Results 111 to 120 of about 32,345 (261)
REGIONALIZATION OF PROBABLE RAINFALL USING REGRESSION ANALYSIS AND KRIGING
Doboku Gakkai Ronbunshu, 1992 地域内のいくつかの観測地点のデータから, 観測データのない地点の推定量を求める方法について検討する. 本研究では, 特に流域内任意地点の確率雨量を対象として, それを推定するのに, 回帰分析法と kriging 法を用いる. これらの方法によれば, 任意地点の確率雨量の推定値のみならず, その推定精度も得られる. 地形効果を考慮するために, 回帰分析法と kriging 法を組み合わせた方法を試みる. 野洲川流域を対象として, 上記の方法の推定精度を比較検討して, その利害得失を明らかにしている.
Kaoru TAKARA, Akio OKA
openaire +2 more sources
Efficient Kilometer‐Scale Precipitation Downscaling With Conditional Wavelet Diffusion
Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 2, April 2026.Abstract Precipitation products such as Integrated Multi‐satellitE Retrievals have coarse resolution (∼10 ${\sim} 10$ km), which limits their application in hydrological modeling and extreme weather analysis. We propose the Wavelet Diffusion Model (WDM), a fast generative framework for high‐quality precipitation downscaling trained on multi‐radar multi‐
Chugang Yi +4 more
wiley +1 more source
ISPRS International Journal of Geo-Information, 2019 Soil property maps are essential resources for agricultural land use. However, soil properties mapping is costly and time-consuming, especially in the regions with complicated topographic conditions.
Tung Gia Pham +3 more
doaj +1 more source
Digital Mapping of Soil Organic Carbon Based on Machine Learning and Regression Kriging. [PDF]
Sensors (Basel), 2022 Zhu C +6 more
europepmc +1 more source
Functional Ecology, Volume 40, Issue 4, Page 983-1000, April 2026.Read the free Plain Language Summary for this article on the Journal blog. Abstract Organic phosphorus mineralization is a critical process in the phosphorus cycle, governing phosphorus bioavailability for plants. The PhoD gene, which encodes the key enzyme alkaline phosphatase, serves as a valuable biomarker for this process.
Sandhya Mishra +3 more
wiley +1 more source
Coarse‐to‐Fine Spatial Modeling: A Scalable, Machine‐Learning‐Compatible Framework
Geographical Analysis, Volume 58, Issue 2, April 2026.ABSTRACT This study proposes coarse‐to‐fine spatial modeling (CFSM) as a scalable and machine learning‐compatible alternative to conventional spatial process models. Unlike conventional covariance‐based spatial models, CFSM represents spatial processes using a multiscale ensemble of local models.
Daisuke Murakami +5 more
wiley +1 more source
Meta-models for structural reliability and uncertainty quantification [PDF]
, 2011 A meta-model (or a surrogate model) is the modern name for what was traditionally called a response surface. It is intended to mimic the behaviour of a computational model M (e.g.
Sudret, Bruno
core +4 more sources
Focal‐Feature Regression Kriging
Geographical Analysis, Volume 58, Issue 2, April 2026.ABSTRACT Spatial interpolation is a crucial task in geography. As perhaps the most widely used interpolation methods, geostatistical models‐such as Ordinary Kriging (OK)‐assume spatial stationarity, which makes it difficult to capture the nonstationary characteristics of geographic variables.
Peng Luo, Yilong Wu, Yongze Song
wiley +1 more source
Remote Sensing, 2017 Soils from the remote areas of the Amazon Rainforest in Brazil are poorly mapped due to the presence of dense forest and lack of access routes. The use of covariates derived from multispectral and radar remote sensors allows mapping large areas and has ...
Marcos B. Ceddia +3 more
doaj +1 more source
An analytic comparison of regularization methods for Gaussian Processes [PDF]
, 2015 Gaussian Processes (GPs) are a popular approach to predict the output of a parameterized experiment. They have many applications in the field of Computer Experiments, in particular to perform sensitivity analysis, adaptive design of experiments and ...
Bay, Xavier +4 more
core +3 more sources