Hybrid MLP-IDW approach based on nearest neighbor for spatial prediction

DOI10.1007/s00180-021-01186-0zbMath1505.62400OpenAlexW4205912348MaRDI QIDQ2095726

A. Tavassoli, Y. Waghei, Alireza Nazemi

Publication date: 15 November 2022

Published in: Computational Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00180-021-01186-0

zbMATH Keywords

simulationmultilayer perceptronartificial neural networkspatial predictionnearest neighborinverse distance weighting

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Artificial neural networks and deep learning (68T07) Geostatistics (86A32)

Uses Software

• geoR
• caret
• brnn

Cites Work

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• Evaluation of interpolation accuracy of neural kriging with application to temperature-distribution analysis
• Integrating data-to-data correlation into inverse distance weighting
• A Concordance Correlation Coefficient to Evaluate Reproducibility
• Comparison of Kriging and artificial neural network models for the prediction of spatial data
• Statistics for Spatial Data
• Learning representations by back-propagating errors
• Geostatistics for Environmental Scientists

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