scholarly journals SVM-Based Geospatial Prediction of Soil Erosion Under Static and Dynamic Conditioning Factors

2018 ◽  
Vol 203 ◽  
pp. 04004
Author(s):  
Muhammad Raza Ul Mustafa ◽  
Abdulkadir Taofeeq Sholagberu ◽  
Khamaruzaman Wan Yusof ◽  
Ahmad Mustafa Hashim ◽  
Muhammad Waris Ali Khan ◽  
...  
Keyword(s):  
Soil Erosion ◽  
Watershed Management ◽  
Land Use Planning ◽  
Land Surface ◽  
Vegetation Index ◽  
Support Vector ◽  
Susceptibility Map ◽  
Rainfall Erosivity ◽  
Kappa Index ◽  
Conditioning Factors

Land degradation caused by soil erosion remains an important global issue due to its adverse consequences on food security and environment. Geospatial prediction of erosion through susceptibility analysis is very crucial to sustainable watershed management. Previous susceptibility studies devoid of some crucial conditioning factors (CFs) termed dynamic CFs whose impacts on the accuracy have not been investigated. Thus, this study evaluates erosion susceptibility under the influence of both non-redundant static and dynamic CFs using support vector machine (SVM), remote sensing and GIS. The CFs considered include drainage density, lineament density, length-slope and soil erodibility as non-redundant static factors, and land surface temperature, soil moisture index, vegetation index and rainfall erosivity as the dynamic factors. The study implements four kernel tricks of SVM with sequential minimal optimization algorithm as a classifier for soil erosion susceptibility modeling. Using area under the curve (AUC) and Cohen’s kappa index (k) as the validation criteria, the results showed that polynomial function had the highest performance followed by linear and radial basis function. However, sigmoid SVM underperformed having the lowest AUC and k values coupled with higher classification errors. The CFs’ weights were implemented for the development of soil erosion susceptibility map. The map would assist planners and decision makers in optimal land-use planning, prevention of soil erosion and its related hazards leading to sustainable watershed management.

scholarly journals Geospatial Analysis of Soil Erosion Susceptibility and Causative Factors in Anambra State, South East, Nigeria

2021 ◽  
pp. 5-32
Author(s):  
Romanus Udegbunam Ayadiuno ◽  
Dominic Chukwuka Ndulue ◽  
Chinemelu Cosmas Ndichie ◽  
Arinze Tagbo Mozie ◽  
Philip O. Phil-Eze ◽  
...  
Keyword(s):  
Soil Erosion ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Soil Loss ◽  
Vegetation Index ◽  
Anthropogenic Activities ◽  
Rainfall Erosivity ◽  
Anambra State ◽  
Causative Factors

Land degradation is a function of soil erosion leading to soil loss and reduction in crop productivity as well as other socio-economic activities. The menace of soil erosion is challenging due to diverse factors including advertent and inadvertent anthropogenic activities. This study looks at soil erosion susceptibility and causative factors in Anambra State, both static and dynamic with the intent of identifying them, investigating spatial variability of soil loss, relate erodibility to soil properties and causative factors to soil erosion. Eight (8) prominent causative factors (CFs), were identified. These causative factors (CFs) were analyzed using ArcGIS 10.2. Sixty (60) soil samples were extracted randomly, analyzed, and tested. The study identified CFs such as Drainage Density, Erosion Density, Lineament Density, Slope Length, Land Surface Temperature, and Rainfall Erosivity, which contribute to Soil Erodibility (K - Factor). Land Surface Temperature, Soil Moisture Index, Rainfall Erosivity, and Normalized Difference Vegetation Index contributed to the loss of 8.97 ton/ha/yr, 9.1288 ton/ha/yr, 1,1134.7 ton/ha/yr, and 0.245 ton/ha/yr respectively to erosion in Anambra State. Conclusively, the dynamic causative factors influence soil susceptibility and trigger erosion in the State.

scholarly journals Incorporating Landslide Spatial Information and Correlated Features among Conditioning Factors for Landslide Susceptibility Mapping

2021 ◽  
Vol 13 (11) ◽  
pp. 2166
Author(s):  
Xin Yang ◽  
Rui Liu ◽  
Mei Yang ◽  
Jingjue Chen ◽  
Tianqiang Liu ◽  
...  
Keyword(s):  
Land Use ◽  
Hybrid Model ◽  
Landslide Susceptibility ◽  
Spatial Information ◽  
Landslide Susceptibility Mapping ◽  
Ratio Method ◽  
Support Vector ◽  
Susceptibility Map ◽  
Conditioning Factors ◽  
Proposed Model

This study proposed a new hybrid model based on the convolutional neural network (CNN) for making effective use of historical datasets and producing a reliable landslide susceptibility map. The proposed model consists of two parts; one is the extraction of landslide spatial information using two-dimensional CNN and pixel windows, and the other is to capture the correlated features among the conditioning factors using one-dimensional convolutional operations. To evaluate the validity of the proposed model, two pure CNN models and the previously used methods of random forest and a support vector machine were selected as the benchmark models. A total of 621 earthquake-triggered landslides in Ludian County, China and 14 conditioning factors derived from the topography, geological, hydrological, geophysical, land use and land cover data were used to generate a geospatial dataset. The conditioning factors were then selected and analyzed by a multicollinearity analysis and the frequency ratio method. Finally, the trained model calculated the landslide probability of each pixel in the study area and produced the resultant susceptibility map. The results indicated that the hybrid model benefitted from the features extraction capability of the CNN and achieved high-performance results in terms of the area under the receiver operating characteristic curve (AUC) and statistical indices. Moreover, the proposed model had 6.2% and 3.7% more improvement than the two pure CNN models in terms of the AUC, respectively. Therefore, the proposed model is capable of accurately mapping landslide susceptibility and providing a promising method for hazard mitigation and land use planning. Additionally, it is recommended to be applied to other areas of the world.

scholarly journals Landslide Susceptibility Mapping Using Different GIS-Based Bivariate Models

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1402 ◽  
Author(s):  
Nohani ◽  
Moharrami ◽  
Sharafi ◽  
Khosravi ◽  
Pradhan ◽  
...  
Keyword(s):  
Land Use ◽  
Landslide Susceptibility ◽  
Vegetation Index ◽  
Slope Angle ◽  
Aerial Photographs ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Susceptibility Map ◽  
Conditioning Factors ◽  
Bivariate Models

Landslides are the most frequent phenomenon in the northern part of Iran, which cause considerable financial and life damages every year. One of the most widely used approaches to reduce these damages is preparing a landslide susceptibility map (LSM) using suitable methods and selecting the proper conditioning factors. The current study is aimed at comparing four bivariate models, namely the frequency ratio (FR), Shannon entropy (SE), weights of evidence (WoE), and evidential belief function (EBF), for a LSM of Klijanrestagh Watershed, Iran. Firstly, 109 locations of landslides were obtained from field surveys and interpretation of aerial photographs. Then, the locations were categorized into two groups of 70% (74 locations) and 30% (35 locations), randomly, for modeling and validation processes, respectively. Then, 10 conditioning factors of slope aspect, curvature, elevation, distance from fault, lithology, normalized difference vegetation index (NDVI), distance from the river, distance from the road, the slope angle, and land use were determined to construct the spatial database. From the results of multicollinearity, it was concluded that no collinearity existed between the 10 considered conditioning factors in the occurrence of landslides. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) were used for validation of the four achieved LSMs. The AUC results introduced the success rates of 0.8, 0.86, 0.84, and 0.85 for EBF, WoE, SE, and FR, respectively. Also, they indicated that the rates of prediction were 0.84, 0.83, 0.82, and 0.79 for WoE, FR, SE, and EBF, respectively. Therefore, the WoE model, having the highest AUC, was the most accurate method among the four implemented methods in identifying the regions at risk of future landslides in the study area. The outcomes of this research are useful and essential for the government, planners, decision makers, researchers, and general land-use planners in the study area.

scholarly journals Discriminating Forest Successional Stages, Forest Degradation, and Land Use in Central Amazon Using ALOS/PALSAR-2 Full-Polarimetric Data

2020 ◽  
Vol 12 (21) ◽  
pp. 3512
Author(s):  
Natalia C. Wiederkehr ◽  
Fabio F. Gama ◽  
Paulo B. N. Castro ◽  
Polyanna da Conceição Bispo ◽  
Heiko Balzter ◽  
...  
Keyword(s):  
Land Use ◽  
Vegetation Index ◽  
Canopy Structure ◽  
Principal Component ◽  
Forest Degradation ◽  
Superior Performance ◽  
Support Vector ◽  
Kappa Index ◽  
Alos Palsar ◽  
Scattering Index

We discriminated different successional forest stages, forest degradation, and land use classes in the Tapajós National Forest (TNF), located in the Central Brazilian Amazon. We used full polarimetric images from ALOS/PALSAR-2 that have not yet been tested for land use and land cover (LULC) classification, neither for forest degradation classification in the TNF. Our specific objectives were: (1) to test the potential of ALOS/PALSAR-2 full polarimetric images to discriminate LULC classes and forest degradation; (2) to determine the optimum subset of attributes to be used in LULC classification and forest degradation studies; and (3) to evaluate the performance of Random Forest (RF) and Support Vector Machine (SVM) supervised classifications to discriminate LULC classes and forest degradation. PALSAR-2 images from 2015 and 2016 were processed to generate Radar Vegetation Index, Canopy Structure Index, Volume Scattering Index, Biomass Index, and Cloude–Pottier, van Zyl, Freeman–Durden, and Yamaguchi polarimetric decompositions. To determine the optimum subset, we used principal component analysis in order to select the best attributes to discriminate the LULC classes and forest degradation, which were classified by RF. Based on the variable importance score, we selected the four first attributes for 2015, alpha, anisotropy, volumetric scattering, and double-bounce, and for 2016, entropy, anisotropy, surface scattering, and biomass index, subsequently classified by SVM. Individual backscattering indexes and polarimetric decompositions were also considered in both RF and SVM classifiers. Yamaguchi decomposition performed by RF presented the best results, with an overall accuracy (OA) of 76.9% and 83.3%, and Kappa index of 0.70 and 0.80 for 2015 and 2016, respectively. The optimum subset classified by RF showed an OA of 75.4% and 79.9%, and Kappa index of 0.68 and 0.76 for 2015 and 2016, respectively. RF exhibited superior performance in relation to SVM in both years. Polarimetric attributes exhibited an adequate capability to discriminate forest degradation and classes of different ecological succession from the ones with less vegetation cover.

scholarly journals Soil erosion risk assessment: a case study of the Jos Plateau, Nigeria

2021 ◽  
pp. 109-117
Author(s):  
Ayodele Owonubi
Keyword(s):  
Soil Erosion ◽  
Vegetation Index ◽  
Vegetation Coverage ◽  
Anthropogenic Factors ◽  
Rainfall Erosivity ◽  
Entire Area ◽  
Jos Plateau ◽  
Erosion Rates ◽  
Erosion Processes ◽  
Soil Erosion Rates

Soil erosion is a treat to global food security. The objective of this study was to evaluate factors influencing erosion on the arable lands of the Jos Plateau; and to estimate the extent of soil erosion in the area. Universal Soil Loss Equation (USLE) model was used to evaluate soil erosion processes in the study area. This was facilitated with the aid of Geographic Information System Both for Interpolation and Geospatial analysis. Soil data from field survey was the primary source of data for analysis of soil erodibility. Topographic factor was determined from 90-meter elevation data. Rainfall erosivity was determined from rainfall data at 1 kilometer resolution. Whereas vegetation cover factor was determined from Normalized Difference Vegetation Index. Results of the study indicate that rainfall erosivity values were remarkably high and have mean values of 5117MJ.mm/ ha.h.y. Analysis of percent areal coverage indicate that the entire area had 52, 34, 7, and 7% low, moderate, high and very high topographic factors respectively. Further analysis indicate that anthropogenic factors had severely affected vegetation coverage of the Jos plateau, especially on the arable lands. Furthermore, during this research, the mean annual actual and potential soil erosion rates were estimated spatially over the Jos Plateau area. Soil erosion rates were far more than tolerable rates thereby affecting soil fertility and productivity.

scholarly journals Urban Overheating Assessment through Prediction of Surface Temperatures: A Case Study of Karachi, Pakistan

2021 ◽  
Vol 10 (8) ◽  
pp. 539
Author(s):  
Bilal Aslam ◽  
Ahsen Maqsoom ◽  
Nauman Khalid ◽  
Fahim Ullah ◽  
Samad Sepasgozar
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Short Term Memory ◽  
Thermal Emission ◽  
Global Climate ◽  
Current Model ◽  
Mitigation Strategies ◽  
Percentage Error ◽  
Support Vector ◽  
Road Density

Global climate has been radically affected by the urbanization process in recent years. Karachi, Pakistan’s economic hub, is also showing signs of swift urbanization. Owing to the construction of infrastructure projects under the China-Pakistan Economic Corridor (CPEC) and associated urbanization, Karachi’s climate has been significantly affected. The associated replacement of natural surfaces by anthropogenic materials results in urban overheating and increased local temperatures leading to serious health issues and higher air pollution. Thus, these temperature changes and urban overheating effects must be addressed to minimize their impact on the city’s population. For analyzing the urban overheating of Karachi city, LST (land surface temperature) is assessed in the current study, where data of the past 20 years (2000–2020) is used. For this purpose, remote sensing data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) and Moderate-Resolution Imaging Spectroradiometer (MODIS) sensors were utilized. The long short-term memory (LSTM) model was utilized where the road density (RD), elevation, and enhanced vegetation index (EVI) are used as input parameters. Upon comparing estimated and measured LST, the values of mean absolute error (MAE), mean square error (MSE), and mean absolute percentage error (MAPE) are 0.27 K, 0.237, and 0.15% for January, and 0.29 K, 0.261, and 0.13% for May, respectively. The low MAE, MSE, and MAPE values show a higher correlation between the predicted and observed LST values. Moreover, results show that more than 90% of the pixel data falls in the least possible error range of −1 K to +1 K. The MAE, MSE and MAPE values for Support Vector Regression (SVR) are 0.52 K, 0.453 and 0.18% and 0.76 K, 0.873, and 0.26%. The current model outperforms previous studies, shows a higher accuracy, and depicts greater reliability to predict the actual scenario. In the future, based on the accurate LST results from this model, city planners can propose mitigation strategies to reduce the harmful effects of urban overheating and associated Urban Heat Island effects (UHI).

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

Land ◽  
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.

scholarly journals Estimation of Soil Erosion Using RUSLE Model and GIS: The Case of Satinskyi Catchment, Western Rwanda

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
R. V Byizigiro ◽  
G Rwanyiziri ◽  
M. Mugabowindekwe ◽  
C. Kagoyire ◽  
M. Biryabarema
Keyword(s):  
Land Use ◽  
Soil Erosion ◽  
Land Surface ◽  
Soil Loss ◽  
Slope Angle ◽  
Rainfall Erosivity ◽  
Land Use Patterns ◽  
Rusle Model ◽  
Factor C ◽  
Loss Rates

The problem of soil erosion in Rwanda has been highlighted in previous studies. They have shown that half of the country’s farmland suffers moderate to severe erosion, with the highest soil loss rates found in the steeper and highly rainy northern and western highlands of the country. The purpose of this study was to estimate soil loss in Satinskyi, one of the catchments located in Ngororero District of Western Rwanda. This has been achieved using the Revised Universal Soil Loss Equation (RUSLE) model, which has been implemented in a Geographic Information Systems (GIS) environment. The methods consisted of preparing a set of input factor layers including Slope Length and Steepness (LS) factor, Rainfall Erosivity (R) factor, Soil Erodibility (K) factor, Support Practice (P) factor, and Land Surface Cover Management Factor (C) factor, for the model. The input factors have been integrated for soil loss estimates computation using RUSLE model, and this has enabled to quantitatively assess variations in the mean of the total estimated soil loss per annum in relation to topography and land-use patterns of the studied catchment. The findings showed that the average soil loss in Satinskyi catchment is estimated at 38.4 t/ha/year. It was however found that about 91% of the study area consists of areas with slope angle exceeding 15°, a situation which exposes the land to severe soil loss rates ranging between 31 t/ha/year and 41 t/ha/year. Apart from the steep slope, changes in land use also contribute to high rates of soil loss in the catchment. Keywords: Soil Erosion Estimation, GIS, RUSLE, Satinskyi Catchment, Rwanda

scholarly journals mosquitoes.pdf

2019 ◽  
Author(s):  
Oladimeji Mudele ◽  
Fabio M. Bayer ◽  
Lucas Zanandrez ◽  
Alvaro Eiras ◽  
Paolo Gamba
Keyword(s):  
Machine Learning ◽  
Statistical Models ◽  
Land Surface ◽  
Vegetation Index ◽  
Machine Learning Techniques ◽  
Distribution Model ◽  
Support Vector ◽  
World Population ◽  
Vector Population ◽  
Learning Techniques

<div>Over 50% of the world population is at risk of mosquito-borne diseases. Female Ae. aegypti mosquito species transmit Zika, Dengue, and Chikungunya. The spread of these diseases correlate positively with the vector population, and this population depends on biotic and abiotic environmental factors including temperature, vegetation condition, humidity and precipitation. To combat virus outbreaks, information about vector population is required. To this aim, Earth observation (EO) data provide fast, efficient and economically viable means to estimate environmental features of interest. In this work, we present a temporal distribution model for adult female Ae. aegypti mosquitoes based on the joint use of the Normalized Difference Vegetation Index, the Normalized Difference Water Index, the Land Surface Temperature (both at day and night time), along with the precipitation information, extracted from EO data. The model was applied separately to data obtained during three different vector control and field data collection condition regimes, and used to explain the differences in environmental variable contributions across these regimes. To this aim, a random forest (RF) regression technique and its nonlinear features importance ranking based on mean decrease impurity (MDI) were implemented. To prove the robustness of the proposed model, other machine learning techniques, including support vector regression, decision trees and k-nearest neighbor regression, as well as artificial neural networks, and statistical models such as the linear regression model and generalized linear model were also considered. Our results show that machine learning techniques perform better than linear statistical models for the task at hand, and RF performs best. By ranking the importance of all features based on MDI in RF and selecting the subset comprising the most</div>

scholarly journals Susceptibility Mapping of Soil Water Erosion Using Machine Learning Models

Water ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1995 ◽  
Author(s):  
Amirhosein Mosavi ◽  
Farzaneh Sajedi-Hosseini ◽  
Bahram Choubin ◽  
Fereshteh Taromideh ◽  
Gholamreza Rahi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Erosion ◽  
Vegetation Index ◽  
Water Erosion ◽  
Kappa Coefficient ◽  
Probability Of Detection ◽  
Susceptibility Mapping ◽  
Rainfall Erosivity ◽  
Critical Factors ◽  
Effective Factors

Soil erosion is a serious threat to sustainable agriculture, food production, and environmental security. The advancement of accurate models for soil erosion susceptibility and hazard assessment is of utmost importance for enhancing mitigation policies and laws. This paper proposes novel machine learning (ML) models for the susceptibility mapping of the water erosion of soil. The weighted subspace random forest (WSRF), Gaussian process with a radial basis function kernel (Gaussprradial), and naive Bayes (NB) ML methods were used in the prediction of the soil erosion susceptibility. Data included 227 samples of erosion and non-erosion locations through field surveys to advance models of the spatial distribution using predictive factors. In this study, 19 effective factors of soil erosion were considered. The critical factors were selected using simulated annealing feature selection (SAFS). The critical factors included aspect, curvature, slope length, flow accumulation, rainfall erosivity factor, distance from the stream, drainage density, fault density, normalized difference vegetation index (NDVI), hydrologic soil group, soil texture, and lithology. The dataset cells of samples (70% for training and 30% for testing) were randomly prepared to assess the robustness of the different models. The functional relevance between soil erosion and effective factors was computed using the ML models. The ML models were evaluated using different metrics, including accuracy, the kappa coefficient, and the probability of detection (POD). The accuracies of the WSRF, Gaussprradial, and NB methods were 0.91, 0.88, and 0.85, respectively, for the testing data; 0.82, 0.76, and 0.71, respectively, for the kappa coefficient; and 0.94, 0.94, and 0.94, respectively, for POD. However, the ML models, especially the WSRF, had an acceptable performance regarding producing soil erosion susceptibility maps. Maps produced with the most robust models can be a useful tool for sustainable management, watershed conservation, and the reduction of soil and water loss.

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