Coupling random forest and inverse distance weighting to generate climate surfaces of precipitation and temperature with Multiple-Covariates

Tan, Jianbo; Xie, Xinyao; Zuo, Jiaqi; Xing, Xuemin; Liu, Bin; Xia, Qing; Zhang, Yunfei

doi:10.1016/j.jhydrol.2021.126270

Cited by 60 publications

(25 citation statements)

References 52 publications

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“…Then, the geographical coordinates, x k , of any spatial point in a finite set of sampling points are substituted into the function formula, f , to calculate its attribute value. Among them, IDW has the advantages of simple principle and convenient calculation, and is in accordance with the first law of geography [48,49]. It is widely used in the construction of the digital elevation model, meteorological and hydrological analysis, etc.…”

Section: Gis Spatial Analysis Methodsmentioning

confidence: 99%

Spatial Structure Characteristics of Tourist Attraction Cooperation Networks in the Yangtze River Delta Based on Tourism Flow

Wang

Hang

2021

Sustainability

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This study aimed to examine the spatial structure of the tourist attraction cooperation network in the Yangtze River Delta, from the perspective of tourist flow. This study conducted spatial and social network analyses of 470 popular tourist attractions in the Yangtze River Delta region of China, accounting for the occurrence and co-occurrence of tourist attraction information in tourist travel notes. The analyzed tourist attractions show an obvious spatial agglomeration effect, including four high-density agglomeration areas and two medium-density agglomeration areas. Degree centrality, closeness centrality, and betweenness centrality were used to examine the tourism function, distribution function, and connection function of nodes in the network; nodes were divided into various types of roles according to their function. There are eight condensed subgroups, but their scales are unbalanced. In these condensed subgroups, several tourist attractions with an intermediate function can be selected as transit and stopover points on tourist routes. This study can contribute to the understanding of tourists’ spatial behavior, clarify the role and status of nodes in the cooperation network of tourist attractions based on tourism flow, and help them to formulate measures for the joint marketing of tourist attractions, and promote the development of tourism in the Yangtze River Delta region.

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Section: Gis Spatial Analysis Methodsmentioning

confidence: 99%

Spatial Structure Characteristics of Tourist Attraction Cooperation Networks in the Yangtze River Delta Based on Tourism Flow

Wang

Hang

2021

Sustainability

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“…This interpolation was conducted using the inverse distance weighting (IDW) method at a fixed cell resolution of 0.05°. This method was chosen due to its representativeness in variable terrain area and wide adoption for climate data interpolation, e.g., Tan et al (2021). Additionally, we performed a leave-one out cross-validation and extracted details on the accuracy of these interpolations (Table 2).…”

Section: Input Preprocessing Workflowmentioning

confidence: 99%

Hybrid Machine Learning for Integrating Pedological Knowledge into Digital Soil Mapping to Advance Next-Generation Earth System Models

Miranda¹,

Nóbrega²,

Silva³

et al. 2023

Preprint

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Environmental models often require soil maps to represent the spatial variability of soil properties. However, mapping soils using conventional in situ survey protocols is time-consuming and costly. As an alternative, Digital Soil Mapping (DSM) offers a fast-mapping approach that has the potential to estimate soil properties and their interrelationships over large areas. In this study, we address the currently outdated spatial information on soil properties across a tropical region (approx. 98,000 km2) with a ~700-km longitudinal gradient of contrasting topography, climate, and vegetation in Brazil by developing and applying statistical soil models for this region using a novel hybrid machine learning (HML) framework. This framework reduces prediction redundancies due to high multicollinearity by implementing a recursive feature selector algorithm for input selection. The hybrid framework’s core is composed of the Soil-Landscape Estimation and Evaluation Program (SLEEP) and a calibrated Gradient Boosting Model (GBM) capable of modeling the spatial distribution of soil properties at multiple soil depths. The use of SLEEP and GBM allowed us to explain the spatial distribution of various basic physical and chemical soil properties and their environmental modulators. The model training and testing approach used six topographical, ten meteorological and two vegetation properties, and data from 223 soil profiles across the study area. Our models demonstrated a consistent performance with spatial extrapolations exhibiting r2 values ranging from 0.79 to 0.98, and percent bias (PBIAS) from -1.39 to 1.14%. The properties related to topographic and climatic conditions were dominating when estimating the number of soil layers, percentage of silt and the sum of bases. Our framework features high flexibility, while reducing capital investments and increasing accuracy when compared to traditional mapping protocols that require extensive surveys.

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“…Consequently, geostatistical techniques have been widely disseminated in different disciplines (e.g., mining engineering, soil sciences, anthropology: Li and Heap, 2008;Relethford, 2008;Li and Heap, 2014). Spatial interpolation has been used for characterizing the spatial distribution and mapping soil gradients and properties (e.g., Robinson and Metternicht, 2006;Zuquim et al, 2019), generating climate surfaces of precipitation and temperature (e.g., Tan et al, 2021), and mapping epidemic vectors and diseases (e.g., Zhou et al, 2021). In ecological studies, they have also been applied, for example, to map temporal changes in live coral cover (Walker et al, 2012), predict forest stem volume (Wallerman et al, 2002), or to characterize the spatial structure of vegetation communities (Wallace et al, 2000).…”

Section: Introductionmentioning

confidence: 99%

Combining geospatial abundance and ecological niche models to identify high-priority areas for conservation: The neglected role of broadscale interspecific competition

2022

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Ecological niche models (ENMs) have become a practical and key mechanism for filling major gaps in spatial information for targeted conservation planning, particularly when only occurrence data are available. Nonetheless, accounting for abundance patterns in the internal structure of species’ ranges, and the role of biotic interactions in such models across broadscale, remains highly challenging. Our study gathered baseline information on abundance data of two Endangered Amazonian primates (Ateles chamek and Lagothrix lagotricha cana) to create geospatial abundance models using two spatial interpolation methods: Inverse distance weight (IDW) and Ordinary kriging (OK). The main goals were to: (i) test whether geospatial abundance models are correlated with habitat suitability derived from correlative ENMs; (ii) compare the strength of the abundance-suitability relationships between original and interpolated abundances; (iii) test whether interspecific competition between the two target taxa constrained abundance over broad spatial scales; and (iv) create ensemble models incorporating both habitat suitability and abundance to identify high-priority areas for conservation. We found a significant positive relationship between habitat suitability with observed and predicted abundances of woolly (L. l. cana) and spider (A. chamek) monkeys. Abundance-suitability correlations showed no significant differences when using original relative abundances compared to using interpolated abundances. We also found that the association between L. l. cana abundance and habitat suitability depended on the abundance of its putative competitor species, A. chamek. Our final models combining geospatial abundance information with ENMs were able to provide more realistic assessments of hotspots for conservation, especially when accounting for the important, but often neglected, role of interspecific competition in shaping species’ geographic ranges at broader scales. The framework developed here, including general trends in abundance patterns and suitability information, can be used as a surrogate to identify high-priority areas for conservation of poorly known species across their entire geographic ranges.

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Coupling random forest and inverse distance weighting to generate climate surfaces of precipitation and temperature with Multiple-Covariates

Cited by 60 publications

References 52 publications

Spatial Structure Characteristics of Tourist Attraction Cooperation Networks in the Yangtze River Delta Based on Tourism Flow

Spatial Structure Characteristics of Tourist Attraction Cooperation Networks in the Yangtze River Delta Based on Tourism Flow

Hybrid Machine Learning for Integrating Pedological Knowledge into Digital Soil Mapping to Advance Next-Generation Earth System Models

Combining geospatial abundance and ecological niche models to identify high-priority areas for conservation: The neglected role of broadscale interspecific competition

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