scholarly journals GIS Based Hybrid Computational Approaches for Flash Flood Susceptibility Assessment

Water ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 683 ◽  
Author(s):  
Binh Thai Pham ◽  
Mohammadtaghi Avand ◽  
Saeid Janizadeh ◽  
Tran Van Phong ◽  
Nadhir Al-Ansari ◽  
...  
Keyword(s):  
Land Use ◽  
Feature Selection ◽  
Decision Tree ◽  
Flash Flood ◽  
Feature Selection Method ◽  
Flash Floods ◽  
Computational Approaches ◽  
Flood Susceptibility ◽  
Statistical Measures ◽  
Susceptibility Maps

Flash floods are one of the most devastating natural hazards; they occur within a catchment (region) where the response time of the drainage basin is short. Identification of probable flash flood locations and development of accurate flash flood susceptibility maps are important for proper flash flood management of a region. With this objective, we proposed and compared several novel hybrid computational approaches of machine learning methods for flash flood susceptibility mapping, namely AdaBoostM1 based Credal Decision Tree (ABM-CDT); Bagging based Credal Decision Tree (Bag-CDT); Dagging based Credal Decision Tree (Dag-CDT); MultiBoostAB based Credal Decision Tree (MBAB-CDT), and single Credal Decision Tree (CDT). These models were applied at a catchment of Markazi state in Iran. About 320 past flash flood events and nine flash flood influencing factors, namely distance from rivers, aspect, elevation, slope, rainfall, distance from faults, soil, land use, and lithology were considered and analyzed for the development of flash flood susceptibility maps. Correlation based feature selection method was used to validate and select the important factors for modeling of flash floods. Based on this feature selection analysis, only eight factors (distance from rivers, aspect, elevation, slope, rainfall, soil, land use, and lithology) were selected for the modeling, where distance to rivers is the most important factor for modeling of flash flood in this area. Performance of the models was validated and compared by using several robust metrics such as statistical measures and Area Under the Receiver Operating Characteristic (AUC) curve. The results of this study suggested that ABM-CDT (AUC = 0.957) has the best predictive capability in terms of accuracy, followed by Dag-CDT (AUC = 0.947), MBAB-CDT (AUC = 0.933), Bag-CDT (AUC = 0.932), and CDT (0.900), respectively. The proposed methods presented in this study would help in the development of accurate flash flood susceptible maps of watershed areas not only in Iran but also other parts of the world.

scholarly journals Flash Flood Susceptibility Assessment Based on Geodetector, Certainty Factor, and Logistic Regression Analyses in Fujian Province, China

2020 ◽  
Vol 9 (12) ◽  
pp. 748
Author(s):  
Yifan Cao ◽  
Hongliang Jia ◽  
Junnan Xiong ◽  
Weiming Cheng ◽  
Kun Li ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Flash Flood ◽  
Pearson Correlation ◽  
Flash Floods ◽  
Ensemble Model ◽  
Susceptibility Assessment ◽  
Certainty Factor ◽  
Fujian Province ◽  
Flood Susceptibility ◽  
Susceptibility Maps

Flash floods are one of the most frequent natural disasters in Fujian Province, China, and they seriously threaten the safety of infrastructure, natural ecosystems, and human life. Thus, recognition of possible flash flood locations and exploitation of more precise flash flood susceptibility maps are crucial to appropriate flash flood management in Fujian. Based on this objective, in this study, we developed a new method of flash flood susceptibility assessment. First, we utilized double standards, including the Pearson correlation coefficient (PCC) and Geodetector to screen the assessment indicator. Second, in order to consider the weight of each classification of indicator and the weights of the indicators simultaneously, we used the ensemble model of the certainty factor (CF) and logistic regression (LR) to establish a frame for the flash flood susceptibility assessment. Ultimately, we used this ensemble model (CF-LR), the standalone CF model, and the standalone LR model to prepare flash flood susceptibility maps for Fujian Province and compared their prediction performance. The results revealed the following. (1) Land use, topographic relief, and 24 h precipitation (H24_100) within a 100-year return period were the three main factors causing flash floods in Fujian Province. (2) The area under the curve (AUC) results showed that the CF-LR model had the best precision in terms of both the success rate (0.860) and the prediction rate (0.882). (3) The assessment results of all three models showed that between 22.27% and 29.35% of the study area have high and very high susceptibility levels, and these areas are mainly located in the east, south, and southeast coastal areas, and the north and west low mountain areas. The results of this study provide a scientific basis and support for flash flood prevention in Fujian Province. The proposed susceptibility assessment framework may also be helpful for other natural disaster susceptibility analyses.

scholarly journals Computational Method for Classification of Avian Influenza A Virus Using DNA Sequence Information and Physicochemical Properties

2021 ◽  
Vol 12 ◽  
Author(s):  
Fahad Humayun ◽  
Fatima Khan ◽  
Nasim Fawad ◽  
Shazia Shamas ◽  
Sahar Fazal ◽  
...  
Keyword(s):  
Feature Selection ◽  
Decision Tree ◽  
Avian Influenza ◽  
Physicochemical Properties ◽  
Influenza A Virus ◽  
Dna Sequence ◽  
Influenza A ◽  
Feature Selection Method ◽  
Selection Method ◽  
Avian Influenza A Virus

Accurate and fast characterization of the subtype sequences of Avian influenza A virus (AIAV) hemagglutinin (HA) and neuraminidase (NA) depends on expanding diagnostic services and is embedded in molecular epidemiological studies. A new approach for classifying the AIAV sequences of the HA and NA genes into subtypes using DNA sequence data and physicochemical properties is proposed. This method simply requires unaligned, full-length, or partial sequences of HA or NA DNA as input. It allows for quick and highly accurate assignments of HA sequences to subtypes H1–H16 and NA sequences to subtypes N1–N9. For feature extraction, k-gram, discrete wavelet transformation, and multivariate mutual information were used, and different classifiers were trained for prediction. Four different classifiers, Naïve Bayes, Support Vector Machine (SVM), K nearest neighbor (KNN), and Decision Tree, were compared using our feature selection method. This comparison is based on the 30% dataset separated from the original dataset for testing purposes. Among the four classifiers, Decision Tree was the best, and Precision, Recall, F1 score, and Accuracy were 0.9514, 0.9535, 0.9524, and 0.9571, respectively. Decision Tree had considerable improvements over the other three classifiers using our method. Results show that the proposed feature selection method, when trained with a Decision Tree classifier, gives the best results for accurate prediction of the AIAV subtype.

scholarly journals GIS Based Novel Hybrid Computational Intelligence Models for Mapping Landslide Susceptibility: A Case Study at Da Lat City, Vietnam

2019 ◽  
Vol 11 (24) ◽  
pp. 7118 ◽  
Author(s):  
Viet-Tien Nguyen ◽  
Trong Hien Tran ◽  
Ngoc Anh Ha ◽  
Van Liem Ngo ◽  
Al-Ansari Nadhir ◽  
...  
Keyword(s):  
Land Use ◽  
Risk Management ◽  
Land Use Planning ◽  
Computational Intelligence ◽  
Landslide Susceptibility ◽  
Vegetation Index ◽  
Statistical Measures ◽  
Susceptibility Maps ◽  
Intelligence Models ◽  
Landslide Susceptibility Maps

Landslides affect properties and the lives of a large number of people in many hilly parts of Vietnam and in the world. Damages caused by landslides can be reduced by understanding distribution, nature, mechanisms and causes of landslides with the help of model studies for better planning and risk management of the area. Development of landslide susceptibility maps is one of the main steps in landslide management. In this study, the main objective is to develop GIS based hybrid computational intelligence models to generate landslide susceptibility maps of the Da Lat province, which is one of the landslide prone regions of Vietnam. Novel hybrid models of alternating decision trees (ADT) with various ensemble methods, namely bagging, dagging, MultiBoostAB, and RealAdaBoost, were developed namely B-ADT, D-ADT, MBAB-ADT, RAB-ADT, respectively. Data of 72 past landslide events was used in conjunction with 11 landslide conditioning factors (curvature, distance from geological boundaries, elevation, land use, Normalized Difference Vegetation Index (NDVI), relief amplitude, stream density, slope, lithology, weathering crust and soil) in the development and validation of the models. Area under the receiver operating characteristic (ROC) curve (AUC), and several statistical measures were applied to validate these models. Results indicated that performance of all the models was good (AUC value greater than 0.8) but B-ADT model performed the best (AUC= 0.856). Landslide susceptibility maps generated using the proposed models would be helpful to decision makers in the risk management for land use planning and infrastructure development.

scholarly journals Novel Ensembles of Deep Learning Neural Network and Statistical Learning for Flash-Flood Susceptibility Mapping

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1549 ◽  
Author(s):  
Romulus Costache ◽  
Phuong Thao Thi Ngo ◽  
Dieu Tien Bui
Keyword(s):  
Neural Network ◽  
Operating Characteristic ◽  
Flash Flood ◽  
Flood Susceptibility ◽  
Statistical Measures ◽  
Geospatial Database ◽  
Machine Leaning ◽  
Flood Susceptibility Mapping ◽  
Deep Learning Neural Network ◽  
Hierarchy Process

This study aimed to assess flash-flood susceptibility using a new hybridization approach of Deep Neural Network (DNN), Analytical Hierarchy Process (AHP), and Frequency Ratio (FR). A catchment area in south-eastern Romania was selected for this proposed approach. In this regard, a geospatial database of the flood with 178 flood locations and with 10 flash-flood predictors was prepared and used for this proposed approach. AHP and FR were used for processing and coding the predictors into a numeric format, whereas DNN, which is a powerful and state-of-the-art probabilistic machine leaning, was employed to build an inference flash-flood model. The reliability of the models was verified with the help of Receiver Operating Characteristic (ROC) Curve, Area Under Curve (AUC), and several statistical measures. The result shows that the two proposed ensemble models, DNN-AHP and DNN-FR, are capable of predicting future flash-flood areas with accuracy higher than 92%; therefore, they are a new tool for flash-flood studies.

Metamorphic Malware Detection Using Minimal Opcode Statistical Patterns

2018 ◽  
pp. 337-359
Author(s):  
Mahmood Fazlali ◽  
Peyman Khodamoradi
Keyword(s):  
Feature Selection ◽  
Decision Tree ◽  
High Speed ◽  
Detection Method ◽  
Malware Detection ◽  
Feature Selection Method ◽  
Selection Method ◽  
Input File ◽  
Feature Vectors ◽  
Metamorphic Malware

High-speed and accurate malware detection for metamorphic malware are two goals in antiviruses. To reach beyond this issue, this chapter presents a new malware detection method that can be summarized as follows: (1) Input file is disassembled and classified to obtain the minimal opcode pattern as feature vectors; (2) a forward feature selection method (i.e., maximum relevancy and minimum redundancy) is applied to remove the redundant as well as irrelevant features; and (3) the process ends by classification through using decision tree. The results indicate the proposed method can effectively detect metamorphic malware in terms of speed, efficiency, and accuracy.

scholarly journals EEG feature selection method based on decision tree

2015 ◽  
Vol 26 (s1) ◽  
pp. S1019-S1025 ◽  
Author(s):  
Lijuan Duan ◽  
Hui Ge ◽  
Wei Ma ◽  
Jun Miao
Keyword(s):  
Feature Selection ◽  
Decision Tree ◽  
Feature Selection Method ◽  
Selection Method

scholarly journals Flash Flood Susceptibility Mapping at Andungbiru Watershed, East Java Using AHP-Information Weighted Method

2021 ◽  
Vol 6 (2) ◽  
pp. 127
Author(s):  
Devi Ratna Handini ◽  
Entin Hidayah ◽  
Gusfan Halik
Keyword(s):  
Land Use ◽  
Vegetation Index ◽  
Flash Flood ◽  
Characteristic Curve ◽  
Flash Floods ◽  
Assessment Method ◽  
Economic Losses ◽  
Landsat 8 ◽  
Gis Technology ◽  
Topographic Wetness Index

Flash floods are among the most frequent natural disasters caused by heavy rain associated with a severe thunderstorm, which leads to social and economic losses in infrastructure and agriculture. Therefore, this research aims to map flash flood potential susceptibility (FFPS) in the Pekalen watershed, using Geographic Information System (GIS) technology and statistical analysis to reduce the risk of flooding. The opinion and experience of an expert on the weight assessment method were carried out using the Analytical Hierarchy Process (AHP). Furthermore, the probability statistical methods and GIS were used in flash flood areas in the Pekalen watershed in Andungbiru, Probolinggo village. This study was carried out using geomorphological factors, namely elevation, slope, stream power index, and topographic wetness index, with a resolution of 30 m. Thematic map scale of the land use, river density, distance to the river, rainfall, and geology is in the ratio of is in a ratio of 1:25.000. Imagery processing was carried out using Landsat 8 30 m x 30 m resolution imagery, such as the Normalized Difference Vegetation Index. The result showed that the model map of FFPS obtained low 8%, low 23%, moderate 27%, moderate to high 26%, high 13%, and very high 2% index values. The next stage of modeling analysis led to validation using statistic receiver operating Characteristic Curve (ROC) of area Under Curve (AUC) with a value of 90.15. In conclusion, the factors that significantly trigger flash floods are distance to the river, land use, and slope.   Keywords: AHP-weighted; information content; FFSP; GIS; Geomorphology Copyright (c) 2021 Geosfera Indonesia and Department of Geography Education, University of Jember   This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License

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