Application Programming Interface for Flood Forecasting from Geospatial Big Data and Crowdsourcing Data

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.

Dengue Risk Mapping from Geospatial Data Using GIS and Data Mining Techniques

“…This research uses data mining techniques to analyze the accuracy of the data because it is a fast and searchable method. Relationships that are hidden in that data set easily [31][32]. It does not require the WLC method to analyze the suitability of the factor because it is a delayed and multi-step because of the need to apply the scoring criteria obtained from the classification of each factor (Rating) in the analysis together with the weight factor obtained from experts.…”

“…The steps are shown in Figure 7: Data Cleaning is a procedure for eliminating unrelated data, while Data Integration is the process of combining data with multiple sources into one set of data. Data Transformation is a data conversion procedure that is suitable for use, while Data Reduction is the process to reduce the complexity of data [31][32]. We choose to use the Decision Tree Algorithm as it is the algorithm that is widely known and suitable for solving complex problems.…”

“…When the results of the analysis are required, Pattern Evaluation is the process of evaluating patterns obtained from data mining. Knowledge Representation is the process of presenting knowledge that has been discovered [31][32].…”

Flood Damage Assessment Geospatial Application Using Geoinformatics and Deep Learning Classification

“…Floods are one of most frequent disasters, and cause impacts as well as damage in different areas depending on land use and land cover type (LULC types) in flooded areas [1][2][3]. According to the studies on the statistics of floods in Thailand during the past 10 years, it was found that floods have occurred every year, with the different severe levels of floods in each area of each year [4][5][6]. Most assessment of impacts and damage caused by floods currently relies on field survey by manpower [7][8][9] to verify and record data to report to government agencies for remedy and assistance of victims from different groups.…”

Regional Inundation Forecasting Using Machine Learning Techniques with the Internet of Things

“…The integration of a large number of IoT sensors can provide on-line comprehensive and broader information to effectively perform environmental monitoring and forecasting [30][31][32][33][34]. Recently, several studies have implemented IoT and big data [35][36][37][38] for flood forecasting. For instance, Chang et al [39] proposed building an intelligent hydro-informatics integration platform to integrate ML models with sensors data for flood prediction.…”