Abstract
The particulate matter PM10 concentrations have been impacting hospital admissions due to respiratory diseases. The air pollution studies seek to understand how this pollutant affects the health system. Since prediction involves several variables, any disparity causes a disturbance in the overall system, increasing the difficulty of the models’ development. Due to the complex nonlinear behavior of the problem and their influencing factors, Artificial Neural Networks are attractive approaches for solving estimations problems. This paper explores two neural network architectures denoted unorganized machines: the echo state networks and the extreme learning machines. Beyond the standard forms, models variations are also proposed: the regularization parameter (RP) to increase the generalization capability, and the Volterra filter to explore nonlinear patterns of the hidden layers. To evaluate the proposed models’ performance for the hospital admissions estimation by respiratory diseases, three cities of São Paulo state, Brazil: Cubatão, Campinas and São Paulo, are investigated. Numerical results show the standard models’ superior performance for most scenarios. Nevertheless, considering divergent intensity in hospital admissions, the RP models present the best results in terms of data dispersion. Finally, an overall analysis highlights the models’ efficiency to assist the hospital admissions management during high air pollution episodes.
Highlights
The Nonlinear Output Layers strategy is applied to the three single forms creating the extreme learning machines (ELM) with Volterra Filtering Structure (ELM Volt), the echo state networks (ESN) J
This work predicted the hospital admissions due to respiratory diseases caused by the particulate matter PM10 concentrations using the extreme learning machines (ELM) and the echo state networks (ESN) in the standard forms and applying the variations from the regularization parameter (RP) and the Volterra filter
Numerical results indicated the superior performance of the standard models, pointing to ELM as the best predictor for most scenarios
Summary
Kassomenos et al [24] have shown that the ANN had better performance than linear approaches like the GLM when dealing with nonlinear mapping problems In this context, Tadano et al [26] proposed to use two models, known as Unorganized Machines (UM): the echo state networks (ESN) and the extreme learning machines (ELM), to predict hospital admissions. The main contribution of this research is an epistemological study that predicts the impact of PM10 (particulate matter with an aerodynamic diameter less than 10 μm) daily mean concentrations on hospital admissions due to respiratory diseases using versions of the UM: the addition of regularization parameter applied to increase the generalization capability of the models [32] and the use of the Volterra filter to capture nonlinear patterns of the neural information [33]. This work is organized as follows: Section 2 presents the ELM and ESN standard models, the regularization parameter and nonlinear output layer strategies; Section 3 describes the addressed databases; Section 4 shows the computational results and critical analysis regarding the models’ performances; Section 5 presents the main conclusions and future works
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
