Development of an intelligent system for early forecasting and modelling of flood situation on the example of the Republic of Bashkortostan using a proprietary machine and deep learning library

Authors:
raw_author_name: Evgeny Palchevsky
raw_affiliation_string: Financial University under the Government of the Russian Federation, Moscow, Russia
raw_author_name: Vyacheslav Antonov
raw_affiliation_string: Ufa State Aviation Technical University, Ufa, Russia
raw_author_name: Nikolay Filimonov
raw_affiliation_string: Moscow M.V. Lomonosov State University, Moscow, Russia

Abstract: Reliable early flood forecasting models and systems are essential for minimizing and preventing the impacts of floods worldwide. However, the predictive accuracy of such models and systems is often unacceptable for long-term forecasting, creating uncertainty in flood management decision-making. To address this problem, we propose an intelligent «Flood 2.0» system based on a proprietary machine learning and deep learning library and a flood zone visualisation module. The development of a new version of the system is preceded by a previous study «A system based on an artificial neural network of the second generation for decision support in especially significant situations». The machine learning and deep learning library developed within «Flood 2.0» consists of two parts: preprocessing and prediction. Initially, the study considers the method of data preprocessing. The essence of this method is the development and application of an impulse neural network for generation of information filtering rules, which makes it possible to generate a high-quality initial set of retrospective data in an automated mode, cutting off unnecessary digital noise. Next, a predictive part (method) for water level prediction based on a dataset generated by a data preprocessing method is discussed. This water level prediction method consists of: a) a machine learning model represented as a polynomial regression; b) a deep learning model implemented as a modified recurrent neural network (RNN), a classical neural network with long-term short-term memory (LSTM) and a pulse neural network (PNN); c) a flood zone visualisation module, which is implemented on the basis of the Leaflet library and allows modelling flood zones without the use of digital elevation models, i.e. only on the basis of predicted water levels. To verify the effectiveness of the developed «Flood 2.0» system in general and the machine learning and deep learning library in particular, many 10-day-ahead prediction experiments have been conducted. The empirical results show that the developed recurrent neural network model predicts more accurately under the proposed intelligent system “Flood 2.0″. Moreover, the difference compared to other models ranges from 49.22% to 78.98%, which proves the effectiveness of the development of the Flood 2.0 intelligent system in general and mathematical/technical modifications in particular. Thus, an early and accurate forecast, as well as visualised flood zones will give special services the necessary time to carry out flood control measures to prepare for the protection of technical facilities of enterprises and evacuation of the population. Additional research has also shown that the Flood 2.0 system can be used to predict water levels and model flood zones in other regions.