Potential of weight of evidence modelling for gully erosion hazard assessment in Mbire District – Zimbabwe

2014 ◽  
Vol 67-69 ◽  
pp. 145-152 ◽  
Author(s):  
F. Dube ◽  
I. Nhapi ◽  
A. Murwira ◽  
W. Gumindoga ◽  
J. Goldin ◽  
...  
Keyword(s):  
Hazard Assessment ◽  
Gully Erosion ◽  
Weight Of Evidence ◽  
Erosion Hazard

GIS Use for Mapping Land Degradation

2019 ◽  
pp. 736-770
Author(s):  
Mohamed Rached Boussema
Keyword(s):  
Soil Erosion ◽  
Data Acquisition ◽  
Hazard Assessment ◽  
Land Degradation ◽  
Digital Camera ◽  
Remotely Sensed ◽  
Regional Level ◽  
Digital Photogrammetry ◽  
Remotely Sensed Data ◽  
Erosion Hazard

In this chapter, the author presents a review of the GIS use during the research carried out during the past three decades dealing with land degradation. The objective is to assess the viability of applying GIS with different modes of remotely sensed data acquisition for quantifying land degradation in Tunisia. Various GIS based modelling approaches for soil erosion hazard assessment such as empirical and physical distributed are discussed. Five case studies are selected from several projects. They apply different methods for land degradation investigation at different scales using GIS and remotely sensed data. The research dealt mainly with: 1) The prediction of soil erosion at the regional level related to conservation techniques; 2) The quantification of soil erosion at the gully level based on GIS, digital photogrammetry and fieldwork; 3) The monitoring of gully erosion using GIS combined to images acquired by a non-metric digital camera on board a kite.

scholarly journals Machine Learning-Based Gully Erosion Susceptibility Mapping: A Case Study of Eastern India

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1313 ◽  
Author(s):  
Sunil Saha ◽  
Jagabandhu Roy ◽  
Alireza Arabameri ◽  
Thomas Blaschke ◽  
Dieu Tien Bui
Keyword(s):  
Machine Learning ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Gully Erosion ◽  
Machine Learning Techniques ◽  
Weight Of Evidence ◽  
Validation Dataset ◽  
Boosted Regression Tree ◽  
Area Index ◽  
Statistical Measures

Gully erosion is a form of natural disaster and one of the land loss mechanisms causing severe problems worldwide. This study aims to delineate the areas with the most severe gully erosion susceptibility (GES) using the machine learning techniques Random Forest (RF), Gradient Boosted Regression Tree (GBRT), Naïve Bayes Tree (NBT), and Tree Ensemble (TE). The gully inventory map (GIM) consists of 120 gullies. Of the 120 gullies, 84 gullies (70%) were used for training and 36 gullies (30%) were used to validate the models. Fourteen gully conditioning factors (GCFs) were used for GES modeling and the relationships between the GCFs and gully erosion was assessed using the weight-of-evidence (WofE) model. The GES maps were prepared using RF, GBRT, NBT, and TE and were validated using area under the receiver operating characteristic (AUROC) curve, the seed cell area index (SCAI) and five statistical measures including precision (PPV), false discovery rate (FDR), accuracy, mean absolute error (MAE), and root mean squared error (RMSE). Nearly 7% of the basin has high to very high susceptibility for gully erosion. Validation results proved the excellent ability of these models to predict the GES. Of the analyzed models, the RF (AUROC = 0.96, PPV = 1.00, FDR = 0.00, accuracy = 0.87, MAE = 0.11, RMSE = 0.19 for validation dataset) is accurate enough for modeling and better suited for GES modeling than the other models. Therefore, the RF model can be used to model the GES areas not only in this river basin but also in other areas with the same geo-environmental conditions.

Wind erosion hazard assessment of the Mongolian Plateau using FCM and GIS techniques

2009 ◽  
Vol 61 (4) ◽  
pp. 689-697 ◽  
Author(s):  
Huading Shi ◽  
Qingxian Gao ◽  
Yongqing Qi ◽  
Jiyuan Liu ◽  
Yunfeng Hu
Keyword(s):  
Hazard Assessment ◽  
Wind Erosion ◽  
Mongolian Plateau ◽  
Erosion Hazard ◽  
Gis Techniques

scholarly journals Gully Erosion Susceptibility Mapping in Highly Complex Terrain Using Machine Learning Models

2021 ◽  
Vol 10 (10) ◽  
pp. 680
Author(s):  
Annan Yang ◽  
Chunmei Wang ◽  
Guowei Pang ◽  
Yongqing Long ◽  
Lei Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Complex Terrain ◽  
Large Scale ◽  
Area Under The Curve ◽  
Gully Erosion ◽  
Susceptibility Mapping ◽  
Weight Of Evidence ◽  
Gradient Boosting ◽  
Machine Learning Classification ◽  
Extreme Gradient Boosting

Gully erosion is the most severe type of water erosion and is a major land degradation process. Gully erosion susceptibility mapping (GESM)’s efficiency and interpretability remains a challenge, especially in complex terrain areas. In this study, a WoE-MLC model was used to solve the above problem, which combines machine learning classification algorithms and the statistical weight of evidence (WoE) model in the Loess Plateau. The three machine learning (ML) algorithms utilized in this research were random forest (RF), gradient boosted decision trees (GBDT), and extreme gradient boosting (XGBoost). The results showed that: (1) GESM were well predicted by combining both machine learning regression models and WoE-MLC models, with the area under the curve (AUC) values both greater than 0.92, and the latter was more computationally efficient and interpretable; (2) The XGBoost algorithm was more efficient in GESM than the other two algorithms, with the strongest generalization ability and best performance in avoiding overfitting (averaged AUC = 0.947), followed by the RF algorithm (averaged AUC = 0.944), and GBDT algorithm (averaged AUC = 0.938); and (3) slope gradient, land use, and altitude were the main factors for GESM. This study may provide a possible method for gully erosion susceptibility mapping at large scale.

scholarly journals Spatial Pattern Analysis and Prediction of Gully Erosion Using Novel Hybrid Model of Entropy-Weight of Evidence

Water ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 1129 ◽  
Author(s):  
Alireza Arabameri ◽  
Artemi Cerda ◽  
John P. Tiefenbacher
Keyword(s):  
Land Use ◽  
Hybrid Model ◽  
Land Use Planning ◽  
Drainage Density ◽  
Gully Erosion ◽  
Weight Of Evidence ◽  
Spatial Pattern Analysis ◽  
Entropy Weight ◽  
Northern Iran ◽  
Environmental Conditioning

Gully erosion is an environmental problem in arid and semi-arid areas. Gullies threaten the soil and water resources and cause off- and on-site problems. In this research, a new hybrid model combines the index-of-entropy (IoE) model with the weight-of-evidence (WoE) model. Remote sensing and GIS techniques are used to map gully-erosion susceptibility in the watershed of the Bastam district of Semnan Province in northern Iran. The performance of the hybrid model is assessed by comparing the results with from models that use only IoE or WoE. Three hundred and three gullies were mapped in the study area and were randomly classified into two groups for training (70% or 212 gullies) and validation (30% or 91 gullies). Eighteen topographical, hydrological, geological, and environmental conditioning factors were considered in the modeling process. Prediction-rate curves (PRCs) and success-rate curves (SRCs) were used for validation. Results from the IoE model indicate that drainage density, slope, and rainfall factors are the most important factors promoting gullying in the study area. Validation results indicate that the ensemble model performed better than either the IoE or WoE models. The hybrid model predicted that 38.02 percent of the study area has either high or very high susceptible to gullying. Given the high accuracy of the novel hybrid model, this scientific methodology may be very useful for land use management decisions and for land use planning in gully-prone regions. Our research contributes to achieve Land Degradation Neutrality as will help to design remediation programs to control non-sustainable soil erosion rates.

Erosion hazard assessment along the Capitolo coast (Monopoli, southern Italy)

2013 ◽  
Vol 9 (2) ◽  
pp. 274-278
Author(s):  
Angela Indiveri ◽  
Antonella Marsico ◽  
Luigi Pennetta
Keyword(s):  
Hazard Assessment ◽  
Southern Italy ◽  
Erosion Hazard

scholarly journals Landslide Hazard Assessment Map as an Element Supporting Spatial Planning: The Flysch Carpathians Region Study

2021 ◽  
Vol 13 (2) ◽  
pp. 317
Author(s):  
Izabela Skrzypczak ◽  
Wanda Kokoszka ◽  
Dawid Zientek ◽  
Yongjing Tang ◽  
Janusz Kogut
Keyword(s):  
Hazard Assessment ◽  
Spatial Planning ◽  
Large Scale ◽  
Expert Knowledge ◽  
Building Design ◽  
Geological Structure ◽  
Landslide Hazard ◽  
Weight Of Evidence ◽  
Hazard Map ◽  
Rock Falls

Landslides and rock falls are among the many phenomena that have an impact on sustainable construction and infrastructure safety. The main causes of landslides are natural meteorological and hydrological phenomena. In building design and construction, environmental monitoring by identifying geotechnical hazards must be taken into account, as appropriate hazard assessment contributes to ensuring future construction safety. The Carpathian region in southern Poland is particularly predisposed to landslide formation. This may be favored by the nature of the shapes associated with the high and steep slopes of the region’s valleys. Another reason for concern is the flysch geological structure, which is characterized by alternating layers of water-permeable sandstones and poorly permeable shales, clays, and marls. Furthermore, the presence of a quaternary weathering cover makes the geological structure more susceptible to landslide processes and tectonic formations. The paper presents the results of a study whose aim was to elaborate a detailed landslide hazard map for a selected area in the Polish Carpathians, using statistical methods. The approach is based on the Hellwig method, which seems particularly useful in the assessment of susceptibility and landslide hazards on a local scale for a relatively small area. A two-stage study was conducted. The first stage of the research involved the creation of a database associated with environmental parameters and triggering factors, whereas the second stage consisted of the adoption of weights for seven thematic sections and their special features on the basis of expert knowledge. The hazard map developed as a result was compared to the mapping made using the weight-of-evidence method. The proposed data normalization method allows the use and analysis of both qualitative and quantitative data collected from various sources. The advantage of this method is the simple calculation procedure. A large-scale (1:2000) map might be used to assess the landslide hazard for specific cadastral units. Such a map becomes the basis for municipal spatial planning and may be able to influence investment decisions. Detailed landslide hazard maps are crucial for more precise risk evaluation for specific cadastral units. This, in turn, allows one to reduce serious economic and social losses, which might be the future results of landslides.

scholarly journals Comparison of Gully Erosion Susceptibility Mapping Using Weight of Evidence and Frequency Ratio Models at Sanganeh Kalat Basin

2018 ◽  
Vol 32 (4) ◽  
pp. 105-126
Author(s):  
Majid Ebrahim ◽  
Abolghasem Amir Ahmadi ◽  
Mohammad Ali Zangeneh Asadi ◽  
◽  
◽  
...  
Keyword(s):  
Frequency Ratio ◽  
Gully Erosion ◽  
Susceptibility Mapping ◽  
Weight Of Evidence
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