MaxEnt brings comparable results when the input data are being completed; Model parameterization of four species distribution models
Sign Up to like & getrecommendations! Published in 2023 at "Ecology and Evolution"
DOI: 10.1002/ece3.9827
Abstract: Abstract Species distribution models (SDMs) are practical tools to assess the habitat suitability of species with numerous applications in environmental management and conservation planning. The manipulation of the input data to deal with their spatial… read more here.
Keywords: input data; species distribution; model; performance ... See more keywords
A Machine Learning Framework for Drought Prediction in Tropical Malaysia With Input Data Uncertainty
Sign Up to like & getrecommendations! Published in 2025 at "International Journal of Climatology"
DOI: 10.1002/joc.70196
Abstract: The far‐reaching and devastating impacts of drought underscored the need for effective drought forecasting. This study addressed the critical gap of the often‐overlooked impacts of input data uncertainty on the forecasting accuracy. The study incorporated… read more here.
Keywords: data uncertainty; input data; drought; xgboost rnn ... See more keywords
Identification of structures and mechanisms in a flow field by POD analysis for input data obtained from visualization and PIV
Sign Up to like & getrecommendations! Published in 2020 at "Experiments in Fluids"
DOI: 10.1007/s00348-020-03005-6
Abstract: Abstract This paper investigates the application of proper orthogonal decomposition (POD) for data obtained from visualizations. Using the POD method, the flow field behind one and two cylinders in a staggered configuration was analyzed. The… read more here.
Keywords: structures mechanisms; analysis; visualization; input data ... See more keywords
Hebbian Learning with Kernel-Based Embedding of Input Data
Sign Up to like & getrecommendations! Published in 2024 at "Neural Processing Letters"
DOI: 10.1007/s11063-024-11707-9
Abstract: Although it requires simple computations, provides good performance on linear classification tasks and offers a suitable environment for active learning strategies, the Hebbian learning rule is very sensitive to how the training data relate to… read more here.
Keywords: input data; input; hebbian learning; based embedding ... See more keywords
Partial Differential Equations with Random Input Data: A Perturbation Approach
Sign Up to like & getrecommendations! Published in 2018 at "Archives of Computational Methods in Engineering"
DOI: 10.1007/s11831-018-9275-2
Abstract: We study the numerical approximation of partial differential equations with random input data. Such problems arise when the uncertainty of the underlying system is taken into account using a probability setting. The main goal of… read more here.
Keywords: random input; partial differential; equations random; input data ... See more keywords
Sources of uncertainty in gross primary productivity simulated by light use efficiency models: Model structure, parameters, input data, and spatial resolution
Sign Up to like & getrecommendations! Published in 2018 at "Agricultural and Forest Meteorology"
DOI: 10.1016/j.agrformet.2018.08.003
Abstract: Abstract Accurate estimation of gross primary productivity (GPP) is essential for understanding ecosystem function and global carbon cycling. However, there is still substantial uncertainty in the magnitude, spatial distribution, and temporal dynamics of GPP. Using… read more here.
Keywords: data spatial; uncertainty; input data; spatial resolution ... See more keywords
Time-delayed machine learning models for estimating groundwater depth in the Hetao Irrigation District, China
Sign Up to like & getrecommendations! Published in 2021 at "Agricultural Water Management"
DOI: 10.1016/j.agwat.2021.107032
Abstract: Abstract A large amount of continuous input data is used to estimate groundwater level (GWL) by using machine learning models. However, data collection is very difficult and costly in undeveloped countries. Therefore, obtaining a general… read more here.
Keywords: model; learning models; input data; irrigation ... See more keywords
Uncertainty analysis of composite laminated plate with data-driven polynomial chaos expansion method under insufficient input data of uncertain parameters
Sign Up to like & getrecommendations! Published in 2019 at "Composite Structures"
DOI: 10.1016/j.compstruct.2018.11.015
Abstract: Abstract The uncertainty information related to uncertain structural, material and geometric parameters is included in the available input uncertainty data, and there are multiple uncertainty types when only insufficient input data is acquired from experimental… read more here.
Keywords: composite laminated; insufficient input; method; uncertainty ... See more keywords
Effects of input data aggregation on simulated crop yields in temperate and Mediterranean climates
Sign Up to like & getrecommendations! Published in 2019 at "European Journal of Agronomy"
DOI: 10.1016/j.eja.2018.11.001
Abstract: The modelling exercise for this study was highly supported by partner universities and research institutes in the framework of the MACSUR project and financially supported by the German Federal Ministry of Education and Research BMBF… read more here.
Keywords: input data; effects input; agricultural sciences; data aggregation ... See more keywords
Comparison of measured (XRPD) and modeled (A2M) soil mineralogies: A study of some Swedish forest soils in the context of weathering rate predictions
Sign Up to like & getrecommendations! Published in 2018 at "Geoderma"
DOI: 10.1016/j.geoderma.2017.09.004
Abstract: Abstract Quantitative soil mineralogy has been identified as a key factor influencing PROFILE weathering estimates, and is often calculated with normative methods, such as the “Analysis to Mineralogy” (‘A2M’) model. In Sweden and other countries,… read more here.
Keywords: mineralogy; xrpd; geochemistry; site ... See more keywords
Spatial prediction of permafrost occurrence in Sikkim Himalayas using logistic regression, random forests, support vector machines and neural networks
Sign Up to like & getrecommendations! Published in 2020 at "Geomorphology"
DOI: 10.1016/j.geomorph.2020.107331
Abstract: Abstract We have generated permafrost probability distribution maps (10 m resolution) for the north-eastern Himalayan region in Sikkim using remote sensing measurements and machine learning algorithms. Four machine learning algorithms, logistic regression, random forests, support vector… read more here.
Keywords: machine learning; permafrost; input data; data set ... See more keywords