scholarly journals Tsunami flood risk prediction using a neural network

2014 ◽  
Author(s):  
H. Gotoh ◽  
M. Takezawa
Keyword(s):  
Neural Network ◽  
Risk Prediction ◽  
Flood Risk

scholarly journals Research on Risk Prediction of Dyslipidemia in Steel Workers Based on Recurrent Neural Network and LSTM Neural Network

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 34153-34161 ◽  
Author(s):  
Shiyue Cui ◽  
Chao Li ◽  
Zhe Chen ◽  
Jiaojiao Wang ◽  
Juxiang Yuan
Keyword(s):  
Neural Network ◽  
Risk Prediction ◽  
Recurrent Neural Network ◽  
Steel Workers

Hybrid Model for Credit Risk Prediction: An Application of Neural Network Approaches

2019 ◽  
Vol 28 (05) ◽  
pp. 1950017 ◽  
Author(s):  
Guotai Chi ◽  
Mohammad Shamsu Uddin ◽  
Mohammad Zoynul Abedin ◽  
Kunpeng Yuan
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Logistic Regression ◽  
Credit Risk ◽  
Risk Prediction ◽  
Hybrid Model ◽  
Credit Scoring ◽  
Hybrid Models ◽  
Multilayer Perceptrons ◽  
Inference Systems

Credit risk prediction is essential for banks and financial institutions as it helps them to evade any inappropriate assessments that can lead to wasted opportunities or monetary losses. In recent times, the hybrid prediction model, a combination of traditional and modern artificial intelligence (AI) methods that provides better prediction capacity than the use of single techniques, has been introduced. Similarly, using conventional and topical artificial intelligence (AI) technologies, researchers have recommended hybrid models which amalgamate logistic regression (LR) with multilayer perceptron (MLP). To investigate the efficiency and viability of the proposed hybrid models, we compared 16 hybrid models created by combining logistic regression (LR), discriminant analysis (DA), and decision trees (DT) with four types of neural network (NN): adaptive neurofuzzy inference systems (ANFISs), deep neural networks (DNNs), radial basis function networks (RBFs) and multilayer perceptrons (MLPs). The experimental outcome, investigation, and statistical examination express the capacity of the planned hybrid model to develop a credit risk prediction technique different from all other approaches, as indicated by ten different performance measures. The classifier was authenticated on five real-world credit scoring data sets.

Utilizing Multilayer Perceptron Neural Network for Crash Risk Prediction Based on a Full Set of Data

CICTP 2018 ◽  
2018 ◽  
Author(s):  
Yi Gao ◽  
Zhen Gao ◽  
Rongjie Yu ◽  
Zhiqing Huang ◽  
Jinsong Feng
Keyword(s):  
Neural Network ◽  
Risk Prediction ◽  
Multilayer Perceptron ◽  
Crash Risk

scholarly journals Urban Flood Hazard Modeling Using Self-Organizing Map Neural Network

Water ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 2370 ◽  
Author(s):  
Rahmati ◽  
Darabi ◽  
Haghighi ◽  
Stefanidis ◽  
Kornejady ◽  
...  
Keyword(s):  
Neural Network ◽  
Flood Risk ◽  
Goodness Of Fit ◽  
Flood Hazard ◽  
Characteristic Curve ◽  
Urban Environments ◽  
Hazard Mapping ◽  
Self Organizing Map ◽  
Urban Flood ◽  
Self Organizing

Floods are the most common natural disaster globally and lead to severe damage, especially in urban environments. This study evaluated the efficiency of a self-organizing map neural network (SOMN) algorithm for urban flood hazard mapping in the case of Amol city, Iran. First, a flood inventory database was prepared using field survey data covering 118 flooded points. A 70:30 data ratio was applied for training and validation purposes. Six factors (elevation, slope percent, distance from river, distance from channel, curve number, and precipitation) were selected as predictor variables. After building the model, the odds ratio skill score (ORSS), efficiency (E), true skill statistic (TSS), and the area under the receiver operating characteristic curve (AUC-ROC) were used as evaluation metrics to scrutinize the goodness-of-fit and predictive performance of the model. The results indicated that the SOMN model performed excellently in modeling flood hazard in both the training (AUC = 0.946, E = 0.849, TSS = 0.716, ORSS = 0.954) and validation (AUC = 0.924, E = 0.857, TSS = 0.714, ORSS = 0.945) steps. The model identified around 23% of the Amol city area as being in high or very high flood risk classes that need to be carefully managed. Overall, the results demonstrate that the SOMN model can be used for flood hazard mapping in urban environments and can provide valuable insights about flood risk management.

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