model

Mampitiya,

Rozumbetov,

Rathnayake

et al. 2024

Sci Rep

Self Cite

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“…Nuclear reactors employ aerogels (1), diaphanous permeable solids made by using Cherenkov monitoring equipment to dry gels or solvents above their critical point. As insulating glazing materials, aerogels are also about to enter the market [7]. Ceramics are insulators; however, metal and semiconductor ceramics are more prevalent.…”

Section: Introductionmentioning

confidence: 99%

Ceramic Materials in dentistry using the SPSS Method

2024

JEMM

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Dental ceramics can be used in a variety of root canal therapy, such as inlays, bone grafting, crowns, and bridges, to replace earthenware (PFM) systems with everything systems. Dental crowns, dental composite components, and dentures are all made of porcelain and glass-ceramic materials, which are together referred to as dental ceramics. Conventional dental porcelain is feldspar-based and contain sizable amounts of kaolin (Al2O32SiO22H2O), quartz, and feldspar (KAlSi3O8). Rocks containing mica and iron are known to contain feldspar, a grey crystalline mineral. Ceramics are any of the countless hard, brittle, heat, corrosion-resistant substances produced by molding and thereafter scorching an inorganic, non-metallic substance like clay to high temperatures. Common examples include earthenware, pottery, and brick. Dental ceramics that are made of Lucite feldspathic material are very attractive and frequently utilized. Inlays, on lays, partial crowns, crowns, and veneers for metals and ceramics are among their clinical indications. Ceramics are resistant to high temperatures, effective thermal insulators, and have minimal thermal expansion. For uses like as lining industrial furnaces and sealing space shuttles, it makes really good thermal barriers. Dental ceramics are explained within a framework that makes it simple to comprehend how they developed. symptoms and composition. Engineering assessments of efficacy of treatment are discussed, and research is done. Behavior of all earthenware systems clinically. The usage of dental ceramics is discussed from a practical standpoint. Emphasizing what they know but also how we know it while maximizing beauty and endurance. Reviewing the history of ceramics' use in dentistry is helpful. This account has three purposes: (1) to warn professionals Pottery and improved ceramics were created to remedy the issue; (2) to increase actual issues or restoration diversity; and (3) give a soft backdrop in nature and ceramic science. The use of ceramics has always represented the adoption of "high technology" and "Craftsmanship". Ratio studies are statistical analyses of data from appraisals and property valuations. Nearly all states utilize them to produce quantitative measure of the proportion of current market price about which individually estimated taxable property is appraised as well as to offer assessment performance indicators. Evaluation parameters: Ceramic materials in dentistry, Glass-ceramics, predominantly glassy ceramics, Substructure ceramics, Particle-filled glasses and Polycrystalline ceramics. Result: The Cronbach's Alpha Reliability result. The overall Cronbach's Alpha value for the model is .658 which indicates 66% reliability. From the literature review, the above 50% Cronbach's Alpha value model can be considered for analysis. Characteristics of sisal fiber the Cronbach's Alpha Reliability result. The overall Cronbach's Alpha value for the model is .658 which indicates 66% reliability. From the literature review, the above 50% Cronbach's Alpha value model can be considered for analysis.

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“…In recent years, machine-learning techniques have been applied to predict fine particulate matter [13][14][15][16]. Corani [17] used feed-forward neural network models, pruned neural network models, and lazy learning to predict the major air pollutants, O 3 and PM 10 concentrations, in Milan, Italy.…”

Section: Introductionmentioning

confidence: 99%

Establishing a Real-Time Prediction System for Fine Particulate Matter Concentration Using Machine-Learning Models

Wei,

Kao

2023

Atmosphere

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With the rapid urbanization and industrialization in Taiwan, pollutants generated from industrial processes, coal combustion, and vehicle emissions have led to severe air pollution issues. This study focuses on predicting the fine particulate matter (PM2.5) concentration. This enables individuals to be aware of their immediate surroundings in advance, reducing their exposure to high concentrations of fine particulate matter. The research area includes Keelung City and Xizhi District in New Taipei City, located in northern Taiwan. This study establishes five fine prediction models based on machine-learning algorithms, namely, the deep neural network (DNN), M5’ decision tree algorithm (M5P), M5’ rules decision tree algorithm (M5Rules), alternating model tree (AMT), and multiple linear regression (MLR). Based on the predictive results from these five models, the study evaluates the optimal model for forecast horizons and proposes a real-time PM2.5 concentration prediction system by integrating various models. The results demonstrate that the prediction errors vary across different models at different forecast horizons, with no single model consistently outperforming the others. Therefore, the establishment of a hybrid prediction system proves to be more accurate in predicting future PM2.5 concentration compared to a single model. To assess the practicality of the system, the study process involved simulating data, with a particular focus on the winter season when high PM2.5 concentrations are prevalent. The predictive system generated excellent results, even though errors increased in long-term predictions. The system can promptly adjust its predictions over time, effectively forecasting the PM2.5 concentration for the next 12 h.

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Machine Learning Techniques to Predict the Air Quality Using Meteorological Data in Two Urban Areas in Sri Lanka

Cited by 30 publications

References 32 publications

Artificial intelligence to predict soil temperatures by development of novel model

Artificial intelligence to predict soil temperatures by development of novel model

Ceramic Materials in dentistry using the SPSS Method

Establishing a Real-Time Prediction System for Fine Particulate Matter Concentration Using Machine-Learning Models

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