Mapped predictions of manganese and arsenic in an alluvial aquifer using boosted regression trees

AbstractBackground: Although pelagic seabirds are broadly recognised as indicators of the health of marine systems, numerous gaps exist in knowledge of their at-sea distributions at the species level. These gaps have profound negative impacts on the robustness of marine conservation policies. Correlative modelling techniques have provided some information, but few studies have explored model development for non-breeding pelagic seabirds. Here, I present a first phase in developing robust niche models for highly mobile species as a baseline for further development. Methodology: Using observational data from a 12-year time period, 217 unique model parameterisations across three correlative modelling algorithms (boosted regression trees, Maxent and minimum volume ellipsoids) were tested in a time-averaged approach for their ability to recreate the at-sea distribution of non-breeding Wandering Albatrosses (Diomedea exulans) to provide a baseline for further development. Principle Findings/Results: Overall, minimum volume ellipsoids outperformed both boosted regression trees and Maxent. However, whilst the latter two algorithms generally overfit the data, minimum volume ellipsoids tended to underfit the data. Conclusions: The results of this exercise suggest a necessary evolution in how correlative modelling for highly mobile species such as pelagic seabirds should be approached. These insights are crucial for understanding seabird-environment interactions at macroscales, which can facilitate the ability to address population declines and inform effective marine conservation policy in the wake of rapid global change.

Download Full-text

Integration of Boosted Regression Trees and Cellular Automata—Markov Model to Predict the Land Use Spatial Pattern in Hotan Oasis

Sustainability ◽

10.3390/su12041396 ◽

2020 ◽

Vol 12 (4) ◽

pp. 1396

Author(s):

Shufang Wang ◽

Xiyun Jiao ◽

Liping Wang ◽

Aimin Gong ◽

Honghui Sang ◽

...

Keyword(s):

Land Use ◽

Cellular Automata ◽

Markov Model ◽

Land Use Changes ◽

Regression Trees ◽

Boosted Regression Trees ◽

Utilization Rate ◽

Protection Measures ◽

Unused Land ◽

The Government

The simulation and prediction of the land use changes is generally carried out by cellular automata—Markov (CA-Markov) model, and the generation of suitable maps collection is subjective in the simulation process. In this study, the CA-Markov model was improved by the Boosted Regression Trees (BRT) to simulate land use to make the model objectively. The weight of ten driving factors of the land use changes was analyzed in BRT, in order to produce the suitable maps collection. The accuracy of the model was verified. The outcomes represent a match of over 84% between simulated and actual land use in 2015, and the Kappa coefficient was 0.89, which was satisfactory to approve the calibration process. The land use of Hotan Oasis in 2025 and 2035 were predicted by means of this hybrid model. The area of farmland, built-up land and water body in Hotan Oasis showed an increasing trend, while the area of forestland, grassland and unused land continued to show a decreasing trend in 2025 and 2035. The government needs to formulate measures to improve the utilization rate of water resources to meet the growth of farmland, and need to increase ecological environment protection measures to curb the reduction of grass land and forest land for the ecological health.

Download Full-text

Case fatality ratio estimates for the 2013–2016 West African Ebola epidemic: application of Boosted Regression Trees for imputation

International Journal of Infectious Diseases ◽

10.1016/j.ijid.2018.11.313 ◽

2019 ◽

Vol 79 ◽

pp. 128 ◽

Cited By ~ 3

Author(s):

A. Forna ◽

P. Nouvellet ◽

I. Dorigatti ◽

C.A. Donnelly

Keyword(s):

Case Fatality ◽

Regression Trees ◽

West African ◽

Boosted Regression Trees ◽

Case Fatality Ratio

Download Full-text

Insights into the BRT (Boosted Regression Trees) Method in the Study of the Climate-Growth Relationship of Masson Pine in Subtropical China

Forests ◽

10.3390/f10030228 ◽

2019 ◽

Vol 10 (3) ◽

pp. 228 ◽

Cited By ~ 8

Author(s):

Hongliang Gu ◽

Jian Wang ◽

Lijuan Ma ◽

Zhiyuan Shang ◽

Qipeng Zhang

Keyword(s):

Tree Growth ◽

General Circulation ◽

Growth Response ◽

Circulation Model ◽

Regression Trees ◽

Pinus Massoniana ◽

Boosted Regression Trees ◽

Climate Factors ◽

Masson Pine ◽

Hydroclimatic Variability

Dendroclimatology and dendroecology have entered mainstream dendrochronology research in subtropical and tropical areas. Our study focused on the use of the chronology series of Masson pine (Pinus massoniana Lamb.), the most widely distributed tree species in the subtropical wet monsoon climate regions in China, to understand the tree growth response to ecological and hydroclimatic variability. The boosted regression trees (BRT) model, a nonlinear machine learning method, was used to explore the complex relationship between tree-ring growth and climate factors on a larger spatial scale. The common pattern of an asymptotic growth response to the climate indicated that the climate-growth relationship may be linear until a certain threshold. Once beyond this threshold, tree growth will be insensitive to some climate factors, after which a nonlinear relationship may occur. Spring and autumn climate factors are important controls of tree growth in most study areas. General circulation model (GCM) projections of future climates suggest that warming climates, especially temperatures in excess of those of the optimum growth threshold (as estimated by BRT), will be particularly threatening to the adaptation of Masson pine.

Download Full-text

Comparative assessment using boosted regression trees, binary logistic regression, frequency ratio and numerical risk factor for gully erosion susceptibility modelling

CATENA ◽

10.1016/j.catena.2019.104223 ◽

2019 ◽

Vol 183 ◽

pp. 104223 ◽

Cited By ~ 17

Author(s):

Alireza Arabameri ◽

Biswajeet Pradhan ◽

Luigi Lombardo

Keyword(s):

Logistic Regression ◽

Risk Factor ◽

Frequency Ratio ◽

Binary Logistic Regression ◽

Regression Trees ◽

Comparative Assessment ◽

Gully Erosion ◽

Boosted Regression Trees

Download Full-text

Residuals in the modelling of pollution concentration depending on meteorological conditions and traffic flow, employing decision trees

ITM Web of Conferences ◽

10.1051/itmconf/20182300016 ◽

2018 ◽

Vol 23 ◽

pp. 00016 ◽

Cited By ~ 3

Author(s):

Joanna A. Kamińska

Keyword(s):

Random Forest ◽

Traffic Flow ◽

Goodness Of Fit ◽

Regression Trees ◽

Meteorological Conditions ◽

Boosted Regression Trees ◽

Significant Preference ◽

Explanatory Variables ◽

The City

Two data mining methods – a random forest and boosted regression trees – were used to model values of roadside air pollution depending on meteorological conditions and traffic flow, using the example of data obtained in the city of Wrocław in the years 2015–2016. Eight explanatory variables – five continuous and three categorical – were considered in the models. A comparison was made of the quality of the fit of the models to empirical data. Commonly used goodness-of-fit measures did not imply a significant preference for either of the methods. Residual analysis was also performed; this showed boosted regression trees to be a more effective method for predicting typical values in the modelling of NO2, NOx and PM2.5, while the random forest method leads to smaller errors when predicting peaks.

Download Full-text

Evaluating the Influence of Rain Event Characteristics on Rainfall Interception by Urban Trees Using Multiple Correspondence Analysis

Water ◽

10.3390/w11122659 ◽

2019 ◽

Vol 11 (12) ◽

pp. 2659

Author(s):

Katarina Zabret ◽

Mojca Šraj

Keyword(s):

Correspondence Analysis ◽

Multiple Correspondence Analysis ◽

Regression Trees ◽

Pinus Nigra ◽

Rainfall Amount ◽

Urban Trees ◽

Boosted Regression Trees ◽

Rain Event ◽

Rainfall Interception ◽

Pine Tree

Urban trees play an important role in the built environment, reducing the rainfall reaching the ground by rainfall interception. The amount of intercepted rainfall depends on the meteorological and vegetation characteristics. By applying the multiple correspondence analysis (MCA), we analysed the influence of rainfall amount, intensity and duration, the number of raindrops, the mean volume diameter (MVD), wind speed and direction on rainfall interception. The analysis was based on data from 176 events collected over more than three years of observations. Measurements were taken under birch (Betula pendula Roth.) and pine (Pinus nigra Arnold) trees located in an urban park in the city of Ljubljana, Slovenia. The results indicate that rainfall interception is influenced the most by rainfall amount and the number of raindrops. In general, the ratio of rainfall interception to gross rainfall decreases with longer and more intense rainfall events. The influence of the raindrop number depends also on their size (MVD), which is evident especially for the pine tree. For example, pine tree interception increases with smaller raindrops regardless of their number. In addition, MCA gives a new insight into the influence of wind characteristics, which was not visible using previous methods of data analysis (regression analysis, correlation matrices, regression trees, boosted regression trees). According to the nearby buildings, a wind corridor is sometimes created, decreasing rainfall interception by both tree species.

Download Full-text