436 research outputs found

    Short- and long-term forecasting of ambient air pollution levels using wavelet-based non-linear autoregressive artificial neural networks with exogenous inputs

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    Roadside air pollution is a major issue due to its adverse effects on human health and the environment. This highlights the need for parsimonious and robust forecasting tools that help vulnerable members of the public reduce their exposure to harmful air pollutants. Recent results in air pollution forecasting applications include the use of hybrid models based on non-linear autoregressive artificial neural networks (ANN) with exogenous multi-variable inputs (NARX) and wavelet decomposition techniques. However, attempts employing both methods into one hybrid modelling system have not been widely made. Hence, this work further investigates the utilisation of wavelet-based NARX-ANN models in the shortand long-term prediction of hourly NO2 concentration levels. The models were trained using emissions and meteorological data collected from a busy roadside site in Central London, United Kingdom from January to December 2015. A discrete wavelet transformation technique was then implemented to address the highly variable characteristic of the collected NO2 concentration data. Overall results exhibit the superiority of the wavelet-based NARX-ANN models improving the accuracy of the benchmark NARX-ANN model results by up to 6% in terms of explained variance. The proposed models also provide fairly accurate long-term forecasts, explaining 68–76% of the variance of actual NO2 data. In conclusion, the findings of this study demonstrate the high potential of wavelet-based NARX-ANN models as alternative tools in short- and long-term forecasting of air pollutants in urban environments

    Spatiotemporal and temporal forecasting of ambient air pollution levels through data-intensive hybrid artificial neural network models

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    Outdoor air pollution (AP) is a serious public threat which has been linked to severe respiratory and cardiovascular illnesses, and premature deaths especially among those residing in highly urbanised cities. As such, there is a need to develop early-warning and risk management tools to alleviate its effects. The main objective of this research is to develop AP forecasting models based on Artificial Neural Networks (ANNs) according to an identified model-building protocol from existing related works. Plain, hybrid and ensemble ANN model architectures were developed to estimate the temporal and spatiotemporal variability of hourly NO2 levels in several locations in the Greater London area. Wavelet decomposition was integrated with Multilayer Perceptron (MLP) and Long Short-term Memory (LSTM) models to address the issue of high variability of AP data and improve the estimation of peak AP levels. Block-splitting and crossvalidation procedures have been adapted to validate the models based on Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Willmott’s index of agreement (IA). The results of the proposed models present better performance than those from the benchmark models. For instance, the proposed wavelet-based hybrid approach provided 39.15% and 28.58% reductions in RMSE and MAE indices, respectively, on the performance of the benchmark MLP model results for the temporal forecasting of NO2 levels. The same approach reduced the RMSE and MAE indices of the benchmark LSTM model results by 12.45% and 20.08%, respectively, for the spatiotemporal estimation of NO2 levels in one site at Central London. The proposed hybrid deep learning approach offers great potential to be operational in providing air pollution forecasts in areas without a reliable database. The model-building protocol adapted in this thesis can also be applied to studies using measurements from other sites.Outdoor air pollution (AP) is a serious public threat which has been linked to severe respiratory and cardiovascular illnesses, and premature deaths especially among those residing in highly urbanised cities. As such, there is a need to develop early-warning and risk management tools to alleviate its effects. The main objective of this research is to develop AP forecasting models based on Artificial Neural Networks (ANNs) according to an identified model-building protocol from existing related works. Plain, hybrid and ensemble ANN model architectures were developed to estimate the temporal and spatiotemporal variability of hourly NO2 levels in several locations in the Greater London area. Wavelet decomposition was integrated with Multilayer Perceptron (MLP) and Long Short-term Memory (LSTM) models to address the issue of high variability of AP data and improve the estimation of peak AP levels. Block-splitting and crossvalidation procedures have been adapted to validate the models based on Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Willmott’s index of agreement (IA). The results of the proposed models present better performance than those from the benchmark models. For instance, the proposed wavelet-based hybrid approach provided 39.15% and 28.58% reductions in RMSE and MAE indices, respectively, on the performance of the benchmark MLP model results for the temporal forecasting of NO2 levels. The same approach reduced the RMSE and MAE indices of the benchmark LSTM model results by 12.45% and 20.08%, respectively, for the spatiotemporal estimation of NO2 levels in one site at Central London. The proposed hybrid deep learning approach offers great potential to be operational in providing air pollution forecasts in areas without a reliable database. The model-building protocol adapted in this thesis can also be applied to studies using measurements from other sites

    Three layer wavelet based modeling for river flow

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    All existing methods regarding time series forecasting have always been challenged by the continuous climatic change taking place in the world. These climatic changes influence many unpredictable indefinite factors. This alarming situation requires a robust forecasting method that could efficiently work with incomplete and multivariate data. Most of the existing methods tend to trap into local minimum or encounter over fitting problems that mostly lead to an inappropriate outcome. The complexity of data regarding time series forecasting does not allow any one single method to yield results suitable in all situations as claimed by most researchers. To deal with the problem, a technique that uses hybrid models has also been devised and tested. The applied hybrid methods did bring some improvement compared to the individual model performance. However, most of these available hybrid models exploit univariate data that requires huge historical data to achieve precise forecasting results. Therefore, this study introduces a new hybrid model based on three layered architecture: Least Square Support Vector Machine (LSSVM), Discrete Wavelet Transform (DWT), correlation (R) and Kernel Principle Components Analyses (KPCA). The three-staged architecture of the proposed hybrid model includes Wavelet-LSSVM and Wavelet-KPCA-LSSVM enabling the model to present itself as a well-established alternative application to predict the future of river flow. The proposed model has been applied to four different data sets of time series, taking into account different time series behavior and data scale. The performance of the proposed model is compared against the existing individual models and then a comparison is also drawn with the existing hybrid models. The results of WKPLSSVM obtained from Coefficient of Efficiency (CE) performance measuring methods confirmed that proposed model has encouraging data of 0.98%, 0.99%, 0.94% and 0.99% for Jhelum River, Chenab River, Bernam River and Tualang River, respectively. It is more robust for all datasets regardless of the sample sizes and data behavior. These results are further verified using diverse data sets in order to check the stability and adaptability. The results have demonstrated that the proposed hybrid model is a better alternative tool for time series forecasting. The proposed hybrid model proves to be one of the best available solutions considering the time series forecasting issues

    Prediction of Carbon Monoxide Concentration with Variation of Support Vector Regression Kernel Parameter Value

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    Human and industrial activities produce air pollutants that can cause a decline in air quality. In urban areas, transportation activities are the main source of air pollution. One of the emitted air pollutants produced by transportation is carbon monoxide (CO). The understanding of CO concentration is crucial since its overabundance beyond a certain limit will have a negative impact on human health and the environment. In this study, the support vector regression (SVR) method was used to predict CO concentration. The purpose of this study was to predict the hourly CO concentration in the Ujung Berung district, Bandung City, West Java, Indonesia with optimal prediction accuracy. An experiment was carried out by modeling the CO concentration with varying kernel parameter values to obtain accurate prediction results. The suitability of the values between error (ɛ), a trade-off constant (C), and variation mismatch (γ) is vital to obtain optimal prediction results. The results showed that the best prediction accuracy value was 97.68% with kernel parameter values ɛ = 0.02, γ = 30, and C = 0.006. These results may lead to proper decision making on environmental issues and can improve air pollution control strategies

    Prediction of Carbon Monoxide Concentration with Variation of Support Vector Regression Kernel Parameter Value

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    Human and industrial activities produce air pollutants that can cause a decline in air quality. In urban areas, transportation activities are the main source of air pollution. One of the emitted air pollutants produced by transportation is carbon monoxide (CO). The understanding of CO concentration is crucial since its overabundance beyond a certain limit will have a negative impact on human health and the environment. In this study, the support vector regression (SVR) method was used to predict CO concentration. The purpose of this study was to predict the hourly CO concentration in the Ujung Berung district, Bandung City, West Java, Indonesia with optimal prediction accuracy. An experiment was carried out by modeling the CO concentration with varying kernel parameter values to obtain accurate prediction results. The suitability of the values between error (ɛ), a trade-off constant (C), and variation mismatch (γ) is vital to obtain optimal prediction results. The results showed that the best prediction accuracy value was 97.68% with kernel parameter values ɛ = 0.02, γ = 30, and C = 0.006. These results may lead to proper decision making on environmental issues and can improve air pollution control strategies

    Analysis, Characterization, Prediction and Attribution of Extreme Atmospheric Events with Machine Learning: a Review

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    Atmospheric Extreme Events (EEs) cause severe damages to human societies and ecosystems. The frequency and intensity of EEs and other associated events are increasing in the current climate change and global warming risk. The accurate prediction, characterization, and attribution of atmospheric EEs is therefore a key research field, in which many groups are currently working by applying different methodologies and computational tools. Machine Learning (ML) methods have arisen in the last years as powerful techniques to tackle many of the problems related to atmospheric EEs. This paper reviews the ML algorithms applied to the analysis, characterization, prediction, and attribution of the most important atmospheric EEs. A summary of the most used ML techniques in this area, and a comprehensive critical review of literature related to ML in EEs, are provided. A number of examples is discussed and perspectives and outlooks on the field are drawn.Comment: 93 pages, 18 figures, under revie

    Smart models to improve agrometeorological estimations and predictions

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    La población mundial, en continuo crecimiento, alcanzará de forma estimada los 9,7 mil millones de habitantes en el 2050. Este incremento, combinado con el aumento en los estándares de vida y la situación de emergencia climática (aumento de la temperatura, intensificación del ciclo del agua, etc.) nos enfrentan al enorme desafío de gestionar de forma sostenible los cada vez más escasos recursos disponibles. El sector agrícola tiene que afrontar retos tan importantes como la mejora en la gestión de los recursos naturales, la reducción de la degradación medioambiental o la seguridad alimentaria y nutricional. Todo ello condicionado por la escasez de agua y las condiciones de aridez: factores limitantes en la producción de cultivos. Para garantizar una producción agrícola sostenible bajo estas condiciones, es necesario que todas las decisiones que se tomen estén basadas en el conocimiento, la innovación y la digitalización de la agricultura de forma que se garantice la resiliencia de los agroecosistemas, especialmente en entornos áridos, semi-áridos y secos sub-húmedos en los que el déficit de agua es estructural. Por todo esto, el presente trabajo se centra en la mejora de la precisión de los actuales modelos agrometeorológicos, aplicando técnicas de inteligencia artificial. Estos modelos pueden proporcionar estimaciones y predicciones precisas de variables clave como la precipitación, la radiación solar y la evapotranspiración de referencia. A partir de ellas, es posible favorecer estrategias agrícolas más sostenibles, gracias a la posibilidad de reducir el consumo de agua y energía, por ejemplo. Además, se han reducido el número de mediciones requeridas como parámetros de entrada para estos modelos, haciéndolos más accesibles y aplicables en áreas rurales y países en desarrollo que no pueden permitirse el alto costo de la instalación, calibración y mantenimiento de estaciones meteorológicas automáticas completas. Este enfoque puede ayudar a proporcionar información valiosa a los técnicos, agricultores, gestores y responsables políticos de la planificación hídrica y agraria en zonas clave. Esta tesis doctoral ha desarrollado y validado nuevas metodologías basadas en inteligencia artificial que han ser vido para mejorar la precision de variables cruciales en al ámbito agrometeorológico: precipitación, radiación solar y evapotranspiración de referencia. En particular, se han modelado sistemas de predicción y rellenado de huecos de precipitación a diferentes escalas utilizando redes neuronales. También se han desarrollado modelos de estimación de radiación solar utilizando exclusivamente parámetros térmicos y validados en zonas con características climáticas similares a lugar de entrenamiento, sin necesidad de estar geográficamente en la misma región o país. Analógamente, se han desarrollado modelos de estimación y predicción de evapotranspiración de referencia a nivel local y regional utilizando también solamente datos de temperatura para todo el proceso: regionalización, entrenamiento y validación. Y finalmente, se ha creado una librería de Python de código abierto a nivel internacional (AgroML) que facilita el proceso de desarrollo y aplicación de modelos de inteligencia artificial, no solo enfocadas al sector agrometeorológico, sino también a cualquier modelo supervisado que mejore la toma de decisiones en otras áreas de interés.The world population, which is constantly growing, is estimated to reach 9.7 billion people in 2050. This increase, combined with the rise in living standards and the climate emergency situation (increase in temperature, intensification of the water cycle, etc.), presents us with the enormous challenge of managing increasingly scarce resources in a sustainable way. The agricultural sector must face important challenges such as improving natural resource management, reducing environmental degradation, and ensuring food and nutritional security. All of this is conditioned by water scarcity and aridity, limiting factors in crop production. To guarantee sustainable agricultural production under these conditions, it is necessary to based all the decision made on knowledge, innovation, and the digitization of agriculture to ensure the resilience of agroecosystems, especially in arid, semi-arid, and sub-humid dry environments where water deficit is structural. Therefore, this work focuses on improving the precision of current agrometeorological models by applying artificial intelligence techniques. These models can provide accurate estimates and predictions of key variables such as precipitation, solar radiation, and reference evapotranspiration. This way, it is possible to promote more sustainable agricultural strategies by reducing water and energy consumption, for example. In addition, the number of measurements required as input parameters for these models has been reduced, making them more accessible and applicable in rural areas and developing countries that cannot afford the high cost of installing, calibrating, and maintaining complete automatic weather stations. This approach can help provide valuable information to technicians, farmers, managers, and policy makers in key wáter and agricultural planning areas. This doctoral thesis has developed and validated new methodologies based on artificial intelligence that have been used to improve the precision of crucial variables in the agrometeorological field: precipitation, solar radiation, and reference evapotranspiration. Specifically, prediction systems and gap-filling models for precipitation at different scales have been modeled using neural networks. Models for estimating solar radiation using only thermal parameters have also been developed and validated in areas with similar climatic characteristics to the training location, without the need to be geographically in the same region or country. Similarly, models for estimating and predicting reference evapotranspiration at the local and regional level have been developed using only temperature data for the entire process: regionalization, training, and validation. Finally, an internationally open-source Python library (AgroML) has been created to facilitate the development and application of artificial intelligence models, not only focused on the agrometeorological sector but also on any supervised model that improves decision-making in other areas of interest

    Spatial-temporal PM2.5 Prediction Using MODIS AOD Products

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    학위논문 (석사)-- 서울대학교 대학원 : 사회과학대학 지리학과, 2018. 2. Park, Key Ho.In recently decade haze in China has severely hurt its economy and threatened the health of its population. There is often strong demand from the Ministry for the Environment for assessing, predicting, and trying to reduce the levels of PM2.5 around the country. In practice, PM2.5 data is difficult to measure. Monitor sites are not distributed uniformly, most of them built in urban area. Traditional air pollution epidemiology studies being conducted in large cities can be limited by the availability of monitoring. Satellite Aerosol Optical Depth (AOD) measurements offer the possibility of exposure estimates for the entire population. In this situation, the 10 km MODIS Aerosol Optical Depth (AOD) product can be used as predictor since recent studies has proved the statistical relationship between AOD and PM2.5. The traditional statistical study on AOD and PM2.5 are primarily Geographic Weighted Regression. Based on Gaussian process regression, this study developed a new regression approach to predict PM2.5 distribution in a Bayesian hierarchical setting from October 2016 to October 2017. The spatial non-stationarity was modeled by a Gaussian process with exponential covariance function. Parameters to explain factors like AOD, spatial random effects and non-spatial factors were estimated via a Bayesian hierarchical framework. The result illustrated that our model showed a good daily prediction on unknow sites by giving a 0.76 R^2 under 10 cross validation and a precise annual prediction with R^2 equal to 0.90. For daily model, we compared our result with GWR and a machine learning method support vector machine (0.68 and 0.75 respectively), which showed modeling spatial random effects via Gaussian process was able to improve the accuracy PM2.5 predicting using MODIS AOD data.Chapter 1 Introduction 1 1.1 Research Motivation 1 1.2 Problem Description 2 1.3 Research Objective and Research Question 4 1.4 Methodology 4 1.5 Contribution 7 Chapter 2 Literature Review 9 2.1 Introduction to PM2.5 9 2.2 Aerosol Optical Depth 12 2.3 Satellite Data and Algorithms for AOD retrieval 14 2.3.1 The MODIS AOD product 15 2.3.2 Validation on MODIS AOD in China 16 2.4 PM2.5 Estimation based on AOD 20 2.4.1 Theoretical basis 20 2.4.2 Estimation Models 23 2.5 Machine Learning Methods 27 Chapter 3 Study Area and Data 33 3.1 Study Area 33 3.2 Data Acquisition 34 3.2.1 MODIS 10km Products 34 3.2.2 PM2.5 ground monitoring data 35 3.2.3 Supplementary Data 37 Chapter 4 Model 40 4.1 Overview of Workflow 40 4.2 Data Pre-processing 41 4.3 Model Construction 43 4.3.1 Gaussian Process Regression Model 43 4.3.2 Geographically Weighted Regression Model 48 4.3.3 Support Vector Regression Model 49 Chapter 5 Results and Analysis 51 5.1 Descriptive Statistics on dataset 51 5.2 Model validation 52 Chapter 6 Conclusions and Limitations 61 5.1 Conclusion 61 5.2 Limitation of this study 63 Bibliography 64Maste
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