scholarly journals Implementation of Artificial Intelligence Based Ensemble Models for Gully Erosion Susceptibility Assessment

2020 ◽  
Vol 12 (21) ◽  
pp. 3620
Author(s):  
Indrajit Chowdhuri ◽  
Subodh Chandra Pal ◽  
Alireza Arabameri ◽  
Asish Saha ◽  
Rabin Chakrabortty ◽  
...  
Keyword(s):  
Land Use Changes ◽  
Area Under The Curve ◽  
Regression Tree ◽  
Gully Erosion ◽  
Support Vector ◽  
Boosted Regression Tree ◽  
Conditioning Factors ◽  
Susceptibility Maps ◽  
Chotanagpur Plateau ◽  
Additive Regression

The Rarh Bengal region in West Bengal, particularly the eastern fringe area of the Chotanagpur plateau, is highly prone to water-induced gully erosion. In this study, we analyzed the spatial patterns of a potential gully erosion in the Gandheswari watershed. This area is highly affected by monsoon rainfall and ongoing land-use changes. This combination causes intensive gully erosion and land degradation. Therefore, we developed gully erosion susceptibility maps (GESMs) using the machine learning (ML) algorithms boosted regression tree (BRT), Bayesian additive regression tree (BART), support vector regression (SVR), and the ensemble of the SVR-Bee algorithm. The gully erosion inventory maps are based on a total of 178 gully head-cutting points, taken as the dependent factor, and gully erosion conditioning factors, which serve as the independent factors. We validated the ML model results using the area under the curve (AUC), accuracy (ACC), true skill statistic (TSS), and Kappa coefficient index. The AUC result of the BRT, BART, SVR, and SVR-Bee models are 0.895, 0.902, 0.927, and 0.960, respectively, which show very good GESM accuracies. The ensemble model provides more accurate prediction results than any single ML model used in this study.

scholarly journals Spatial Modelling of Gully Erosion Using GIS and R Programing: A Comparison among Three Data Mining Algorithms

2018 ◽  
Vol 8 (8) ◽  
pp. 1369 ◽  
Author(s):  
Alireza Arabameri ◽  
Biswajeet Pradhan ◽  
Hamid Reza Pourghasemi ◽  
Khalil Rezaei ◽  
Norman Kerle
Keyword(s):  
Data Mining ◽  
Spatial Relationship ◽  
Area Under The Curve ◽  
Regression Tree ◽  
Drainage Density ◽  
Gully Erosion ◽  
Slope Aspect ◽  
Topographic Wetness Index ◽  
Boosted Regression Tree ◽  
Area Index

Gully erosion triggers land degradation and restricts the use of land. This study assesses the spatial relationship between gully erosion (GE) and geo-environmental variables (GEVs) using Weights-of-Evidence (WoE) Bayes theory, and then applies three data mining methods—Random Forest (RF), boosted regression tree (BRT), and multivariate adaptive regression spline (MARS)—for gully erosion susceptibility mapping (GESM) in the Shahroud watershed, Iran. Gully locations were identified by extensive field surveys, and a total of 172 GE locations were mapped. Twelve gully-related GEVs: Elevation, slope degree, slope aspect, plan curvature, convergence index, topographic wetness index (TWI), lithology, land use/land cover (LU/LC), distance from rivers, distance from roads, drainage density, and NDVI were selected to model GE. The results of variables importance by RF and BRT models indicated that distance from road, elevation, and lithology had the highest effect on GE occurrence. The area under the curve (AUC) and seed cell area index (SCAI) methods were used to validate the three GE maps. The results showed that AUC for the three models varies from 0.911 to 0.927, whereas the RF model had a prediction accuracy of 0.927 as per SCAI values, when compared to the other models. The findings will be of help for planning and developing the studied region.

scholarly journals A Novel Ensemble Artificial Intelligence Approach for Gully Erosion Mapping in a Semi-Arid Watershed (Iran)

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2444 ◽  
Author(s):  
Dieu Tien Bui ◽  
Ataollah Shirzadi ◽  
Himan Shahabi ◽  
Kamran Chapi ◽  
Ebrahim Omidavr ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Slope Angle ◽  
Area Under The Curve ◽  
Kernel Functions ◽  
Gully Erosion ◽  
Support Vector ◽  
Base Function ◽  
Conditioning Factors ◽  
Intelligence Approach ◽  
Artificial Intelligence Approach

In this study, we introduced a novel hybrid artificial intelligence approach of rotation forest (RF) as a Meta/ensemble classifier based on alternating decision tree (ADTree) as a base classifier called RF-ADTree in order to spatially predict gully erosion at Klocheh watershed of Kurdistan province, Iran. A total of 915 gully erosion locations along with 22 gully conditioning factors were used to construct a database. Some soft computing benchmark models (SCBM) including the ADTree, the Support Vector Machine by two kernel functions such as Polynomial and Radial Base Function (SVM-Polynomial and SVM-RBF), the Logistic Regression (LR), and the Naïve Bayes Multinomial Updatable (NBMU) models were used for comparison of the designed model. Results indicated that 19 conditioning factors were effective among which distance to river, geomorphology, land use, hydrological group, lithology and slope angle were the most remarkable factors for gully modeling process. Additionally, results of modeling concluded the RF-ADTree ensemble model could significantly improve (area under the curve (AUC) = 0.906) the prediction accuracy of the ADTree model (AUC = 0.882). The new proposed model had also the highest performance (AUC = 0.913) in comparison to the SVM-Polynomial model (AUC = 0.879), the SVM-RBF model (AUC = 0.867), the LR model (AUC = 0.75), the ADTree model (AUC = 0.861) and the NBMU model (AUC = 0.811).

scholarly journals A Comparative Assessment of Random Forest and k-Nearest Neighbor Classifiers for Gully Erosion Susceptibility Mapping

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 2076 ◽  
Author(s):  
Avand ◽  
Janizadeh ◽  
Naghibi ◽  
Pourghasemi ◽  
Khosrobeigi Bozchaloei ◽  
...  
Keyword(s):  
Random Forest ◽  
Nearest Neighbor ◽  
Area Under The Curve ◽  
Gully Erosion ◽  
K Nearest Neighbor ◽  
K Nearest Neighbors ◽  
Factors Affecting ◽  
Conditioning Factors ◽  
Susceptibility Maps ◽  
Nearest Neighbor Classifiers

This research was conducted to determine which areas in the Robat Turk watershed in Iran are sensitive to gully erosion, and to define the relationship between gully erosion and geo-environmental factors by two data mining techniques, namely, Random Forest (RF) and k-Nearest Neighbors (KNN). First, 242 gully locations we determined in field surveys and mapped in ArcGIS software. Then, twelve gully-related conditioning factors were selected. Our results showed that, for both the RF and KNN models, altitude, distance to roads, and distance from the river had the highest influence upon gully erosion sensitivity. We assessed the gully erosion susceptibility maps using the Receiver Operating Characteristic (ROC) curve. Validation results showed that the RF and KNN models had Area Under the Curve (AUC) 87.4 and 80.9%, respectively. As a result, the RF method has better performance compared with the KNN method for mapping gully erosion susceptibility. Rainfall, altitude, and distance from a river were identified as the most important factors affecting gully erosion in this area. The methodology used in this research is transferable to other regions to determine which areas are prone to gully erosion and to explicitly delineate high-risk zones within these areas.

scholarly journals An Improved Long Short-Term Memory Model for Dam Displacement Prediction

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Jun Zhang ◽  
Xiyao Cao ◽  
Jiemin Xie ◽  
Pangao Kou
Keyword(s):  
Short Term Memory ◽  
Water Pressure ◽  
Extreme Temperature ◽  
Regression Tree ◽  
Support Vector ◽  
Temperature Structure ◽  
Short Term ◽  
Boosted Regression Tree ◽  
Term Memory ◽  
Long Short Term Memory

Displacement plays a vital role in dam safety monitoring data, which adequately responds to security risks such as the flood water pressure, extreme temperature, structure deterioration, and bottom bedrock damage. To make accurate predictions, former researchers established various models. However, these models’ input variables cannot efficiently reflect the delays between the external environment and displacement. Therefore, a long short-term memory (LSTM) model is proposed to make full use of the historical data to reflect the delays. Furthermore, the LSTM model is improved to optimize the performance by making variables more physically reasonable. Finally, a real-world radial displacement dataset is used to compare the performance of LSTM models, multiple linear regression (MLR), multilayer perceptron (MLP) neural networks, support vector machine (SVM), and boosted regression tree (BRT). The results indicate that (1) the LSTM models can efficiently reflect the delays and make the variables selection more convenient and (2) the improved LSTM model achieves the best performance by optimizing the input form and network structure based on a clearer physical meaning.

scholarly journals Assessment of gully erosion on the territory of Tomsk using GIS technologies

2021 ◽  
Vol 1 ◽  
pp. 74-86
Author(s):  
Anna Vladimirovna LEONOVA ◽  
◽  
Lyudmila Aleksandrovna STROKOVA ◽  
Keyword(s):  
Gully Erosion ◽  
Slope Aspect ◽  
Multidimensional Data ◽  
Left Bank ◽  
City Management ◽  
High Quality ◽  
Conditioning Factors ◽  
Gis Technologies ◽  
Susceptibility Maps ◽  
The City

Relevance and purpose of the work. Currently, the area of development of Tomsk is increasing. New neighborhoods are growing on previously undeveloped land (for example, on the left bank of the river Tom). The central part of the city is being redeveloped and reconstructed. It is impossible to develop a high-quality territory without taking into account the dynamics, mechanisms, factors and patterns of development of dangerous natural and technological processes, the forecast of their development. The purpose of the work is to establish the patterns of gully erosion, assess the intensity of its development, and predict the probability of its occurrence within the new city boundaries. Methods of research. We performed an assessment and forecast of the development of gully erosion in Tomsk using GIS technologies, which are an important tool in the city management process due to their ability to process and analyze multidimensional data about the geological environment. We compared the traditional model of data-driven frequency ratio (FR) and expert-based multi-criteria assessment, i.e. analytical hierarchical process by weighting of gulley conditioning factors. Results of the work. We constructed a map of the distribution of gullies on the territory of the city, including 23 polygons. These polygons were then randomly divided into training (16 polygons or 70%) and validation data (7 polygons or 30%). We used seven gulley-conditioning factors for the two models to produce gulley susceptibility maps: slope angle, slope aspect, curvature, elevation, geological structure of the territory; types of filtration sections; distance to the river, to analyze the spatial patterns that determine the development of gully erosion. The spatial correlation between gulley locations and the conditioning factors were identified using GIS-based statistical models. We constructed gulley susceptibility maps based on the ranking of each factor by two methods using a training data set. Receiver operating characteristics (ROC) were used to validate the resulting susceptibility maps. The area under the curve (AUC) was 0.905 for the AHP model and 0,800 for the FR model, respectively, which indicates excellent and high quality of forecast maps. We proved that both methods are beneficial for assessment the susceptibility of the territory to gully erosion. We recommend using the constructed maps for regional planning and hazard mitigation, as well as in education by teaching the discipline “Engineering geodynamics”.

Regression analysis for detecting epileptic seizure with different feature extracting strategies

2019 ◽  
Vol 64 (6) ◽  
pp. 619-642 ◽  
Author(s):  
Lal Hussain ◽  
Sharjil Saeed ◽  
Adnan Idris ◽  
Imtiaz Ahmed Awan ◽  
Saeed Arif Shah ◽  
...  
Keyword(s):  
Mean Square Error ◽  
Regression Tree ◽  
Gaussian Process Regression ◽  
Function Evaluation ◽  
Support Vector ◽  
Mean Square ◽  
Boosted Regression Tree ◽  
Regression Methods ◽  
Evaluation Time ◽  
The Brain

Abstract Due to the excitability of neurons in the brain, a neurological disorder is produced known as epilepsy. The brain activity of patients suffering from epilepsy is monitored through electroencephalography (EEG). The multivariate nature of features from time domain, frequency domain, complexity and wavelet entropy based, and the statistical features were extracted from healthy and epileptic subjects using the Bonn University database and seizure and non-seizure intervals using the CHB MIT database. The robust machine learning regression methods based on regression, support vector regression (SVR), regression tree (RT), ensemble regression, Gaussian process regression (GPR) were employed for detecting and predicting epileptic seizures. Performance was measured in terms of root mean square error (RMSE), squared error, mean square error (MSE) and mean absolute error (MAE). Moreover, detailed optimization was performed using a RT to predict the selected features from each feature category. A deeper analysis was conducted on features and tree regression methods where optimal RMSE and MSE results were obtained. The best optimal performance was obtained using the ensemble boosted regression tree (BRT) and exponential GPR with an RMSE of 0.47, an MSE (0.22), an R Square (RS) (0.25) and an MAE (0.30) using the Bonn University database and support vector machine (SVM) fine Gaussian with RMSE (0.63634), RS (0.03), MSE (0.40493) and MAE (0.31744); squared exponential GPR and rational quadratic GPR with an RMSE of 0.63841, an RS (0.03), an MSE (0.40757) and an MAE (0.3472) was obtained using the CHB MIT database. A further deeper analysis for the prediction of selected features was performed on an RT to compute the optimal feasible point, observed and estimated function values, function evaluation time, objective function evaluation time and overall elapsed time.

Predicting Lipid-Lowering Medication Persistence after the First Cardiovascular Disease Hospitalization

2020 ◽  
Vol 59 (02/03) ◽  
pp. 061-074
Author(s):  
Feiyu Hu ◽  
Jim Warren ◽  
Daniel J. Exeter
Keyword(s):  
Cardiovascular Disease ◽  
Regression Tree ◽  
Predictive Performance ◽  
Lipid Lowering ◽  
Support Vector ◽  
Lipid Lowering Therapy ◽  
Proportional Hazard ◽  
Medication Persistence ◽  
Boosted Regression Tree ◽  
Support Tool

Abstract Objectives This study analyzed patient factors in medication persistence after discharge from the first hospitalization for cardiovascular disease (CVD) with the aim of predicting persistence to lipid-lowering therapy for 1 to 2 years. Methods A subcohort having a first CVD hospitalization was selected from 313,207 patients for proportional hazard model analysis. Logistic regression, support vector machine, artificial neural networks, and boosted regression tree (BRT) models were used to predict 1- and 2-year medication persistence. Results Proportional hazard modeling found significant association of persistence with age, diabetes history, complication and comorbidity level, days stayed in hospital, CVD diagnosis type, in-patient procedures, and being new to therapy. BRT had the best predictive performance with c-statistic of 0.811 (0.799–0.824) for 1-year and 0.793 (0.772–0.814) for 2-year prediction using variables potentially available shortly after discharge. Conclusion The results suggest that development of a machine learning-based clinical decision support tool to focus improvements in secondary prevention of CVD is feasible.

scholarly journals Spatial Mapping of the Groundwater Potential of the Geum River Basin Using Ensemble Models Based on Remote Sensing Images

2019 ◽  
Vol 11 (19) ◽  
pp. 2285 ◽  
Author(s):  
Jeong-Cheol Kim ◽  
Hyung-Sup Jung ◽  
Saro Lee
Keyword(s):  
Remote Sensing ◽  
Specific Capacity ◽  
Area Under The Curve ◽  
Regression Tree ◽  
Groundwater Potential ◽  
The Other ◽  
Thematic Maps ◽  
Remote Sensing Images ◽  
Boosted Regression Tree ◽  
Productivity Data

This study analyzed the Groundwater Productivity Potential (GPP) of Okcheon city, Korea, using three different models. Two of these three models are data mining models: Boosted Regression Tree (BRT) model and Random Forest (RF) model. The other model is the Logistic Regression (LR) model. The three models are based on the relationship between groundwater-productivity data (specific capacity (SPC) and transmissivity (T)) and the related hydro-geological factors from thematic maps, such as topography, lineament, geology, land cover, and etc. The thematic maps which are generated from the remote sensing images. Groundwater productivity data were collected from 86 wells locations. The resulting GPP maps were validated through area-under-the-curve (AUC) analysis using wells data that had not been used for training the model. When T was used in the BRT, RF, and LR models, the obtained GPP maps had 81.66%, 80.21%, and 85.04% accuracy, respectively, and when SPC was used, the maps had 81.53%, 78.57%, and 82.22% accuracy, respectively. The LR model, which is a statistical model, showed the highest verification accuracy, also the other two models showed high accuracies. These observations indicate that all three models can be useful for groundwater resource development.

scholarly journals A multi-hazard map-based flooding, gully erosion, forest fires, and earthquakes in Iran

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Soheila Pouyan ◽  
Hamid Reza Pourghasemi ◽  
Mojgan Bordbar ◽  
Soroor Rahmanian ◽  
John J. Clague
Keyword(s):  
Random Forest ◽  
Forest Fires ◽  
Predictive Accuracy ◽  
Earthquake Hazard ◽  
Gully Erosion ◽  
Machine Learning Techniques ◽  
Validation Dataset ◽  
Support Vector ◽  
Hazard Map ◽  
Boosted Regression Tree

AbstractWe used three state-of-the-art machine learning techniques (boosted regression tree, random forest, and support vector machine) to produce a multi-hazard (MHR) map illustrating areas susceptible to flooding, gully erosion, forest fires, and earthquakes in Kohgiluyeh and Boyer-Ahmad Province, Iran. The earthquake hazard map was derived from a probabilistic seismic hazard analysis. The mean decrease Gini (MDG) method was implemented to determine the relative importance of effective factors on the spatial occurrence of each of the four hazards. Area under the curve (AUC) plots, based on a validation dataset, were created for the maps generated using the three algorithms to compare the results. The random forest model had the highest predictive accuracy, with AUC values of 0.994, 0.982, and 0.885 for gully erosion, flooding, and forest fires, respectively. Approximately 41%, 40%, 28%, and 3% of the study area are at risk of forest fires, earthquakes, floods, and gully erosion, respectively.

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