Neural Network Approaches to Modeling of Natural. Emergencies

IOP Conference Series: Earth and Environmental Science PAPER • OPEN ACCESS Neural Network Approaches to Modeling of Natural. Emergencies. Prediction of Lena River Spring High Waters To cite this article: G Struchkova et al 2021 IOP Conf. Ser.: Earth Environ. Sci. 666 032084 View the article online for updates and enhancements. This content was downloaded from IP address 170.106.202.126 on 25/09/2021 at 11:20 International science and technology conference "Earth science" IOP Publishing IOP Conf. Series: Earth and Environmental Science 666 (2021) 032084 doi:10.1088/1755-1315/666/3/032084 Neural Network Approaches to Modeling of Natural. Emergencies. Prediction of Lena River Spring High Waters G Struchkova1, M Lebedev1, V Timofeeva1, T Kapitonova1, A Gavrilieva1 1Larionov Institute of Physical-Technical Problems of the North Siberian Branch of the Russian Academy of Science, 677980 Yakutsk Oktyabrskaya St. 1, Russia E-mail: [email protected] Abstract. Floods play a significant role in terms of damage and safety during construction and operation of crucial objects such as a bridge over the Lena river and underwater crossings of trunk pipelines in the North and the Arctic. A rapid rise of spring high water on the Lena river is due to accelerated melting of snow in a basin and a meridional flow direction of the river. If flood control measures are not taken, then severe economic and social consequences are inevitable, especially in places with complex infrastructure. As, for example, heavily populated cities, the strategically important objects, the underwater crossings of the trunk pipelines, bridges and power lines. This paper presents results of a study of a possibility of use of neural network algorithms to predict danger of the flood from the spring high waters on a section of the Lena river based on statistical archival data obtained over 70 years and an assessment of effectiveness of the neural network approach. The artificial neural networks have proven their effectiveness in solving various prediction problems, especially when using the statistical data. The use of the neural network approach based on the prediction of a time series from previous values gives the good results. Modeling was carried out using methods of a multilayer perceptron (MLP) and radial basis network (RBF). Both selected methods showed sufficient adequacy of selected statistical models. 1. Introduction The most common natural emergencies (E) in Yakutia are the spring high waters, the severe economic and social consequences of which give every reason to consider them especially catastrophic. For 13 years, from 2001 to 2013, the spring flood of the rivers in the Republic caused the damage totaling about 16 billion rubles [1]. The Lena river plays the extremely important role in social and national economic life of the Republic. By nature of the flow of the Lena river three large sections are usually distinguished: the upper one is from sources to a mouth of the Vitim river (1690 km), the middle one is from the Vitim mouth to a confluence of the Aldan river (1400 km) and the lower one is from the mouth of the Aldan river to the Stolb island in the Lena delta (1310 km) [2]. Average duration of the spring high water is 75 days. The spring high water on the Lena river ends on the average in the second decade of June. In the sections of the Lena river, having a large number of islands, the wave of the high water breaks an ice cover sometimes simultaneously in several anabranches. The river flow dispersing along the individual anabranches weakens, which in turn contributes to formation of the jam of increased power, blocking all or almost the entire channel at a Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Published under licence by IOP Publishing Ltd 1 International science and technology conference "Earth science" IOP Publishing IOP Conf. Series: Earth and Environmental Science 666 (2021) 032084 doi:10.1088/1755-1315/666/3/032084 junction. The jams of this origin often cause the catastrophic floods. Amounts of the damage from the spring high water depend on the effectiveness of the flood control measures taken with account of preliminary prognoses of the jams and the ice jam water levels. The prediction of development of the spring high water is the complicated problem that requires taking into account a complex of various factors. Some papers [3,4,5,6,7] consider the use of the neural network approach in the prediction of the flood during the high waters and freshets. There is a choice of the sections for a solution of the prediction problem. The jam water levels are formed under the influence of the factors common to the whole Lena: 1) a growth rate and height of the spring high water; 2) ice and meteorological conditions during the formation and destruction of the ice cover. In this paper, using the hydrological data for a period from 1970 to 2012, the retro- predictive models of the maximum water levels of the Lena river are constructed for the section near the village Tabaga in the neighborhood of Yakutsk. The choice of the section of the study is due to the fact that near the Tabaginsky Cape close to an underwater crossing of a gas pipeline in the vicinity of the village Khatassy, the construction of a bridge over the Lena river has been planned. Besides, in this section, a frequency of the jams and floods for the considered period of time exceeds 0.8. The study of this section will allow solving the problem of ensuring safety of the construction of the bridge, as well as the underwater crossing of the gas pipeline, which has been operating since 2004. During this time, 3 accidents occurred at the underwater crossing of the gas pipeline. By the nature of the flow and the hydrological regime, the part near the Tabaga village corresponds to a type of the sections of the middle Lena [2]. 2. Materials and research methods The many factors influence the water level in the river during the spring high water, in addition, a degree of the influence of the individual factors on each section of the river can vary. Consideration of all the factors is not possible due to excessive complexity and laboriousness. On the basis of analysis of the data, it is established that the following significant natural factors influence the possibility of the development of the extreme spring high waters in this section of the Lena river: discharge of water at a breakup date; the maximum ice thickness; the water level at the beginning of freeze-up; water reserves in the snow; an average air temperature during spring months. To improve quality of the prediction, the large number of the models with different predictors were analyzed. Besides, standard procedures were performed in all variants to eliminate multicollinearity: a correlation matrix was analyzed, namely, pair correlation coefficients between the predictors. The values of the pair correlation coefficients are not significant, they do not exceed 0.6 in modulus, which indicates a weak relationship between the factors. The more detailed analysis was also performed using the determination coefficients of each argument for all the others. The assessment of the values of the determination coefficients showed that they do not exceed 0.4, which also confirmed absence of the significant relationships between the predictors. As the result of the analysis, it was determined that the following predictors are the most significant ones for the given section: the water level at the beginning of the freeze-up; the discharge of water at the breakup date; the maximum ice thickness; the average water reserve in the snow for April, the average air temperature in April and the first ten days of May that allow making the prediction with the lead time from 3 to 8 days. The data from a Yakutsk weather station and the Tabaga gauging station (a distance of about 20 km) were used. The breakup of the Lena river near the village Tabaga begins on May 10-28, and an onset of the maximum water level is on May 20-24. In this case, the multilayer perceptron (MLP) and the radial basis network (RBF) were considered. To solve the majority of the practical problems with the use of the MLP, only one intermediate layer is enough, two layers are used in the special cases, and the networks with three layers are very rarely used in practice. The network of the radial basis function (RBF) type has the intermediate layer of the radial elements, each of which reproduces a Gaussian response surface. Since these functions are nonlinear, no more than one intermediate layer is also sufficient for the modeling of the arbitrary function. To model any function, it is only necessary to take the sufficient number of the radial 2 International science and technology conference "Earth science" IOP Publishing IOP Conf. Series: Earth and Environmental Science 666 (2021) 032084 doi:10.1088/1755-1315/666/3/032084 elements. To determine the optimal structure of the neural network, there were studied the networks with the following parameters: the type is the MLP; the three-layer structure is: 5 input neurons, 1 output neuron, 3, 4, 7, 8 and 10 elements of the hidden layer; the RBF type is with 5 input neurons, 1 output neuron, 3, 5, 7, 10 and 20 neurons of the hidden layer. When using the neural network to solve the prediction problem, the time series is divided into three sets: the training, testing and control samples, which are then fed to the input of the network.

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