Abstract
Nowadays, natural disasters tend to increase and become more severe. They do affect life and belongings of great numbers of people. One kind of such disasters that hap-pen frequently almost every year is floods in all regions across the world. A prepara-tion measure to cope with upcoming floods is flood forecasting in each particular area in order to use acquired data for monitoring and warning to people and involved per-sons, resulting in the reduction of damage. With advanced computer technology and remote sensing technology, large amounts of applicable data from various sources are provided for flood forecasting. Current flood forecasting is done through computer processing by different techniques. The famous one is machine learning, of which the limitation is to acquire a large amount big data. The one currently used still requires manpower to download and record data, causing delays and failures in real-time flood forecasting. This research, therefore, proposed the development of an automatic big data downloading system from various sources through the development of applica-tion programming interface (API) for flood forecasting by machine learning. This research relied on 4 techniques, i.e., maximum likelihood classification (MLC), fuzzy logic, self-organization map (SOM), and artificial neural network with RBF Kernel. According to accuracy assessment of flood forecasting, the most accurate technique was MLC (99.2%), followed by fuzzy logic, SOM, and RBF (97.8%, 96.6%, and 83.3%), respectively.
Highlights
Floods are a kind of natural disasters that happen almost every year in each region across the world, with effects in wide areas [1]
This research proposed a method to reduce the limitations of traditional methods, through the development of automatic big data downloading system by application programming interface (API) from involved sources of data, i.e., Thailand Meteorological Department (TMD), GLOFAS [40], and database system
At the step of flood forecasting by using each technique of machine learning for leading to learning process, the techniques used for comparison to find flood forecasting efficiency in this research included maximum likelihood classification (MLC), Fuzzy Logic, self-organization map (SOM), and RBF
Summary
Floods are a kind of natural disasters that happen almost every year in each region across the world, with effects in wide areas [1]. They still relied on traditional methods, e.g., sending data via file uploading The limitation of those methods was that they still required manpower to send data to agencies or demanding persons. As for machine learning, the famous models or techniques for flood forecasting include artificial neural network (ANN) [15,16,17], fuzzy logic [1820], decision tree [21,22,23], maximum likelihood [24,25,26], and self-organization map (SOM) [27,28,29]. When using big data and machine learning for flood forecasting, traditional data is still mostly relied on. The purpose is to use data for flood forecasting by machine learning techniques, i.e., MLC, fuzzy logic, SOM, and RBF, which will bring accurate and in-time flood forecasting in order to use data further for flood monitoring and warning; and for efficient flood management
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