Hybrid Systems for River Flood Forecasting Using MLP, SOM and Fuzzy Systems

Valença, Ivna; Ludermir, Teresa B.

doi:10.1007/978-3-642-04274-4_58

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…According to the literature review, famous methods or techniques for flood forecasting include machine learning [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29] and hydrological techniques [7][8][9][10][11][12][13][14]. However, most involved studies revealed the frequently used technique for forecasting works by big data, i.e., machine learning, rather than hydrological techniques [33][34][35][36][37][38][39] because hydrological models are mostly used with fixed factors that are less flexible to be increased as needed.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This research, therefore, relied on machine learning techniques for flood forecasting. The techniques widely used for such forecasting include decision tree (DT) [21][22][23], ANN [15][16][17], MLC [24][25][26], fuzzy logic [18][19][20], and SOM [27][28][29]. These techniques contain high accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In term of hydrology, the famous models for flood forecasting include Mike11 [7,8], Mike21 [9,10], HecRAS [11,12], and HecGeoRAS [13,14]. As for machine learning, the famous models or techniques for flood forecasting include artificial neural network (ANN) [15][16][17], fuzzy logic [18][19][20], decision tree [21][22][23], maximum likelihood [24][25][26], and self-organization map (SOM) [27][28][29]. The results of flood forecasting revealed close accuracy between hydrology and machine learning [30][31][32].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Puttinaovarat

Horkaew

2019

Int. J. Interact. Mob. Technol.

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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.

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