Comparison between ANN and Decision Tree in Aerology Event Prediction

Abstract

Predictive systems use historical and other available data to predict an event. In this paper we tries to compare the power of Artificial Neural Network (ANN) and Decision Tree (DT) in prediction of aerology events with time series streams and events stream using combination of K-means clustering algorithm and Decision Tree C5 algorithm and ANN. We try to find the effective parameters on events occurrences. Firstly, we find the closest time series record for any events; therefore, we have gathered different parameters value when an event is occurring. Using Kmeans we add a field to dataset which determines the cluster of each record and after that we predict the events using C5 algorithm and ANN. This framework and time series model can predict future events efficiently. We gathered 1961 until 2005 data of aerology organization for Tehran Mehrabad Station. This data contains some fields such as wet bulb, relative humidity, amount of cloud, wind speed and etc. This dataset includes 17 types of events. Using this framework the closest event can be predicted. The C5 method is able to predict events with 79.55% accuracy and ANN with 72.87% accuracy. Applying K-means clustering algorithm the prediction increase to 94.59% for C5 and 92.66% accuracies for ANN. We use 10-fold cross validation to evaluate our prediction rate. This framework is the first estimation in the area of event prediction for a huge dataset of aerology and can be extended in many different datasets in any other environments.

Systematic review on event prediction models

“…K-means is one of the clustering algorithms that can help to cluster the data into similar patterns or class and it is also used in numerous cases as the combination of algorithms in prediction model [26,27]. In [28] is one of the examples that does so.…”

Multivariate Temporal Convolutional Network: A Deep Neural Networks Approach for Multivariate Time Series Forecasting

“…With the explosive growth of Internet of Things (IoT) applications and big data, multivariate time series is becoming ubiquitous in many fields, e.g., aerology [1], meteorology [2], environment [3], multimedia [4], power energy [5], finance [6], and transportation [7]. The precise trend forecasting, as well as for potential hazardous events, based on historical dynamical data are a major challenge, especially for aperiodic multivariate time series.…”