scholarly journals An Explorative Application of Random Forest Algorithm for Archaeological Predictive Modeling. A Swiss Case Study

2021 ◽  
Vol 4 (1) ◽  
pp. 110-125
Author(s):  
Maria Elena Castiello ◽  
Marj Tonini
Keyword(s):  
Random Forest ◽  
Predictive Modeling ◽  
Random Forest Algorithm

Predictive modeling of groundwater nitrate pollution using Random Forest and multisource variables related to intrinsic and specific vulnerability: A case study in an agricultural setting (Southern Spain)

2014 ◽  
Vol 476-477 ◽  
pp. 189-206 ◽  
Author(s):  
Victor Rodriguez-Galiano ◽  
Maria Paula Mendes ◽  
Maria Jose Garcia-Soldado ◽  
Mario Chica-Olmo ◽  
Luis Ribeiro
Keyword(s):  
Random Forest ◽  
Predictive Modeling ◽  
Nitrate Pollution ◽  
Southern Spain ◽  
Specific Vulnerability ◽  
Agricultural Setting

scholarly journals Water poverty assessment based on the random forest algorithm: application to Gansu, Northwest China

Water Policy ◽  
2021 ◽  
Author(s):  
Xiang Gao ◽  
Ke Wang ◽  
Kevin Lo ◽  
Ruiyang Wen ◽  
Xingxing Huang ◽  
...  
Keyword(s):  
Random Forest ◽  
Northwest China ◽  
Random Forest Algorithm ◽  
Poverty Assessment ◽  
Significant Difference ◽  
Level 3 ◽  
Level 1 ◽  
Poverty Levels ◽  
High Dimensional Datasets

Abstract This study proposes a random forest algorithm to evaluate water poverty. It shows how the machine learning technique can be used to classify the degree of water poverty into five levels: very severe, severe, moderate, mild, and very mild. The strengths of the proposed random forest method include a high classification accuracy, good operational efficiency, and the ability to handle high-dimensional datasets. The success of the proposed method is empirically illustrated through a case study in Gansu, Northwest China. The analysis shows that from 2000 to 2017, the severity of water poverty in the study area declined. In 2000, most municipalities were classified as level 1 (very severe) or level 2 (severe). In 2017, level 1 water poverty disappeared, with most municipalities classified in as level 3 (moderate) and level 4 (mild). Spatially, there is a significant difference between the water poverty levels of the western, central, and eastern parts of Gansu, and the eastern part is affected by serious water poverty problems.

Measuring urban poverty using multi-source data and a random forest algorithm: A case study in Guangzhou

2020 ◽  
Vol 54 ◽  
pp. 102014 ◽  
Author(s):  
Tong Niu ◽  
Yimin Chen ◽  
Yuan Yuan
Keyword(s):  
Random Forest ◽  
Urban Poverty ◽  
Random Forest Algorithm ◽  
Source Data

Application of random forest algorithm to Sentinel-1 for plantation detection: case study of Tesso Nilo ecosystem

2021 ◽  
Author(s):  
Giusti Ghivarry ◽  
Adhera Sukmawijaya
Keyword(s):  
Random Forest ◽  
Random Forest Algorithm

scholarly journals Preliminary Study of Landslide Susceptibility Modeling with Random Forest Algorithm Using R (Case Study: the Cisangkuy Sub-watershed)

2021 ◽  
Vol 936 (1) ◽  
pp. 012015
Author(s):  
S Sukristiyanti ◽  
K Wikantika ◽  
I A Sadisun ◽  
L F Yayusman ◽  
E Soebowo
Keyword(s):  
Random Forest ◽  
Spatial Data ◽  
Landslide Susceptibility ◽  
Google Earth ◽  
Landslide Susceptibility Mapping ◽  
Random Forest Algorithm ◽  
Visual Interpretation ◽  
Susceptibility Modeling ◽  
Initial Measure

Abstract Landslide susceptibility mapping is an initial measure in the landslide hazard mitigation. This study aims to evaluate landslide susceptibility in the Cisangkuy Sub-watershed, a part of Bandung Basin. Twenty-seven landslide variables were involved in this modeling derived from various data sources. As a target, 25 landslide polygons obtained through a visual interpretation of Google Earth timeseries images and 33 landslide points obtained from a field survey and an official landslide report, were used as landslide inventory data. All spatial data were prepared in the same cell size referring to the highest spatial resolution of data involved in this modeling, i.e., 8.34 m. Fifty-eight (58) landslide locations covering an area of 0.87 Ha are equivalent to 1040 cells in the raster format. In total, 2040 samples consisting of landslides and non-landslides with the same ratio, were trained using random forest algorithm. Non-landslides were sampled randomly from landslide-free cells. This modeling was executed using R environment. In this study, the result was two labels, susceptible and non-susceptible. This model provided an excellent performance, its accuracy reached 98.56%. This research needs an improvement to provide a probability that has a range of 0 to 1 to show the level of landslide susceptibility.

scholarly journals Analysis of the Spatio-Temporal Evolution of Dredging from Satellite Images: A Case Study in the Principality of Asturias (Spain)

2021 ◽  
Vol 9 (3) ◽  
pp. 267
Author(s):  
Vanesa Mateo-Pérez ◽  
Marina Corral-Bobadilla ◽  
Francisco Ortega-Fernández ◽  
Vicente Rodríguez-Montequín
Keyword(s):  
Random Forest ◽  
Satellite Images ◽  
Random Forest Algorithm ◽  
Past Behavior ◽  
Before And After ◽  
Real Behavior ◽  
Environmental Requirements ◽  
Spatio Temporal ◽  
Sentinel 2A

One of the fundamental tasks in the maintenance of port operations is periodic dredging. These dredging operations facilitate the elimination of sediments that the coastal dynamics introduce. Dredging operations are increasingly restrictive and costly due to environmental requirements. Understanding the condition of the seabed before and after dredging is essential. In addition, determining how the seabed has behaved in recent years is important to consider when planning future dredging operations. In order to analyze the behavior of sediment transport and the changes to the seabed due to sedimentation, studies of littoral dynamics are conducted to model the deposition of sediments. Another methodology that could be used to analyze the real behavior of sediments would be to study and compare port bathymetries collected periodically. The problem with this methodology is that it requires numerous bathymetric surveys to produce a sufficiently significant analysis. This study provides an effective solution for obtaining a dense time series of bathymetry mapping using satellite data, and enables the past behavior of the seabed to be examined. The methodology proposed in this work uses Sentinel-2A (10 m resolution) satellite images to obtain historical bathymetric series by the development of a random forest algorithm. From these historical bathymetric series, it is possible to determine how the seabed has behaved and how the entry of sediments into the study area occurs. This methodology is applied in the Port of Luarca (Principality of Asturias), obtaining satellite images and extracting successive bathymetry mapping utilizing the random forest algorithm. This work reveals how once the dock was dredged, the sediments were redeposited and the seabed recovered its level prior to dredging in less than 2 months.

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