scholarly journals GIS-Based Gully Erosion Susceptibility Mapping: A Comparison of Computational Ensemble Data Mining Models

2020 ◽  
Vol 10 (6) ◽  
pp. 2039 ◽  
Author(s):  
Viet-Ha Nhu ◽  
Saeid Janizadeh ◽  
Mohammadtaghi Avand ◽  
Wei Chen ◽  
Mohsen Farzin ◽  
...  
Keyword(s):  
Expert Knowledge ◽  
Characteristic Curve ◽  
Absolute Error ◽  
Hybrid Models ◽  
Gully Erosion ◽  
Susceptibility Mapping ◽  
Aerial Photographs ◽  
River Channels ◽  
Grazing Land ◽  
Semi Arid

Gully erosion destroys agricultural and domestic grazing land in many countries, especially those with arid and semi-arid climates and easily eroded rocks and soils. It also generates large amounts of sediment that can adversely impact downstream river channels. The main objective of this research is to accurately detect and predict areas prone to gully erosion. In this paper, we couple hybrid models of a commonly used base classifier (reduced pruning error tree, REPTree) with AdaBoost (AB), bagging (Bag), and random subspace (RS) algorithms to create gully erosion susceptibility maps for a sub-basin of the Shoor River watershed in northwestern Iran. We compare the performance of these models in terms of their ability to predict gully erosion and discuss their potential use in other arid and semi-arid areas. Our database comprises 242 gully erosion locations, which we randomly divided into training and testing sets with a ratio of 70/30. Based on expert knowledge and analysis of aerial photographs and satellite images, we selected 12 conditioning factors for gully erosion. We used multi-collinearity statistical techniques in the modeling process, and checked model performance using statistical indexes including precision, recall, F-measure, Matthew correlation coefficient (MCC), receiver operatic characteristic curve (ROC), precision–recall graph (PRC), Kappa, root mean square error (RMSE), relative absolute error (PRSE), mean absolute error (MAE), and relative absolute error (RAE). Results show that rainfall, elevation, and river density are the most important factors for gully erosion susceptibility mapping in the study area. All three hybrid models that we tested significantly enhanced and improved the predictive power of REPTree (AUC=0.800), but the RS-REPTree (AUC= 0.860) ensemble model outperformed the Bag-REPTree (AUC= 0.841) and the AB-REPTree (AUC= 0.805) models. We suggest that decision makers, planners, and environmental engineers employ the RS-REPTree hybrid model to better manage gully erosion-prone areas in Iran.

scholarly journals Proposing a Novel Predictive Technique for Gully Erosion Susceptibility Mapping in Arid and Semi-arid Regions (Iran)

2019 ◽  
Vol 11 (21) ◽  
pp. 2577 ◽  
Author(s):  
Arabameri ◽  
Cerda ◽  
Rodrigo-Comino ◽  
Pradhan ◽  
Sohrabi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Type ◽  
Characteristic Curve ◽  
Gully Erosion ◽  
Susceptibility Mapping ◽  
Training Dataset ◽  
Validation Dataset ◽  
Slope Aspect ◽  
Semi Arid ◽  
Semnan Province

Gully erosion is considered to be one of the main causes of land degradation in arid and semi-arid territories around the world. In this research, gully erosion susceptibility mapping was carried out in Semnan province (Iran) as a case study in which we tested the efficiency of the index of entropy (IoE), the Vlse Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method, and their combination. Remote sensing and geographic information system (GIS) were used to reduce the time and costs needed for rapid assessment of gully erosion. Firstly, a gully erosion inventory map (GEIM) with 206 gully locations was obtained from various sources and randomly divided into two groups: A training dataset (70% of the data) and a validation dataset (30% of the data). Fifteen gully-related conditioning factors (GRCFs) including elevation, slope, aspect, plan curvature, stream power index, topographical wetness index, rainfall, soil type, drainage density, distance to river, distance to road, distance to fault, lithology, land use/land cover, and soil type, were used for modeling. The advanced land observing satellite (ALOS) digital elevation model with a spatial resolution of 30 m was used for the extraction of the above-mentioned topographic factors. The tolerance (TOL) and variance inflation factor (VIF) were also included for checking the multicollinearity among the GRCFs. Based on IoE, we concluded that soil type, lithology, and elevation were the most significant in terms of gully formation. Validation results using the area under the receiver operating characteristic curve (AUROC) showed that IoE (0.941) reached a higher prediction accuracy than VIKOR (0.857) and VIKOR-IoE (0.868). Based on our results, the combination of statistical (IoE) models along with remote sensing and GIS can convert the multi-criteria decision-making (MCDM) models into efficient and powerful tools for gully erosion prediction. We strongly suggest that decision-makers and managers should use these kinds of results to develop more consistent solutions to achieve sustainable development on degraded lands such as in the Semnan province.

scholarly journals Remote Sensing of Bush Encroachment on Commercial Cattle Farms in Semi-Arid Rangelands in Namibia

2011 ◽  
pp. 327-343
Author(s):  
Matthias Schröter ◽  
Oliver Jakoby ◽  
Roland Olbrich ◽  
Marcus Eichhorn ◽  
Stefan Baumgärtner
Keyword(s):  
Remote Sensing ◽  
Expert Knowledge ◽  
Accuracy Assessment ◽  
Vegetation Indices ◽  
Ground Truth ◽  
Aerial Photographs ◽  
Bush Encroachment ◽  
Arid Rangelands ◽  
Cattle Farms ◽  
Semi Arid

Bush encroachment is one of the most extensive changes in land cover in semi-arid rangelands and an urgent problem for cattle farming, rapidly reducing the productivity of the rangeland. Despite the severity of these consequences, a complete and accurate assessment of bush encroached areas is still missing at large. This study aims at assessing bush encroachment on commercial cattle farms in central Namibia by employing remote sensing methods to distinguish between areas covered by bush and open rangeland. The authors use different classification techniques and vegetation indices to characterize the nature of vegetation cover. Their analysis shows that results are sensitive to specific classifications of indices. As an accuracy assessment could not be run on these results the authors could not analyze which classification approximates real bush encroachment best. Hence, this study highlights the need for further analysis. Ground truth data, in the form of field mappings, high resolution aerial photographs or local expert knowledge are needed to gain further insights and produce reliable results.

scholarly journals Hybrid Models for Efficient Control, Optimization, and Monitoring of Thermo-Chemical Processes and Plants

Processes ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 515
Author(s):  
Thomas Freudenmann ◽  
Hans-Joachim Gehrmann ◽  
Krasimir Aleksandrov ◽  
Mohanad El-Haji ◽  
Dieter Stapf
Keyword(s):  
Artificial Intelligence ◽  
Expert Knowledge ◽  
Numerical Models ◽  
Integrated Control ◽  
Nox Reduction ◽  
Hybrid Models ◽  
Control Optimization ◽  
Pulverized Fuel ◽  
Renewable Energy Law ◽  
Institute Of Technology

This paper describes a procedure and an IT product that combine numerical models, expert knowledge, and data-based models through artificial intelligence (AI)-based hybrid models to enable the integrated control, optimization, and monitoring of processes and plants. The working principle of the hybrid model is demonstrated by NOx reduction through guided oscillating combustion at the pulverized fuel boiler pilot incineration plant at the Institute for Technical Chemistry, Karlsruhe Institute of Technology. The presented example refers to coal firing, but the approach can be easily applied to any other type of nitrogen-containing solid fuel. The need for a reduction in operation and maintenance costs for biomass-fired plants is huge, especially in the frame of emission reductions and, in the case of Germany, the potential loss of funding as a result of the Renewable Energy Law (Erneuerbare-Energien-Gesetz) for plants older than 20 years. Other social aspects, such as the departure of experienced personnel may be another reason for the increasing demand for data mining and the use of artificial intelligence (AI).

scholarly journals AutoQual: task-oriented structural vibration sensing quality assessment leveraging co-located mobile sensing context

2021 ◽  
Author(s):  
Yue Zhang ◽  
Zhizhang Hu ◽  
Susu Xu ◽  
Shijia Pan
Keyword(s):  
Task Performance ◽  
Expert Knowledge ◽  
Vibration Signal ◽  
Absolute Error ◽  
Mobile Sensing ◽  
Learning Performance ◽  
Structural Vibration ◽  
Vibration Sensing ◽  
Assessment Approach ◽  
Task Oriented

AbstractIn this paper, we introduce AutoQual, a mobile-based assessment scheme for infrastructure sensing task performance prediction under new deployment environments. With the growth of the Internet-of-Things (IoT), many non-intrusive sensing systems have been explored for various indoor applications, such as structural vibration sensing. This indirect sensing approach’s learning performance is prone to deployment variance when signals propagate through the environment. As a result, current systems heavily rely on expert knowledge and manual assessment to achieve effective deployments and high sensing task performance. In order to mitigate this expert effort, we propose to systematically study factors that reflect deployment environment characteristics and methods to measure them autonomously. We present AutoQual that measures a series of assessment factors (AFs) reflecting how the deployment environment impacts the system performance. AutoQual outputs a task-oriented sensing quality (TSQ) score by integrating measured AFs trained from known deployments as a prediction of untested system’s performance. In addition, AutoQual achieves this assessment without manual effort by leveraging co-located mobile sensing context to extract structural vibration signal for processing automatically. We evaluate AutoQual by using it to predict untested systems’ performance over multiple sensing tasks. We conduct real-world experiments and investigate 48 deployments in 11 environments. AutoQual achieves less than 0.10 average absolute error when auto-assessing multiple tasks at untested deployments, which shows a $$\le 0.018$$ ≤ 0.018 absolute error difference compared to the manual assessment approach.

scholarly journals Surface and subsurface flow effect on permanent gully formation and upland erosion near Lake Tana in the northern highlands of Ethiopia

2010 ◽  
Vol 14 (11) ◽  
pp. 2207-2217 ◽  
Author(s):  
T. Y. Tebebu ◽  
A. Z. Abiy ◽  
A. D. Zegeye ◽  
H. E. Dahlke ◽  
Z. M. Easton ◽  
...  
Keyword(s):  
Water Table ◽  
Gully Erosion ◽  
Grazing Land ◽  
Cross Sectional ◽  
Lake Tana ◽  
Erosion Rates ◽  
Remotely Sensed Imagery ◽  
Reservoir Siltation ◽  
Upland Erosion ◽  
Elevated Water

Abstract. Gully formation in the Ethiopian Highlands has been identified as a major source of sediment in water bodies, and results in sever land degradation. Loss of soil from gully erosion reduces agricultural productivity and grazing land availability, and is one of the major causes of reservoir siltation in the Nile Basin. This study was conducted in the 523 ha Debre-Mawi watershed south of Bahir Dar, Ethiopia, where gullies are actively forming in the landscape. Historic gully development in a section of the Debre-Mawi watershed was estimated with semi structured farmer interviews, remotely sensed imagery, and measurements of current gully volumes. Gully formation was assessed by instrumenting the gully and surrounding area to measure water table levels and soil physical properties. Gully formation began in the late 1980's following the removal of indigenous vegetation, leading to an increase in surface and subsurface runoff from the hillsides. A comparison of the gully area, estimated from a 0.58 m resolution QuickBird image, with the current gully area mapped with a GPS, indicated that the total eroded area of the gully increased from 0.65 ha in 2005 to 1.0 ha in 2007 and 1.43 ha in 2008. The gully erosion rate, calculated from cross-sectional transect measurements, between 2007 and 2008 was 530 t ha−1 yr−1 in the 17.4 ha area contributing to the gully, equivalent to over 4 cm soil loss over the contributing area. As a comparison, we also measured rill and interrill erosion rates in a nearby section of the watershed, gully erosion rates were approximately 20 times the measured rill and interrill rates. Depths to the water table measured with piezometers showed that in the actively eroding sections of the gully the water table was above the gully bottom and, in stable gully sections the water table was below the gully bottom during the rainy season. The elevated water table appears to facilitate the slumping of gully walls, which causes the gully to widen and to migrate up the hillside.

One- year mortality in patients with advanced hepatocellular carcinoma on immunotherapy: Prediction using machine learning models (Preprint)

2021 ◽  
Author(s):  
Thomas Ka-Luen Lui ◽  
Ka Shing, Michael Cheung ◽  
Wai Keung Leung
Keyword(s):  
Machine Learning ◽  
Hepatocellular Carcinoma ◽  
Characteristic Curve ◽  
False Negative ◽  
False Negative Rate ◽  
Absolute Error ◽  
Advanced Hepatocellular Carcinoma ◽  
Data Set ◽  
One Year ◽  
Related Mortality

BACKGROUND Immunotherapy is a new promising treatment for patients with advanced hepatocellular carcinoma (HCC), but is costly and potentially associated with considerable side effects. OBJECTIVE This study aimed to evaluate the role of machine learning (ML) models in predicting the one-year cancer-related mortality in advanced HCC patients treated with immunotherapy METHODS 395 HCC patients who had received immunotherapy (including nivolumab, pembrolizumab or ipilimumab) in 2014 - 2019 in Hong Kong were included. The whole data set were randomly divided into training (n=316) and validation (n=79) set. The data set, including 45 clinical variables, was used to construct six different ML models in predicting the risk of one-year mortality. The performances of ML models were measured by the area under receiver operating characteristic curve (AUC) and the mean absolute error (MAE) using calibration analysis. RESULTS The overall one-year cancer-related mortality was 51.1%. Of the six ML models, the random forest (RF) has the highest AUC of 0.93 (95%CI: 0.86-0.98), which was better than logistic regression (0.82, p=0.01) and XGBoost (0.86, p=0.04). RF also had the lowest false positive (6.7%) and false negative rate (2.8%). High baseline AFP, bilirubin and alkaline phosphatase were three common risk factors identified by all ML models. CONCLUSIONS ML models could predict one-year cancer-related mortality of HCC patients treated with immunotherapy, which may help to select patients who would most benefit from this new treatment option.

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