FORECASTING ACTIVITY OF THE KILAUA VOLCANE USING INTELLIGENT METHODS OF DATA ANALYSIS. Stanislav Zabielin

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1 94 International Journal Information Theories and Applications, Vol. 25, Number 1, 2018 FORECASTING ACTIVITY OF THE KILAUA VOLCANE USING INTELLIGENT METHODS OF DATA ANALYSIS Stanislav Zabielin Abstract: The study of volcanoes in the Hawaiian Islands has worldwide scientific interest. On the island of Hawaii is located one of the most active and famous volcano in world - Kilauea. Despite detailed reports since 1823, none of studies has been carried of predicting its volcanic activity using modern intelligent method of data analysis. This work shows the results intelligent analysis using polynomial neural networks based on air emissions of various gases from 1970 to Was achieved a high prediction accuracy (~10%) and was given a forecast of the volcano activity for the next 10 years. Keywords: neural networks, logistic regression, volcano, volcanic activity, intelligent methods of data analysis. ITHEA Keywords: 2.8 Problem Solving, Control Methods, and Search Introduction Kilauea is a shield volcano on the island of Hawaii at the southeast end of the Hawaiian archipelago. According to Hawaiian oral tradition, the eruptions from Kilauea occurred thousands of years ago. It is one of the most active and famous volcanoes in the world, and most of our current knowledge of volcanism stems from his research. Kilauea often erupted and sprouted for a long periods of time. Kilauea is one of the most studied volcanoes in the world. Since the arrival of the first Christian missionaries in his vicinity in 1823, detailed descriptions of volcanic eruptions have been conducted. In 1912, after the construction of the Hawaiian Observatory of Volcanoes on the Kilauea Caldera, continuous scientific research is being conducted. Today, this observatory has become one of the world's leading centers of volcanic research [Holcomb, 1980].

2 International Journal Information Theories and Applications, Vol. 25, Number 1, Related work During long time study of volcanoes, many different methods for forecasting their activity have been proposed, from seismological analysis, up to statistical analysis of their emissions. Nevertheless, the complex analysis of the data array with the help of intelligent methods of data analysis was not performed earlier. Materials and Methods Magma contains dissolved gases, which provide the driving force that causes most volcanic eruptions. As magma rises to the surface and pressure decreases, gases are released from the liquid portion of the magma and continue to move upward and eventually are released into the atmosphere. Large eruptions can release a huge amounts of gas in a short time. The most common volcanic gas is water vapor, which is harmless. However, significant amounts of carbon dioxide, sulfur dioxide, hydrogen sulphide and hydrogen halides can also be released from volcanoes [2, p. 1]. For prediction of volcanic activity, the following indicators were selected: Temperature anomaly (C/month) - this term means a deviation from the reference value or the long-term mean. Carbon dioxide (ppm) - carbon dioxide is about 0.04% of the air in the Earth's atmosphere. On average, a year the volcanoes produce about million tons of carbon dioxide [2, p. 1]. Sulfur dioxide (tons per month) is a colorless gas with an acute odor that irritates the skin and mucous membranes of the eyes, nose and throat [2, p. 1]. Precipitation (mm per month) Carbon-13 is one of the natural isotopes, it accounts for about 1.1% of all natural carbon on the Earth. Oxygen-18 - is a natural, stable isotope of oxygen, one of the natural isotopes. The initial indicators were selected by cross-validation and sequential input and output from the model, followed by an assessment of the quality of the model. The output variable was the logical variable E: E = {1 if there is an eruption, 0 otherwise}

3 96 International Journal Information Theories and Applications, Vol. 25, Number 1, 2018 For forecasting, two methods suitable for logical output were chosen: logistic regression and a restricted Boltzmann machine. Logistic regression Logistic regression is used to predict the probability of occurrence of an event from the values of a set of characteristics. To do this, we introduce the so-called dependent variable y, which takes only one of two values: as a rule, these numbers are 0 (the event did not happen) and 1 (the event occurred), and a set of independent variables (also called attributes, predictors or regressors) on the basis of which values it is required to calculate the probability of taking a particular value of the dependent variable. Logistic regression is the appropriate regression analysis to conduct when the dependent variable is dichotomous (binary). Like all regression analyses, the logistic regression is a predictive analysis. Logistic regression is used to describe data and to explain the relationship between one dependent binary variable and one, more nominal, ordinal, interval, or ratio-level independent variables. [Hosmer, Lemeshow, Sturdivant 2013] Restricted Boltzmann machine The Restricted Boltzmann machine, abbreviated RBM is a kind of generative stochastic neural network that determines the probability distribution on the input data samples. The first restricted Boltzmann machine was built in 1986 by Paul Smolensky under the name Harmonium, but gained popularity only after Hinton's invention of fast learning algorithms in the mid- 2000s. A feature of Boltzmann's restricted machines is the ability to pass training without a teacher, but in certain applications, restricted Boltzmann machines are trained with the teacher. The hidden layer of the machine represents deep signs in the data that are revealed in the learning process. [Hinton, 2006] Results Figure 1 shows the data of the level sulfur dioxide and the eruption of Kilauea volcano during [Elias, 1998], as can be seen in years, peaks of the gas level corresponding to eruptions are observed.

4 International Journal Information Theories and Applications, Vol. 25, Number 1, Figure 1. Level of SO Table 1. Forecasting results Data source Percentage of errors,% RMSE Logistic regression 10, ,32691 The restricted Boltzmann machine (10-10 configuration) 9, , The restricted Boltzmann machine ( configuration) 9, ,30266 Restricted Boltzmann machine ( configuration) 9, ,30266

5 98 International Journal Information Theories and Applications, Vol. 25, Number 1, 2018 Table 1 presents the results of forecasting obtained by logistic regression and by restricted Boltzmann machine. The methods were implemented in the language of R. As can be seen, the restricted Boltzmann machine (3-layer configuration) showed the best results. Figure 3 shows the forecast for 10 years, obtained based on the use of vector autoregression on the input data and their further processing by the neural network. The forecast was given since 2013, since some of the input data was available only before this period. Conclusion In this paper, the Kilauea volcano was predicted using a restricted Boltzmann machine. Using crossvalidation, input parameters were selected in the form of atmospheric emissions of various gases. Hidden until today, the relationship between the level of atmospheric gases and the activity of the volcano was discovered. An acceptable level of errors was achieved in the construction of the model. Further, these studies may lead to a global model for a more accurate prediction of volcanic activity around the world. Bibliography [Holcomb, 1980] R.T. Holcomb. Kilauea Volcano, Hawaii: Chronology and Morphology of the Surficial Lava Flows. U.S. Geological Survey, Menlo Park, [Program, 2018] Program, V.H. USGS: Volcano Hazards Program, [Hosmer, Lemeshow, Sturdivant 2013] D. Hosmer, S. Lemeshow & R. Sturdivant, Applied Logistic Regression. John Wiley & Sons, New York, [Hinton, 2006] G.E. Hinton. Reducing the Dimensionality of Data with Neural Networks. Journal Science, Vol. 313, Issue pp ISSN: (print version), ISSN: (online), doi: /science [Elias, 1998] T. Elias. Sulfur dioxide emission rates of Kilauea Volcano. U.S. Geological Survey, Menlo Park, 1998.

6 International Journal Information Theories and Applications, Vol. 25, Number 1, Authors' Information Stanislav Zabielin Igor Sikorsky Kyiv Polytechnic Institute; PhD Student. Kyiv, Ukraine; Major Fields of Scientific Research: Big Data analysis, Intelligent methods of data analysis.