scholarly journals Flash-Flood Susceptibility Assessment Using Multi-Criteria Decision Making and Machine Learning Supported by Remote Sensing and GIS Techniques

2019 ◽  
Vol 12 (1) ◽  
pp. 106 ◽  
Author(s):  
Romulus Costache ◽  
Quoc Bao Pham ◽  
Ehsan Sharifi ◽  
Nguyen Thi Thuy Linh ◽  
S.I. Abba ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Information Gain ◽  
Flash Flood ◽  
Slope Angle ◽  
Training Data ◽  
Operating Characteristics ◽  
Predictive Capability ◽  
Data Set ◽  
Flood Susceptibility ◽  
Remote Sensing Techniques

Concerning the significant increase in the negative effects of flash-floods worldwide, the main goal of this research is to evaluate the power of the Analytical Hierarchy Process (AHP), fi (kNN), K-Star (KS) algorithms and their ensembles in flash-flood susceptibility mapping. To train the two stand-alone models and their ensembles, for the first stage, the areas affected in the past by torrential phenomena are identified using remote sensing techniques. Approximately 70% of these areas are used as a training data set along with 10 flash-flood predictors. It should be remarked that the remote sensing techniques play a crucial role in obtaining eight out of 10 flash-flood conditioning factors. The predictive capability of predictors is evaluated through the Information Gain Ratio (IGR) method. As expected, the slope angle results in the factor with the highest predictive capability. The application of the AHP model implies the construction of ten pair-wise comparison matrices for calculating the normalized weights of each flash-flood predictor. The computed weights are used as input data in kNN–AHP and KS–AHP ensemble models for calculating the Flash-Flood Potential Index (FFPI). The FFPI also is determined through kNN and KS stand-alone models. The performance of the models is evaluated using statistical metrics (i.e., sensitivity, specificity and accuracy) while the validation of the results is done by constructing the Receiver Operating Characteristics (ROC) Curve and Area Under Curve (AUC) values and by calculating the density of torrential pixels within FFPI classes. Overall, the best performance is obtained by the kNN–AHP ensemble model.

scholarly journals Flood Spatial Modeling in Northern Iran Using Remote Sensing and GIS: A Comparison between Evidential Belief Functions and Its Ensemble with a Multivariate Logistic Regression Model

2019 ◽  
Vol 11 (13) ◽  
pp. 1589 ◽  
Author(s):  
Duie Tien Bui ◽  
Khabat Khosravi ◽  
Himan Shahabi ◽  
Prasad Daggupati ◽  
Jan F. Adamowski ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Vegetation Index ◽  
Slope Angle ◽  
Training Data ◽  
Multivariate Logistic Regression Model ◽  
Success Rates ◽  
Topographic Wetness Index ◽  
Validation Data ◽  
Flood Susceptibility ◽  
Susceptibility Maps

Floods are some of the most dangerous and most frequent natural disasters occurring in the northern region of Iran. Flooding in this area frequently leads to major urban, financial, anthropogenic, and environmental impacts. Therefore, the development of flood susceptibility maps used to identify flood zones in the catchment is necessary for improved flood management and decision making. The main objective of this study was to evaluate the performance of an Evidential Belief Function (EBF) model, both as an individual model and in combination with Logistic Regression (LR) methods, in preparing flood susceptibility maps for the Haraz Catchment in the Mazandaran Province, Iran. The spatial database created consisted of a flood inventory, altitude, slope angle, plan curvature, Topographic Wetness Index (TWI), Stream Power Index (SPI), distance from river, rainfall, geology, land use, and Normalized Difference Vegetation Index (NDVI) for the region. After obtaining the required information from various sources, 151 of 211 recorded flooding points were used for model training and preparation of the flood susceptibility maps. For validation, the results of the models were compared to the 60 remaining flooding points. The Receiver Operating Characteristic (ROC) curve was drawn, and the Area Under the Curve (AUC) was calculated to obtain the accuracy of the flood susceptibility maps prepared through success rates (using training data) and prediction rates (using validation data). The AUC results indicated that the EBF, EBF from LR, EBF-LR (enter), and EBF-LR (stepwise) success rates were 94.61%, 67.94%, 86.45%, and 56.31%, respectively, and the prediction rates were 94.55%, 66.41%, 83.19%, and 52.98%, respectively. The results showed that the EBF model had the highest accuracy in predicting flood susceptibility within the catchment, in which 15% of the total areas were located in high and very high susceptibility classes, and 62% were located in low and very low susceptibility classes. These results can be used for the planning and management of areas vulnerable to floods in order to prevent flood-induced damage; the results may also be useful for natural disaster assessment.

Predicting the outcrop of pre-Quaternary formations in the Dorog Basin (Hungary) using random forest classification

2020 ◽  
Author(s):  
Reka Pogacsas ◽  
Gaspar Albert
Keyword(s):  
Remote Sensing ◽  
Random Forest ◽  
Slope Angle ◽  
Morphological Characteristics ◽  
Training Data ◽  
Topographic Wetness Index ◽  
Unique Region ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Geological Map

<p>The Dorog Basin is a morphologically unique region of the Transdanubian Mountains revealing the combined work of tectonic forces and erosion. Overprinted by the forms of fluvial erosion, numerous NW-SE striking half-graben and horst structures are present. The surface is dominantly covered by lose 1–15 m thick Quaternary sediments (aeolian loess, and siliciclastic alluvial and coluvial formations), while the lithified bedrock consists of Mesozoic carbonates, Paleogene limestones, marls and sandstones and limnic coal sequences. The rheological difference of the Quaternary and pre-Quaternary formations is so pronounced that the morphological characteristics of the outcrops also differ significantly. The area was in the focus of geologists for many decades, due to its Eocene coal beds, and a renewal of the geological map of the region is in progress. The current research aims to assist the mapping with multivariate methods based on geomorphological attributes, such as slope angle, aspect, profile curvature, height, and topographic wetness index. We perform a random forest classification (RFC) using these variables, to predict the outcrops of pre-Quaternary formations in the study area.</p><p>Random forest is a powerful tool for multivariate classification that uses several decision trees, each one with a prediction, where the most popular one will be the overall result [1]. The reason why it is getting popular in spatial predictions is the high accuracy to classify raster-type objects [2]. We used raster-type spatial data as subject of RFC predicting a result for each pixel. The geology of the study area was known from previous geological mapping [3]. Morphological information was derived from the MERIT DEM.</p><p>Our model used a raster with multiple bands containing geomorphological variables, and training data from the digitalized geological map. The number of random samples of data was 2500. After testing several combinations of the bands, and several spacing of the study areas, the best prediction has cca. 80% accuracy. Model validation is based on the calculation of rates of well predicted pixels in the same rasterized geological map that was used for training. Our aim was to use exact data, which is completely true for remotely sensed images, but not for geological maps. That means the accuracy still can be improved by field perception, or from borehole data.</p><p> </p><p>References:</p><p>[1] Liaw, A., & Wiener, M. (2002). Classification and regression by randomForest. R news, 2(3), 18-22.</p><p>[2] Belgiu, M., & Drăguţ, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24-31.</p><p>[3] Gidai, L., Nagy, G., & Siposs, Z. (1981). Geological map of the Dorog Basin 1: 25 000. [in Hungarian] Geological Institute of Hungary, Budapest.</p>

Modelling current parna distribution in a local area

Soil Research ◽  
2000 ◽  
Vol 38 (4) ◽  
pp. 867 ◽  
Author(s):  
G. K. Summerell ◽  
T. I. Dowling ◽  
D. P. Richardson ◽  
J. Walker ◽  
B. Lees
Keyword(s):  
Slope Angle ◽  
Training Data ◽  
Leeward Side ◽  
Continuous Variables ◽  
Learning Program ◽  
Training Set ◽  
Data Set ◽  
Landscape Variables ◽  
Eastern Australia ◽  
Central Ridge

Parna is a wind-blown clay, mobilised from inland Australia as the result of a series of intermittent high wind events during the Quaternary. Parna can be recognised on the basis of colour, texture, distributional patterns, and pedology. Parna deposits have been recorded across a wide area of south eastern Australia and have influenced the local pedology and hydrology. In some cases parna has increased soil sodicity and the potential for dryland salinisation. Predicting its spatial distribution is useful when considering agricultural potential and in assessing the risk and spatial spread of dryland salinity. Here we present the results of modelling to predict its local distribution in an area covering 291 km2 in the Young district of NSW. Two conceptual models of parna deposition and subsequent redistribution were used to develop a current parna distribution map: (a) deposition = f(topography, aspect) after assuming that interactions of rainfall, vegetation, and wind speed were relatively the same at the local scale; (b) removal or retention = f (slope angle, catchment size, slope length) as a representation of the erosive energy of gravity. Five landscape variables, elevation, aspect, slope, flow accumulation, and flow length, were derived from a 20 m digital elevation model (DEM). A training set of parna deposits was established using air photos and field survey from limited exposures in the Young district of NSW. These areas were digitised and converted to a grid of areas of parna and no-parna. This training set for parna and the 5 landscape variable grids were processed in the IDRISI for WINDOWS Geographic Information System (GIS). Spatial relationships between the parna and no-parna deposits and the 5 landscape variables were extracted from this training set. This information was imported into an inductive learning program called KnowledgeSEEKER. A decision tree was built by recursive partitioning of the data set using Chi-squares to categorise variables, and an F test for continuous variables to best replicate the training data classification of ‘parna’ and ‘no-parna’. The rules derived from this process were applied to the study area to predict the occurrence of parna in the broader landscape. Predictions were field checked and the rules adjusted until they best represented the occurrence of parna in the field. The final model showed predictions of parna deposits as follows: (i) higher elevations in the Young landscape were the dominant sites of parna deposits; (ii) thicker deposits of parna occurred on the windward south-west and north-west; (iii) thinner deposits occurred on the leeward side of a central ridge feature; (iv) because the training set concentrated around the major central ridge feature, poorer predictions were obtained on gently undulating country.

An Entropy-Based Index Evaluation Scheme for Multiple Sensor Fusion in Classification Process

1999 ◽  
Vol 121 (4) ◽  
pp. 727-732 ◽  
Author(s):  
Y. Chen ◽  
E. Orady
Keyword(s):  
Decision Making ◽  
Sensor Fusion ◽  
Information Gain ◽  
Training Data ◽  
Data Set ◽  
Multiple Sensors ◽  
Evaluation Scheme ◽  
Index Evaluation ◽  
Process System ◽  
Using Data

Sensor fusion aims to identify useful information to facilitate decision-making using data from multiple sensors. Signals from each sensor are usually processed, through feature extraction, into different indices by which knowledge can be better represented. However, cautions should be placed in decision-making when multiple indices are used, since each index may carry different information or different aspects of the knowledge for the process/system under study. To this end, a practical scheme for index evaluation based on entropy and information gain is presented. This procedure is useful when index ranking is needed in designing a classifier for a complex system or process. Both regional entropy and class entropy are introduced based on a set of training data. Application of this scheme is illustrated by using a data set for a tapping process.

scholarly journals Development of a Flash Flood Confidence Index from Disaster Reports and Geophysical Susceptibility

2021 ◽  
Vol 13 (14) ◽  
pp. 2764
Author(s):  
Andrew Kruczkiewicz ◽  
Agathe Bucherie ◽  
Fernanda Ayala ◽  
Carolynne Hultquist ◽  
Humberto Vergara ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Disaster Risk Reduction ◽  
Flash Flood ◽  
Disaster Risk ◽  
Geophysical Data ◽  
Risk Levels ◽  
Flood Susceptibility ◽  
Confidence Index ◽  
Current Risk ◽  
Over Time

The analysis of historical disaster events is a critical step towards understanding current risk levels and changes in disaster risk over time. Disaster databases are potentially useful tools for exploring trends, however, criteria for inclusion of events and for associated descriptive characteristics is not standardized. For example, some databases include only primary disaster types, such as ‘flood’, while others include subtypes, such as ‘coastal flood’ and ‘flash flood’. Here we outline a method to identify candidate events for assignment of a specific disaster subtype—namely, ‘flash floods’—from the corresponding primary disaster type—namely, ‘flood’. Geophysical data, including variables derived from remote sensing, are integrated to develop an enhanced flash flood confidence index, consisting of both a flash flood confidence index based on text mining of disaster reports and a flash flood susceptibility index from remote sensing derived geophysical data. This method was applied to a historical flood event dataset covering Ecuador. Results indicate the potential value of disaggregating events labeled as a primary disaster type into events of a particular subtype. The outputs are potentially useful for disaster risk reduction and vulnerability assessment if appropriately evaluated for fitness of use.

Diagnosis Model of Hydrogen Sulfide Poisoning Based on Support Vector Machine

2020 ◽  
Vol 16 ◽  
Author(s):  
Yifan Ying ◽  
Yongxi Jin ◽  
Xianchuan Wang ◽  
Jianshe Ma ◽  
Min Zeng ◽  
...  
Keyword(s):  
Hydrogen Sulfide ◽  
Evaluation Method ◽  
Information Gain ◽  
Training Data ◽  
Support Vector ◽  
Control Group ◽  
Diagnostic Model ◽  
Data Set ◽  
Training Time ◽  
Svm Classification

Introduction: Hydrogen sulfide (H2S) is a lethal environmental and industrial poison. The mortality rate of occupational acute H2S poisoning reported in China is 23.1% ~ 50%. Due to the huge amount of information on metabolomics changes after body poisoning, it is important to use intelligent algorithms to mine multivariate interactions. Methods: This paper first uses GC-MS metabolomics to detect changes in the urine components of the poisoned group and control rats to form a metabolic data set, and then uses the SVM classification algorithm in machine learning to train the hydrogen sulfide poisoning training data set to obtain a classification recognition model. A batch of rats (n = 15) was randomly selected and exposed to 20 ppm H2S gas for 40 days (twice morning and evening, 1 hour each exposure) to prepare a chronic H2S rat poisoning model. The other rats (n = 15) were exposed to the same volume of air and 0 ppm hydrogen sulfide gas as the control group. The treated urine samples were tested using a GC-MS. Results: The method locates the optimal parameters of SVM, which improves the accuracy of SVM classification to 100%. This paper uses the information gain attribute evaluation method to screen out the top 6 biomarkers that contribute to the predicted category (Glycerol，β-Hydroxybutyric acid， arabinofuranose，Pentitol，L-Tyrosine，L-Proline). Conclusion: The SVM diagnostic model of hydrogen sulfide poisoning constructed in this work has training time and prediction accuracy; it has achieved excellent results and provided an intelligent decision-making method for the diagnosis of hydrogen sulfide poisoning.

The Potential of Remote Sensing to Assess Conditioning Factors for Landslide Detection at a Regional Scale: The Case in Southeastern Colombia

2020 ◽  
Author(s):  
Nixon Alexander Correa-Muñoz ◽  
Carol Andrea Murillo-Feo
Keyword(s):  
Remote Sensing ◽  
Regional Scale ◽  
Training Data ◽  
Sar Interferometry ◽  
Optical Remote Sensing ◽  
High Association ◽  
Conditioning Factors ◽  
Explanatory Variables ◽  
Detection Model ◽  
Remote Sensing Techniques

This landslide detection research applied remote sensing techniques. Morphometry to derive both DEM terrain parameters and land use variables. SAR interferometry (InSAR) for showing that InSAR coherence and InSAR displacement obtained with SRTM DEM 30 m resolution were strongly related to landslides. InSAR coherence values from 0.43 to 0.66 had a high association with landslides. PS-InSAR allowed to estimate terrain velocities in the satellite line-of-sight (LOS) in the range − 10 to 10 mm/year concerning extremely slow landslide displacement rates. SAR polarimetry (PolSAR) was used over L-band UAVSAR quad-pol data, obtaining the scattering mechanism of volume and surface retrodispersion more associated with landslides. The optical remote sensing with a multitemporal approach for change detection by multi-year Landsat (5, 7 and 8)-NDVI, showed that NDVI related to landslides had values between 0.42 and 0.72. All the information was combined into a multidimensional grid product and crossed with training data containing a Colombian Geologic Service (CGS) landslide inventory. A detection model was implemented using the Random Forest supervised method relating the training sample of landslides with multidimensional explanatory variables. A test sample with a proportion of 70:30 allowed to find the accuracy of detection of about 70.8% for slides type.

scholarly journals Unsupervised Haze Removal for High-Resolution Optical Remote-Sensing Images Based on Improved Generative Adversarial Networks

2020 ◽  
Vol 12 (24) ◽  
pp. 4162
Author(s):  
Anna Hu ◽  
Zhong Xie ◽  
Yongyang Xu ◽  
Mingyu Xie ◽  
Liang Wu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Data Set ◽  
Adversarial Networks ◽  
Ground Object ◽  
Edge Sharpening

One major limitation of remote-sensing images is bad weather conditions, such as haze. Haze significantly reduces the accuracy of satellite image interpretation. To solve this problem, this paper proposes a novel unsupervised method to remove haze from high-resolution optical remote-sensing images. The proposed method, based on cycle generative adversarial networks, is called the edge-sharpening cycle-consistent adversarial network (ES-CCGAN). Most importantly, unlike existing methods, this approach does not require prior information; the training data are unsupervised, which mitigates the pressure of preparing the training data set. To enhance the ability to extract ground-object information, the generative network replaces a residual neural network (ResNet) with a dense convolutional network (DenseNet). The edge-sharpening loss function of the deep-learning model is designed to recover clear ground-object edges and obtain more detailed information from hazy images. In the high-frequency information extraction model, this study re-trained the Visual Geometry Group (VGG) network using remote-sensing images. Experimental results reveal that the proposed method can recover different kinds of scenes from hazy images successfully and obtain excellent color consistency. Moreover, the ability of the proposed method to obtain clear edges and rich texture feature information makes it superior to the existing methods.

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