Forecasting of fine particulate matter based on LSTM and optimization algorithm

Journal of Cleaner Production, Год журнала: 2023, Номер 427, С. 139233 - 139233

Опубликована: Окт. 10, 2023

Язык: Английский

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Daily scale air quality index forecasting using bidirectional recurrent neural networks: Case study of Delhi, India

Environmental Pollution, Год журнала: 2024, Номер 351, С. 124040 - 124040

Опубликована: Апрель 27, 2024

Язык: Английский

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Research and application of a novel selective stacking ensemble model based on error compensation and parameter optimization for AQI prediction

Environmental Research, Год журнала: 2024, Номер 247, С. 118176 - 118176

Опубликована: Янв. 11, 2024

Язык: Английский

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Advancing air quality prediction models in urban India: a deep learning approach integrating DCNN and LSTM architectures for AQI time-series classification

Modeling Earth Systems and Environment, Год журнала: 2024, Номер 10(2), С. 2935 - 2955

Опубликована: Фев. 2, 2024

Язык: Английский

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Impacts of climate change on spatial wheat yield and nutritional values using hybrid machine learning

Environmental Research Letters, Год журнала: 2024, Номер 19(10), С. 104049 - 104049

Опубликована: Авг. 30, 2024

Abstract Wheat’s nutritional value is critical for human nutrition and food security. However, more attention needed, particularly regarding the content concentration of iron (Fe) zinc (Zn), especially in context climate change (CC) impacts. To address this, various controlled field experiments were conducted, involving cultivation three wheat cultivars over growing seasons at multiple locations with different soil conditions under varying Fe Zn treatments. The yield attributes, including values such as nitrogen (N), Zn, from these integrated national statistics other to train test machine learning (ML) algorithms. Automated ML leveraging a large number models, outperformed traditional enabling training testing numerous achieving robust predictions grain (GY) ( R 2 > 0.78), N 0.75), 0.71) through stacked ensemble all models. model predicted GY, N, Fe, spatial explicit mid-century (2020–2050) using Global Circulation Models (GCMs): GFDL-ESM4, HadGEM3-GC31-MM, MRI-ESM2-0 two shared socioeconomic pathways (SSPs) specifically SSP2-45 SSP5-85, downscaled NEX-GDDP-CMIP6. Averaged across GCMs SSPs, CC projected increase by 4.5%, protein 0.8% high variability. it expected decrease 5.5%, 4.5% relative historical period (1980–2010). Positive impacts on encountered negative concentrations, further exacerbating challenges related security nutrition.

Язык: Английский

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From Local Issues to Global Impacts: Evidence of Air Pollution for Romania and Turkey

Sensors, Год журнала: 2024, Номер 24(4), С. 1320 - 1320

Опубликована: Фев. 18, 2024

Air pollution significantly threatens human health and natural ecosystems requires urgent attention from decision makers. The fight against air begins with the rigorous monitoring of its levels, followed by intelligent statistical analysis application advanced machine learning algorithms. To effectively reduce pollution, makers must focus on reducing primary sources such as industrial plants obsolete vehicles, well policies that encourage adoption clean energy sources. In this study, data was performed for first time to evaluate based SPSS program. Correlation coefficients between meteorological parameters particulate matter concentrations (PM1, PM2.5, PM10) were calculated in two urban regions Romania (Craiova Drobeta-Turnu Severin) Turkey (Adana). This study establishes strong relationships PM correlation ranging −0.617 (between temperature relative humidity) 0.998 PMs). It shows negative correlations (−0.241 −0.173 Turkey) effects humidity moderately positive PMs (up 0.360 Turkey), highlighting valuable insights offered independent sensor networks assessing improving quality.

Язык: Английский

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An intelligent interval forecasting system based on fuzzy time series and error distribution characteristics for air quality index

Environmental Research, Год журнала: 2024, Номер 251, С. 118577 - 118577

Опубликована: Март 2, 2024

Язык: Английский

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ADNNet: Attention-based deep neural network for Air Quality Index prediction

Expert Systems with Applications, Год журнала: 2024, Номер 258, С. 125128 - 125128

Опубликована: Авг. 17, 2024

Язык: Английский

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Using Random Forest to improve EMEP4PL model estimates of daily PM2.5 in Poland

Atmospheric Environment, Год журнала: 2024, Номер 332, С. 120615 - 120615

Опубликована: Май 31, 2024

Long-term exposure to poor air quality is responsible for many diseases and increased mortality worldwide. European Environmental Agency reports that Poland one of the most polluted countries in Europe due high emissions associated with large coal wood consumption specific weather conditions. Exceedances WHO-recommended PM2.5 thresholds are still common Poland, so further action needed protect health population. Atmospheric chemical transport models (CTMs) provide information on public used regulate pollutant emissions. However, uncertainties CTMs, related e.g. physical/chemical processes input data often lead underestimation concentrations, especially PM2.5, limits applicability CTMs impact studies. A hybrid approach combining EMEP4PL model Random Forest (RF) machine learning algorithm was applied address limitations CTM reduce its underestimation. We EMEP4PL-modelled concentrations period 2016-2019 as a predictor measured daily from 71 monitoring stations dependent variable three RF scenarios, which differed terms selected predictors. The different additional variables area revealed, including population emission data, dominant type land use, Weather Research Forecast (WRF) meteorological parameters, temporal patterns across years. were evaluated random 5-fold spatial leave-one-station-out cross-validations (LOSOCV), well an independent test set. Our final achieved set R2 0.71, compared 0.38 EMEP4PL, along reduction negative bias (0.25 μg m-3 RF, -11 EMEP4PL) improved ability detect severe episodes. Enhanced coefficients determination observed all seasons at sites included study, both types cross-validation estimated contribution each group separately discovered, impactful predictors calculated based averages outcome (such day year, week number, etc.) modelled factors temperature, planetary boundary layer height, wind speed, atmospheric pressure. developed provides basis spatiotemporal estimates forecasting region, important step toward better understanding pollution local well-being.

Язык: Английский

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Forecasting ground-level ozone and fine particulate matter concentrations at Craiova city using a meta-hybrid deep learning model

Urban Climate, Год журнала: 2024, Номер 57, С. 102099 - 102099

Опубликована: Авг. 16, 2024

Язык: Английский

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