Accuracy Assessment of Different Digital Surface Models

Algancı, Uğur; Besol, Baris; Sertel, Elif

doi:10.3390/ijgi7030114

Cited by 103 publications

(76 citation statements)

References 26 publications

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“…Because many factors used in GESM were extracted from a digital elevation model (DEM), the quality of the DEM significantly influences the accuracy of the results [96,97]. The Advanced Land Observing Satellite (ALOS) DEM with 12.5 m spatial resolution was used as it has been shown to provide better accuracy than both the Shuttle Radar Topography Mission (SRTM) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and DEMs [98].…”

Section: Discussionmentioning

confidence: 99%

Gully Head-Cut Distribution Modeling Using Machine Learning Methods—A Case Study of N.W. Iran

Arabameri

Chen

Blaschke

et al. 2019

Water

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To more effectively prevent and manage the scourge of gully erosion in arid and semi-arid regions, we present a novel-ensemble intelligence approach-bagging-based alternating decision-tree classifier (bagging-ADTree)-and use it to model a landscape's susceptibility to gully erosion based on 18 gully-erosion conditioning factors. The model's goodness-of-fit and prediction performance are compared to three other machine learning algorithms (single alternating decision tree, rotational-forest-based alternating decision tree (RF-ADTree), and benchmark logistic regression). To achieve this, a gully-erosion inventory was created for the study area, the Chah Mousi watershed, Iran by combining archival records containing reports of gully erosion, remotely sensed data from Google Earth, and geolocated sites of gully head-cuts gathered in a field survey. A total of 119 gully head-cuts were identified and mapped. To train the models' analysis and prediction capabilities, 83 head-cuts (70% of the total) and the corresponding measures of the conditioning factors were input into each model. The results from the models were validated using the data pertaining to the remaining 36 gully locations (30%). Next, the frequency ratio is used to identify which conditioning-factor classes have the strongest correlation with gully erosion. Using random-forest modeling, the relative importance of each of the conditioning factors was determined. Based on the random-forest results, the top eight factors in this study area are distance-to-road, drainage density, distance-to-stream, LU/LC, annual precipitation, topographic wetness index, NDVI, and elevation. Finally, based on goodness-of-fit and AUROC of the success rate curve (SRC) and prediction rate curve (PRC), the results indicate that the bagging-ADTree ensemble model had the best performance, with SRC (0.964) and PRC (0.978). RF-ADTree (SRC = 0.952 and PRC = 0.971), ADTree (SRC = 0.926 and PRC = 0.965), and LR (SRC = 0.867 and PRC = 0.870) were the subsequent best performers. The results also indicate that bagging and RF, as meta-classifiers, improved the performance of the ADTree model as a base classifier. The bagging-ADTree model's results indicate that 24.28% of the study area is classified as having high and very high susceptibility to gully erosion. The new ensemble model accurately Water 2020, 12, 16 2 of 26 identified the areas that are susceptible to gully erosion based on the past patterns of formation, but it also provides highly accurate predictions of future gully development. The novel ensemble method introduced in this research is recommended for use to evaluate the patterns of gullying in arid and semi-arid environments and can effectively identify the most salient conditioning factors that promote the development and expansion of gullies in erosion-susceptible environments.

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Section: Discussionmentioning

confidence: 99%

Gully Head-Cut Distribution Modeling Using Machine Learning Methods—A Case Study of N.W. Iran

Arabameri

Chen

Blaschke

et al. 2019

Water

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“…DEMs based study provides more accurate and significant results [90][91][92]. Different DEMs provide different results, e.g., ALOS DEM with 30 m spatial resolution provide suitable and excellent results, comparatively the ASTER and SRTM DEMs with 30 m resolution [93]. Here, the authors combined the geomorphology, geology and hydrology parameters to recognize the spatial groundwater potential.…”

Section: Discussionmentioning

confidence: 99%

Application of Probabilistic and Machine Learning Models for Groundwater Potentiality Mapping in Damghan Sedimentary Plain, Iran

et al. 2019

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Groundwater is one of the most important natural resources, as it regulates the earth’s hydrological system. The Damghan sedimentary plain area, located in the region of a semi-arid climate of Iran, has very critical conditions of groundwater due to massive pressure on it and is in need of robust models for identifying the groundwater potential zones (GWPZ). The main goal of the current research is to prepare a groundwater potentiality map (GWPM) considering the probabilistic, machine learning, data mining, and multi-criteria decision analysis (MCDA) approaches. For this purpose, 80 wells collected from the Iranian groundwater resource department and field investigation with global positioning system (GPS), have been selected randomly and considered as the groundwater inventory datasets. Out of 80 wells, 56 (70%) wells have been brought into play for modeling and 24 (30%) for validation purposes. Elevation, slope, aspect, convergence index (CI), rainfall, drainage density (Dd), distance to river, distance to fault, distance to road, lithology, soil type, land use/land cover (LU/LC), normalized difference vegetation index (NDVI), topographic wetness index (TWI), topographic position index (TPI), and stream power index (SPI) have been used for modeling purpose. The area under the receiver operating characteristic (AUROC), sensitivity (SE), specificity (SP), accuracy (AC), mean absolute error (MAE), and root mean square error (RMSE) are used for checking the goodness-of-fit and prediction accuracy of approaches to compare their performance. In addition, the influence of groundwater determining factors (GWDFs) on groundwater occurrence was evaluated by performing a sensitivity analysis model. The GWPMs, produced by technique for order preference by similarity to ideal solution (TOPSIS), random forest (RF), binary logistic regression (BLR), weight of evidence (WoE) and support vector machine (SVM) have been classified into four categories, i.e., low, medium, high and very high groundwater potentiality with the help of the natural break classification methods in the GIS environment. The very high groundwater potentiality class is covered 15.09% for TOPSIS, 15.46% for WoE, 25.26% for RF, 15.47% for BLR, and 18.74% for SVM of the entire plain area. Based on sensitivity analysis, distance from river, and drainage density represent significantly effects on the groundwater occurrence. validation results show that the BLR model with best prediction accuracy and goodness-of-fit outperforms the other five models. Although, all models have very good performance in modeling of groundwater potential. Results of seed cell area index model that used for checking accuracy classification of models show that all models have suitable performance. Therefore, these are promising models that can be applied for the GWPZs identification, which will help for some needful action of these areas.

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“…In this study, the slope data was obtained from ALOS DSM data processing. The ALOS DSM produced very good results, specifically compared to other freely available DSMs [8].…”

Section: Slopementioning

confidence: 90%

Analysis of land use changes to the criticality level of the catchment area in eight watersheds that flow into Jakarta Bay, Indonesia

Rachmat

Helmi

2018

E3S Web Conf.

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Environmental problems in Jakarta, especially flooding problem, is strongly influenced by the condition of eight watersheds that flow into Jakarta Bay, namely Bekasi, Cisadane, Angke Pesanggrahan, Ciliwung, Krukut, Cakung, Buaran and Sunter. The center-periphery relationship between Jakarta and surrounding areas triggered significant land use changes in the watershed, especially in the upstream area. This study aims to analyze land use changes in eight watersheds that flow into Jakarta Bay in 2000, 2009 and 2017 as well as their effect on the distribution of the criticality level of the catchment area, using Geographic Information System (GIS) refers to Planning Procedures for Forest and Land Rehabilitation Engineering Watershed. The criticality map of the catchment area was created by overlay and scoring slope infiltration map, soil infiltration map and rainfall infiltration map. The results showed that there was a significant increase in built-up area of 14,33% in 2000-2009 and 10,42% in 2009-2017. In contrast, the area of dry land farming decreased significantly by 12,99% in 2000-2009 and 5.96% in 2009-2017. Based on the criticality analysis of the catchment area, land use changes in study area, mainly due to urban growth, has a significant effect on the criticality level of the catchment area.

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Accuracy Assessment of Different Digital Surface Models

Cited by 103 publications

References 26 publications

Gully Head-Cut Distribution Modeling Using Machine Learning Methods—A Case Study of N.W. Iran

Gully Head-Cut Distribution Modeling Using Machine Learning Methods—A Case Study of N.W. Iran

Application of Probabilistic and Machine Learning Models for Groundwater Potentiality Mapping in Damghan Sedimentary Plain, Iran

Analysis of land use changes to the criticality level of the catchment area in eight watersheds that flow into Jakarta Bay, Indonesia

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