The Hybrid of Multilayer Perceptrons: A New Geostatistical Tool to Generate High-Resolution Climate Maps in Developing Countries

The ability to produce high-resolution climate maps is crucial for assessing climate change impacts and mitigating climate disasters and risks in developing countries. Mainstream geostatistical downscaling techniques use spatial interpolation or multi-linear regression models to produce high-resolut...

Full description

Saved in:
Bibliographic Details
Published inMathematics (Basel) Vol. 11; no. 5; p. 1239
Main Authors Han, Yue, Zhang, Zhihua, Kobe, Fekadu Tadege
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.03.2023
Subjects
Accuracy
Air temperature
Algorithms
Altitude
Bias
Carbon
Climate change
climate map
Climate models
Climatic changes
Developing countries
Disaster management
Geology
geostatistical downscaling analysis
Geostatistics
High resolution
hybrid of multilayer perceptrons
Interpolation
LDCs
Linear models (Statistics)
Linear regression models
Multilayer perceptrons
Precipitation
Precipitation (Meteorology)
Regression analysis
Regression models
Statistical methods
Topography
Variables
Weather stations
China
Online AccessGet full text

Cover

More Information
Summary:The ability to produce high-resolution climate maps is crucial for assessing climate change impacts and mitigating climate disasters and risks in developing countries. Mainstream geostatistical downscaling techniques use spatial interpolation or multi-linear regression models to produce high-resolution climate maps in data-scarce regions. Since global climate evolution is a nonlinear process governed by complex physical principles, these linear downscaling techniques cannot achieve the desired accuracy. Moreover, these techniques cannot utilize different resolution data as model inputs. In this study, we developed a hybrid of multilayer perceptrons that could couple high-resolution topographic data with sparse climate observation data well and then generate high-resolution climate maps. To test the performance of our tool, we generated high-resolution precipitation and air temperature maps using sparse observation data from 21 meteorological stations in Ethiopia. The accuracy of the high-resolution climate maps generated using our hybrid of MLPs clearly outperformed those using a multi-linear regression model or a pure MLP.
ISSN:2227-7390
2227-7390
DOI:10.3390/math11051239