GIS-based gully erosion susceptibility modeling, adapting bivariate statistical method and AHP approach in Gombe town and environs Northeast Nigeria

Abstract Gully erosion is a major environmental problem in Gombe town, a large area of land is becoming unsuitable for human settlement, hence the need for gully erosion susceptibility map of the study area. To generate a gully inventory map, detailed field exercise was carried out, during this investigation one hundred gullies were identified and studied extensively within the study area of about 550 km2. In addition to the mapped gullies, Google EarthPro with high-resolution imagery was used to locate the spatial extents of fifty (50) more gullies. Ten gully erosion predisposing factors were carefully selected considering the information obtained from literature, and multiple field survey of the study area, the factors include elevation, slope angle, curvature, aspect, topographic wetness index (TWI), soil texture, geology, drainage buffer, road buffer and landuse. In this study, a GIS-based Frequency Ratio (FR) and Analytical Hierarchy Process (AHP) models were employed to predict areas prone to gully erosion in Gombe town and environs. The result obtained from FR shows that drainage, soil texture, and slope have the highest correlation with gully occurrence, while the AHP model revealed that drainage buffer, soil texture, geology have a high correlation with the formation of a gully. Gully erosion susceptibility maps (GESM) were produced and reclassified into very high, high, moderate, and low zones. The overall accuracies of both models were tested utilizing area under the curve (AUC) values and gully density distribution. FR and AHP model have AUC values of 0.73 and 0.72 respectively, the outcome indicates that both models have high prediction accuracy. The gully erosion density distribution values revealed that gullies are concentrated in the very high susceptibility class and it decreases towards the low class, Therefore the GESM produced using these models in this study area is reliable and can be used for land management and future planning

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GIS-based gully erosion susceptibility modeling, adapting bivariate statistical method and AHP approach in Gombe town and environs Northeast Nigeria

Geoenvironmental Disasters ◽

10.1186/s40677-020-00166-8 ◽

2020 ◽

Vol 7 (1) ◽

Author(s):

Ogbonnaya Igwe ◽

Ugwuoke Ikechukwu John ◽

Onwuka Solomon ◽

Ozioko Obinna

Keyword(s):

Soil Texture ◽

Slope Angle ◽

Area Under The Curve ◽

Gully Erosion ◽

Susceptibility Map ◽

Large Area ◽

Topographic Wetness Index ◽

Ahp Model ◽

Susceptibility Modeling ◽

Very High

AbstractGully erosion is a major environmental problem in Gombe town, a large area of land is becoming unsuitable for human settlement, hence the need for a gully erosion susceptibility map of the study area. To generate a gully inventory map, a detailed field exercise was carried out, during this investigation one hundred gullies were identified and studied extensively within the study area of about 550 km2. In addition to the mapped gullies, Google EarthPro with high-resolution imagery was used to locate the spatial extents of fifty (50) more gullies. Ten gully erosion predisposing factors were carefully selected considering the information obtained from literature, and multiple field survey of the study area, the factors include elevation, slope angle, curvature, aspect, topographic wetness index (TWI), soil texture, geology, drainage buffer, road buffer and landuse. In this study, a GIS-based Frequency Ratio (FR) and Analytical Hierarchy Process (AHP) models were employed to predict areas prone to gully erosion in Gombe town and environs. The result obtained from FR shows that drainage, soil texture, and slope have the highest correlation with gully occurrence, while the AHP model revealed that drainage buffer, soil texture, geology have a high correlation with the formation of a gully. Gully erosion susceptibility maps (GESM) were produced and reclassified into very high, high, moderate, and low zones. The overall accuracies of both models were tested utilizing area under the curve (AUC) values and gully density distribution.FR and AHP model have AUC values of 0.73 and 0.72 respectively, the outcome indicates that both models have high prediction accuracy. The gully erosion density distribution values revealed that gullies are concentrated in the very high susceptibility class and it decreases towards the low class, therefore the GESM produced using these models in this study area is reliable and can be used for land management and future planning.

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GIS-based gully erosion susceptibility modeling, adapting bivariate statistical method and AHP approach in Gombe town and environs Northeast Nigeria

10.21203/rs.3.rs-30110/v3 ◽

2020 ◽

Author(s):

Ogbonnaya Igwe ◽

Ikechukwu John Ugwuoke ◽

Onwuka Solomon ◽

Ozioko Obinna

Keyword(s):

Soil Texture ◽

Slope Angle ◽

Area Under The Curve ◽

Gully Erosion ◽

Susceptibility Map ◽

Large Area ◽

Topographic Wetness Index ◽

Ahp Model ◽

Susceptibility Modeling ◽

Very High

Abstract Gully erosion is a major environmental problem in Gombe town, a large area of land is becoming unsuitable for human settlement, hence the need for a gully erosion susceptibility map of the study area. To generate a gully inventory map, a detailed field exercise was carried out, during this investigation one hundred gullies were identified and studied extensively within the study area of about 550 km2. In addition to the mapped gullies, Google EarthPro with high-resolution imagery was used to locate the spatial extents of fifty (50) more gullies. Ten gully erosion predisposing factors were carefully selected considering the information obtained from literature, and multiple field survey of the study area, the factors include elevation, slope angle, curvature, aspect, topographic wetness index (TWI), soil texture, geology, drainage buffer, road buffer and landuse. In this study, a GIS-based Frequency Ratio (FR) and Analytical Hierarchy Process (AHP) models were employed to predict areas prone to gully erosion in Gombe town and environs. The result obtained from FR shows that drainage, soil texture, and slope have the highest correlation with gully occurrence, while the AHP model revealed that drainage buffer, soil texture, geology have a high correlation with the formation of a gully. Gully erosion susceptibility maps (GESM) were produced and reclassified into very high, high, moderate, and low zones. The overall accuracies of both models were tested utilizing area under the curve (AUC) values and gully density distribution.FR and AHP model have AUC values of 0.73 and 0.72 respectively, the outcome indicates that both models have high prediction accuracy. The gully erosion density distribution values revealed that gullies are concentrated in the very high susceptibility class and it decreases towards the low class, therefore the GESM produced using these models in this study area is reliable and can be used for land management and future planning.

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Gis-based Gully Erosion Susceptibility Modelling, Adapting Bivariate Statistical Method and Ahp Approach in Gombe Town and Environs Northeast Nigeria

10.21203/rs.3.rs-30110/v1 ◽

2020 ◽

Author(s):

Ogbonnaya Igwe ◽

Ikechukwu John Ugwuoke ◽

Onwuka Solomon ◽

Ozioko Obinna

Keyword(s):

Density Distribution ◽

Soil Texture ◽

Slope Angle ◽

Gully Erosion ◽

Susceptibility Map ◽

Large Area ◽

Topographic Wetness Index ◽

High Resolution Imagery ◽

Bivariate Statistical Method ◽

Very High

Abstract Gully erosion is a major environmental problem in Gombe town, large area of land is becoming unsuitable for human settlement, hence the need for gully erosion susceptibility map of the study area.To generate gully inventory map, detailed field exercise was carried out, during this investigation one hundred gullies were identified and studied extensively within the study area of about 550 km2. In addition to the mapped gullies, Google EarthPro with high resolution imagery was used to locate the spatial extents of fifty (50) more gullies. Ten gully erosion predisposing factors were carefully selected considering the information obtained from literature, and multiple field survey of the study area, the factors include: elevation, slope angle, curvature, aspect, topographic wetness index (TWI),soil texture, geology, drainage buffer, road buffer and landuse.In this study, a GIS-based Frequency Ratio (FR) and Analytical Hierarchy process (AHP) models were employed to predict areas prone to gully erosion in Gombe town and environs.The result obtained from FR shows that drainage, soil texture and slope have highest correlation with gully occurrence, while AHP modelrevealed that drainage buffer, soil texture, geology have high correlation with the formation of gully. Gully erosion susceptibility maps (GESM) were produced and reclassified into very high, high, moderate and low zones.The overall accuracies of both models weretested by means of area under curve(AUC) values and gully density distribution. FR and AHP model have AUC values of 0.73 and 0.72 respectively, the outcome indicates that both models have high prediction accuracy. The gully erosion density distribution values revealed that gullies are concentrated in the very high susceptibility class and it decreases towards the low class, Therefore the GESM produced using these models in this study area is reliable and can be used for land management and future planning

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Fire Susceptibility Mapping in the Northeast Forests and Rangelands of Iran using New and Ensemble Data Mining Models

10.21203/rs.3.rs-209699/v1 ◽

2021 ◽

Author(s):

Saeedeh Eskandari ◽

Hamid Reza Pourghasemi ◽

John P. Tiefenbacher

Keyword(s):

Data Mining ◽

Slope Angle ◽

Area Under The Curve ◽

Susceptibility Mapping ◽

Wind Effect ◽

Susceptibility Map ◽

Ensemble Model ◽

Topographic Wetness Index ◽

Susceptibility Maps ◽

Golestan Province

Abstract Fires have increased in the northeastern Iran as its semiarid climate landscape is being desiccated by human activities. To combat fire outbreaks in any region, one must map fire susceptibility with accurate and efficient models. This research mapped fire susceptibility in the forests and rangelands of northeastern Iran’s Golestan Province using new data mining models. Fire effective factors data describing elevation, slope angle, annual mean rainfall, annual mean temperature, wind effect, topographic wetness index (TWI), plan curvature, distance from river, distance to road, and distance to village were obtained from several sources. The relative importance of each variable was determined with a random forest algorithm. Fire susceptibility maps were produced in R 3.3.3 software using GAM, MARS, SVM algorithms and a new ensemble of the three models: GAM-MARS-SVM. Validation of the four fire susceptibility maps was performed with the area under the curve. Results show that distance from village, annual mean rainfall and elevation were of greatest importance in predicting fire susceptibility. The new GAM-MARS-SVM ensemble model achieved the highest fire susceptibility mapping precision. The fire susceptibility map produced using the GAM-MARS-SVM ensemble model best detected the high fire risk areas in Golestan Province.

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

Applied Sciences ◽

10.3390/app8081369 ◽

2018 ◽

Vol 8 (8) ◽

pp. 1369 ◽

Cited By ~ 52

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.

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Landslides susceptibility modelling using Multivariate Logistic Regression Model in the Sahla Watershed in Northern Morocco

Sociedade & Natureza ◽

10.14393/sn-v33-2021-59124 ◽

2021 ◽

Vol 33 ◽

Author(s):

Mohammed El-Fengour ◽

Hanifa El Motaki ◽

Aissa El Bouzidi

Keyword(s):

Logistic Regression ◽

Landslide Susceptibility ◽

Slope Angle ◽

Google Earth ◽

Slope Aspect ◽

Multivariate Logistic Regression Model ◽

Susceptibility Map ◽

Potential Hazard ◽

Topographic Wetness Index ◽

Landslide Occurrence

This study aimed to assess landslide susceptibility in the Sahla watershed in northern Morocco. Landslides hazard is the most frequent phenomenon in this part of the state due to its mountainous precarious environment. The abundance of rainfall makes this area suffer mass movements led to a notable adverse impact on the nearby settlements and infrastructures. There were 93 identified landslide scars. Landslide inventories were collected from Google Earth image interpretations. They were prepared out of landslide events in the past, and future landslide occurrence was predicted by correlating landslide predisposing factors. In this paper, landslide inventories are divided into two groups, one for landslide training and the other for validation. The Landslide Susceptibility Map (LSM) is prepared by Logistic Regression (LR) Statistical Method. Lithology, stream density, land use, slope curvature, elevation, topographic wetness index, slope aspect, and slope angle were used as conditioning factors. The Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) was employed to examine the performance of the model. In the analysis, the LR model results in 96% accuracy in the AUC. The LSM consists of the predicted landslide area. Hence it can be used to reduce the potential hazard linked with the landslides in the Sahla watershed area in Rif Mountains in northern Morocco.

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

Sensors ◽

10.3390/s19112444 ◽

2019 ◽

Vol 19 (11) ◽

pp. 2444 ◽

Cited By ~ 37

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).

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Landslide Susceptibility Mapping using Bivariate Statistical Models and GIS in Chattagram District, Bangladesh

10.21203/rs.3.rs-531858/v1 ◽

2021 ◽

Author(s):

Md. Sharafat Chowdhury ◽

Bibi Hafsa

Keyword(s):

Statistical Models ◽

Landslide Susceptibility ◽

Slope Angle ◽

Information Value ◽

Weight Of Evidence ◽

Landslide Susceptibility Mapping ◽

Slope Aspect ◽

Susceptibility Map ◽

Topographic Wetness Index ◽

Conditioning Factors

Abstract This study attempts to produce Landslide Susceptibility Map for Chattagram District of Bangladesh by using five GIS based bivariate statistical models, namely the Frequency Ratio (FR), Shanon’s Entropy (SE), Weight of Evidence (WofE), Information Value (IV) and Certainty Factor (CF). A secondary landslide inventory database was used to correlate the previous landslides with the landslide conditioning factors. Sixteen landslide conditioning factors of Slope Aspect, Slope Angle, Geology, Elevation, Plan Curvature, Profile Curvature, General Curvature, Topographic Wetness Index, Stream Power Index, Sediment Transport Index, Topographic Roughness Index, Distance to Stream, Distance to Anticline, Distance to Fault, Distance to Road and NDVI were used. The Area Under Curve (AUC) was used for validation of the LSMs. The predictive rate of AUC for FR, SE, WofE, IV and CF were 76.11%, 70.11%, 78.93%, 76.57% and 80.43% respectively. CF model indicates 15.04% of areas are highly susceptible to landslide. All the models showed that the high elevated areas are more susceptible to landslide where the low-lying river basin areas have a low probability of landslide occurrence. The findings of this research will contribute to land use planning, management and hazard mitigation of the CHT region.

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Soil Erosion Susceptibility Mapping in Kozetopraghi Catchment, Iran: A Mixed Approach Using Rainfall Simulator and Data Mining Techniques

Land ◽

10.3390/land9100368 ◽

2020 ◽

Vol 9 (10) ◽

pp. 368

Author(s):

Abazar Esmali Ouri ◽

Mohammad Golshan ◽

Saeid Janizadeh ◽

Artemi Cerdà ◽

Assefa M. Melesse

Keyword(s):

Data Mining ◽

Soil Erosion ◽

Land Degradation ◽

Slope Angle ◽

Area Under The Curve ◽

Rainfall Simulation ◽

Support Vector ◽

Susceptibility Map ◽

Operating Characteristics ◽

Adaptive Boosting

Soil erosion determines landforms, soil formation and distribution, soil fertility, and land degradation processes. In arid and semiarid ecosystems, soil erosion is a key process to understand, foresee, and prevent desertification. Addressing soil erosion throughout watersheds scales requires basic information to develop soil erosion control strategies and to reduce land degradation. To assess and remediate the non-sustainable soil erosion rates, restoration programs benefit from the knowledge of the spatial distribution of the soil losses to develop maps of soil erosion. This study presents Support Vector Machine (SVM), Random Forest (RF), and adaptive boosting (AdaBoost) data mining models to map soil erosion susceptibility in Kozetopraghi watershed, Iran. A soil erosion inventory map was prepared from field rainfall simulation experiments on 174 randomly selected points along the Kozetopraghi watershed. In previous studies, this map has been prepared using indirect methods such as the Universal Soil Loss Equation to assess soil erosion. Direct field measurements for mapping soil erosion susceptibility have so far not been carried out in our study site in the past. The soil erosion rate data generated by simulated rainfall in 1 m2 plots at rainfall rate of 40 mmh−1 was used to develop the soil erosion map. Of the available data, 70% and 30% were randomly classified to calibrate and validate the models, respectively. As a result, the RF model with the highest area under the curve (AUC) value in a receiver operating characteristics (ROC) curve (0.91), and the lowest mean square error (MSE) value (0.09), has the most concordance and spatial differentiation. Sensitivity analysis by Jackknife and IncNodePurity methods indicates that the slope angle is the most important factor within the soil erosion susceptibility map. The RF susceptibility map showed that the areas located in the center and near the watershed outlet have the most susceptibility to soil erosion. This information can be used to support the development of sustainable restoration plans with more accuracy. Our methodology has been evaluated and can be also applied in other regions.

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Analysis of factors affecting the Spatial Distribution of co-seismic landslides triggered by the 2011 (Mw 6.9) Sikkim earthquake

10.5194/egusphere-egu21-16369 ◽

2021 ◽

Author(s):

Saurav Kumar ◽

Sengupta Aniruddha

Keyword(s):

Slope Failure ◽

Slope Angle ◽

Slope Aspect ◽

Susceptibility Map ◽

Past Study ◽

Topographic Wetness Index ◽

Landslide Occurrence ◽

Seismic Landslides ◽

Sikkim Earthquake ◽

Seismic Landslide

<p>The Himalayan region is known as an earthquake-triggered landslides prone area. It is characterized by high seismicity, large relative relief, steep slopes, and dense precipitation. These seismically triggered landslides are likely to affect substantial societal impacts, including loss of life, damage to houses, public buildings, various lifeline structures like highways, railways tracks, etc. Further, they obstruct post-earthquake emergency response efforts. A past study by Martha et al. 2014 reported that an earthquake of Mw 6.9 in 2011 triggered 1196 landslides in Sikkim which is a part of the eastern Himalayas. The slope failure events are controlled by several factors, which can be grouped into four main classes: seismology, topography, lithology, and hydrology. Each class contains several sub-factors. Having in-depth knowledge of these factors and their influence on the density of landslide events in the affected area due to the 2011 Sikkim earthquake is essential to realize the level of threat of co-seismic landslide due to future earthquakes. Eight landslide controlling factors is considered in this analysis including peak ground acceleration (PGA), slope, aspect, elevation, curvature, lithology, distance from rivers, and topographic wetness index (TWI). Further, the frequency ratio model using the GIS framework is applied to evaluate the contribution of each landslide controlling factor to landslide occurrence. Scatter plots between the number of landslides per km<sup>2</sup> (LN) and percentage of landslide area (LA) and causative factors indicate that distance from the river, slope angle, and PGA are the dominant factors that control the landslides. The results of the above analysis showed that the majority of co-seismic landslides occurred at slope >30&#176;, preferably in East, Southeast, and South directions and near river within a distance of 1500 m. The detailed study of interactions among these factors can improve the understanding of the mechanisms of co-seismic landslide occurrence in Sikkim and will be useful for producing a co-seismic landslide susceptibility map of the area.</p>

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