Air Quality Modeling Using the PSO-SVM-Based Approach, MLP Neural Network, and M5 Model Tree in the Metropolitan Area of Oviedo (Northern Spain)

Nieto, P.J. Garcı́a; García–Gonzalo, Esperanza; Sánchez, Antonio Bernardo; Miranda, Abhilash Alexander

doi:10.1007/s10666-017-9578-y

Cited by 25 publications

(10 citation statements)

References 32 publications

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“…DT was originally proposed for solving classification problems using a splitting method, for which the available information from the data is extracted via the construction of a tree composed of three kinds of nodes: the internal, the roots, and the leaves nodes [45]. The M5Tree has been used for solving several problems, such as predictions of energy consumption in buildings [69], air quality modeling [70], predicting liquefaction-induced lateral spreading [71], forecasting solar ultraviolet [72], and predicting daily water levels in rivers [73]. The M5Tree is a regression model in which the training data are being apportioned to smaller subsets through the construction of a tree and using a gain ratio criterion, an individual regression model is built for each subset [45].…”

Section: M5 Model Treementioning

confidence: 99%

Prediction of Potential Evapotranspiration Using Temperature-Based Heuristic Approaches

Adnan

Heddam

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

et al. 2020

Sustainability

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The potential or reference evapotranspiration (ET0) is considered as one of the fundamental variables for irrigation management, agricultural planning, and modeling different hydrological pr°Cesses, and therefore, its accurate prediction is highly essential. The study validates the feasibility of new temperature based heuristic models (i.e., group method of data handling neural network (GMDHNN), multivariate adaptive regression spline (MARS), and M5 model tree (M5Tree)) for estimating monthly ET0. The outcomes of the newly developed models are compared with empirical formulations including Hargreaves-Samani (HS), calibrated HS, and Stephens-Stewart (SS) models based on mean absolute error (MAE), root mean square error (RMSE), and Nash-Sutcliffe efficiency. Monthly maximum and minimum temperatures (Tmax and Tmin) observed at two stations in Turkey are utilized as inputs for model development. In the applications, three data division scenarios are utilized and the effect of periodicity component (PC) on models’ accuracies are also examined. By importing PC into the model inputs, the RMSE accuracy of GMDHNN, MARS, and M5Tree models increased by 1.4%, 8%, and 6% in one station, respectively. The GMDHNN model with periodic input provides a superior performance to the other alternatives in both stations. The recommended model reduced the average error of MARS, M5Tree, HS, CHS, and SS models with respect to RMSE by 3.7–6.4%, 10.7–3.9%, 76–75%, 10–35%, and 0.8–17% in estimating monthly ET0, respectively. The HS model provides the worst accuracy while the calibrated version significantly improves its accuracy. The GMDHNN, MARS, M5Tree, SS, and CHS models are also compared in estimating monthly mean ET0. The GMDHNN generally gave the best accuracy while the CHS provides considerably over/under-estimations. The study indicated that the only one data splitting scenario may mislead the modeler and for better validation of the heuristic methods, more data splitting scenarios should be applied.

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Section: M5 Model Treementioning

confidence: 99%

Prediction of Potential Evapotranspiration Using Temperature-Based Heuristic Approaches

Adnan

Heddam

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

et al. 2020

Sustainability

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“…Wang et al [23] combined ARIMA with SVM to design a hybrid Garch model that was applied in the PM 2.5 prediction. García Nieto et al [24] explored the different machine learning methods in the air quality modeling, including the particle swarm optimization with SVM, multilayer perception network, and model tree. It can be seen from the explorations that an appropriate model should be selected and built considering the nonlinear fitting ability as well as the network complexity.…”

Section: Related Workmentioning

confidence: 99%

Spatio-Temporal Prediction for the Monitoring-Blind Area of Industrial Atmosphere Based on the Fusion Network

Bai

Wang

Sun

et al. 2019

IJERPH

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The monitoring-blind area exists in the industrial park because of private interest and limited administrative power. As the atmospheric quality in the blind area impacts the environment management seriously, the prediction and inference of the blind area is explored in this paper. Firstly, the fusion network framework was designed for the solution of “Circumjacent Monitoring-Blind Area Inference”. In the fusion network, the nonlinear autoregressive network was set up for the time series prediction of circumjacent points, and the full connection layer was built for the nonlinear relation fitting of multiple points. Secondly, the physical structure and learning method was studied for the sub-elements in the fusion network. Thirdly, the spatio-temporal prediction algorithm was proposed based on the network for the blind area monitoring problem. Finally, the experiment was conducted with the practical monitoring data in an industrial park in Hebei Province, China. The results show that the solution is feasible for the blind area analysis in the view of spatial and temporal dimensions.

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“…Finally, in recent years, a method based on deep learning has had satisfactory results in air quality prediction [17]. This kind of method uses the neural network model to predict the air quality index, which can better cope with the uncertainty of environmental changes and other complex problems [18][19][20][21]. For instance, the artificial neural network (ANN), proposed in the 1980s, mimics the function of neurons in the human brain so that it can achieve the same effect as through the use of human numerical calculations.…”

Section: Introductionmentioning

confidence: 99%

Hyperparameter-Optimization-Inspired Long Short-Term Memory Network for Air Quality Grade Prediction

et al. 2023

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In the world, with the continuous development of modern society and the acceleration of urbanization, the problem of air pollution is becoming increasingly salient. Methods for predicting the air quality grade and determining the necessary governance are at present most urgent problems waiting to be solved by human beings. In recent years, more and more machine-learning-based methods have been used to solve the air quality prediction problem. However, the uncertainty of environmental changes and the difficulty of precisely predicting quantitative values seriously influence prediction results. In this paper, the proposed air pollutant quality grade prediction method based on a hyperparameter-optimization-inspired long short-term memory (LSTM) network provides two advantages. Firstly, the definition of air quality grade is introduced in the air quality prediction task, which turns a fitting problem into a classification problem and makes the complex problem simple; secondly, the hunter–prey optimization algorithm is used to optimize the hyperparameters of the LSTM structure to obtain the optimal network structure adaptively determined through the use of input data, which can include more generalization abilities. The experimental results from three real Xi’an air quality datasets display the effectiveness of the proposed method.

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Air Quality Modeling Using the PSO-SVM-Based Approach, MLP Neural Network, and M5 Model Tree in the Metropolitan Area of Oviedo (Northern Spain)

Cited by 25 publications

References 32 publications

Prediction of Potential Evapotranspiration Using Temperature-Based Heuristic Approaches

Prediction of Potential Evapotranspiration Using Temperature-Based Heuristic Approaches

Spatio-Temporal Prediction for the Monitoring-Blind Area of Industrial Atmosphere Based on the Fusion Network

Hyperparameter-Optimization-Inspired Long Short-Term Memory Network for Air Quality Grade Prediction

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