Comparative analysis of flood monitoring information systems

Abstract

Against the backdrop of global climate transformations and the increasing frequency of extreme meteorological phenomena, floods are becoming one of the most widespread and destructive natural disasters. According to the World Meteorological Organization, in the period from 2000 to 2023 the consequences of floods affected more than 1.5 billion people, which allows them to be regarded as the predominant type of natural hazard [1]. Overflowing water masses have a significant negative impact on economic systems, infrastructure facilities, and the agricultural sector, and also pose a direct threat to the life and health of the population. In this regard, the implementation of modern digital solutions becomes particularly important, including machine learning methods and artificial intelligence technologies that ensure increased forecasting accuracy and reduced response time [2]. Floods are among the most large-scale in terms of consequences and regularly recurring natural disasters worldwide. According to information from the World Meteorological Organization (WMO), over the past decades there has been a noticeable increase in both the frequency and the intensity of floods, including in territories that were not previously considered high-risk areas [3]. Climate transformations, urbanization processes, disruption of natural ecosystems, and inefficient water resource management contribute to the growing vulnerability of territories and populations to flood and inundation events. It is projected that by 2050 the number of people living in areas of potential flood risk may more than double [4]. This problem is most pronounced in countries characterized by extensive river systems, mountainous terrain, and densely populated lowland areas. The Republic of Kazakhstan also belongs to the group of vulnerable states, as seasonal flood events and emergency situations caused by intense rainfall, rapid snowmelt, and river overflow are recorded annually on its territory. For example, during the spring periods of 2017 and 2023, large-scale floods were documented in several regions of Kazakhstan, resulting in the evacuation of a significant number of residents, damage to infrastructure facilities, and substantial economic losses [5]. Traditional approaches to flood monitoring and response are often characterized by delays and insufficient accuracy of results, as they are based on outdated technological solutions, are susceptible to human factors, and have limited capacity to account for large volumes of real-time information. Under conditions of accelerating natural process dynamics and the growing number of data sources (satellite imagery, meteorological indicators, sensor networks, and IoT devices), the need for the development of automated monitoring systems is increasing, ensuring the rapid collection, processing, and analytical interpretation of information for timely managerial decision-making [6]. The development of artificial intelligence technologies, machine learning methods, and Big Data analytics tools creates fundamentally new opportunities for solving the tasks of automated flood detection and forecasting. Intelligent algorithms are capable of effectively identifying hidden relationships within heterogeneous data streams and adapting to changing operating conditions in real time [7]. The application of such technological solutions contributes to improving the accuracy of predictive assessments, reducing the response time of emergency services, and ultimately ensuring a decrease in human casualties and the minimization of socio-economic damage.