Artificial Neural Network to predict mean monthly total ozone in Arosa, Switzerland

Present study deals with the mean monthly total ozone time series over Arosa, Switzerland. The study period is 1932-1971. First of all, the total ozone time series has been identified as a complex system and then Artificial Neural Networks models in the form of Multilayer Perceptron with back propagation learning have been developed. The models are Single-hidden-layer and Two-hidden-layer Perceptrons with sigmoid activation function. After sequential learning with learning rate 0.9 the peak total ozone period (February-May) concentrations of mean monthly total ozone have been predicted by the two neural net models. After training and validation, both of the models are found skillful. But, Two-hidden-layer Perceptron is found to be more adroit in predicting the mean monthly total ozone concentrations over the aforesaid period.Comment: 22 pages, 14 figure

Total ozone time series analysis: a neural network model approach

International audienceThis work is focused on the application of neural network based models to the analysis of total ozone (TO) time series. Processes that affect total ozone are extremely non linear, especially at the considered European mid-latitudes. Artificial neural networks (ANNs) are intrinsically non-linear systems, hence they are expected to cope with TO series better than classical statistics do. Moreover, neural networks do not assume the stationarity of the data series so they are also able to follow time-changing situations among the implicated variables. These two features turn NNs into a promising tool to catch the interactions between atmospheric variables, and therefore to extract as much information as possible from the available data in order to make, for example, time series reconstructions or future predictions. Models based on NNs have also proved to be very suitable for the treatment of missing values within the data series. In this paper we present several models based on neural networks to fill the missing periods of data within a total ozone time series, and models able to reconstruct the data series. The results released by the ANNs have been compared with those obtained by using classical statistics methods, and better accuracy has been achieved with the non linear ANNs techniques. Different network structures and training strategies have been tested depending on the specific task to be accomplished

Neural network ensembles: Evaluation of aggregation algorithms

Ensembles of artificial neural networks show improved generalization capabilities that outperform those of single networks. However, for aggregation to be effective, the individual networks must be as accurate and diverse as possible. An important problem is, then, how to tune the aggregate members in order to have an optimal compromise between these two conflicting conditions. We present here an extensive evaluation of several algorithms for ensemble construction, including new proposals and comparing them with standard methods in the literature. We also discuss a potential problem with sequential aggregation algorithms: the non-frequent but damaging selection through their heuristics of particularly bad ensemble members. We introduce modified algorithms that cope with this problem by allowing individual weighting of aggregate members. Our algorithms and their weighted modifications are favorably tested against other methods in the literature, producing a sensible improvement in performance on most of the standard statistical databases used as benchmarks.Comment: 35 pages, 2 figures, In press AI Journa

Artificial Neural Networks applied to improve low-cost air quality monitoring precision

It is a fact that air pollution is a major environmental health problem that affects everyone, especially in urban areas. Furthermore, the cost of high-end air pollution monitoring sensors is considerably high, so public administrations are unable to afford to place an elevated number of measuring stations, leading to the loss of information that could be very helpful. Over the last few years, a large number of low-cost sensors have been released, but its use is often problematic, due to their selectivity and precision problems. A calibration process is needed in order to solve an issue with many parameters with no clear relationship among them, which is a field of application of Machine Learning. The objectives of this project are first, integrating three low-cost air quality sensors into a Raspberry Pi and then, training an Artificial Neural Network model that improves precision in the readings made by the sensors.Es un hecho que la contaminación del aire es un gran problema para la salud a nivel mundial, especialmente en zonas urbanas. Además, el coste de los sensores de contaminación de gama alta es considerablemente alto, por lo que los organismos públicos no pueden permitirse emplazar un gran número de estaciones de medida, perdiendo información que podría ser muy útil. A lo largo de los últimos años, han surgido muchos sensores de contaminación de bajo coste, pero su uso suele ser complicado, ya que tienen problemas de selectividad y precisión. Los objetivos de este proyecto son primero integrar tres sensores de contaminación de bajo coste en una Raspberry Pi y sobre todo, entrenar un modelo basado en una red neuronal artificial que mejore la precisión de las lecturas realizadas por los sensores.Està demostrat que la contaminació de l'aire és un gran problema per a la salut a nivell mundial, especialment en zones urbanes. A més, el cost dels sensors de contaminació de gama alta és considerablement alt, motiu pel qual els organismes públics no es poden permetre emplaçar una gran quantitat d'estacions de mesura, perdent informació que podria resultar molt útil. Al llarg dels últims anys, han sorgit molts sensors de contaminació de baix cost, però el seu ús és sovint complicat, ja que tenen problemes de selectivitat i precisió. Els objectius d'aquest projecte són primer de tot integrar tres sensors de contaminació de baix cost en una Raspberry Pi i sobretot, entrenar un model basat en una xarxa neuronal artificial que millori la precisió de les lectures realitzades pels sensors