From 523edb68ecda3959727a0cdf68f1bf7af913b158 Mon Sep 17 00:00:00 2001 From: Jiale Ding Date: Sat, 1 Jun 2024 08:41:37 +0800 Subject: [PATCH] Update README.md --- README.md | 18 ++++++++++++++++-- 1 file changed, 16 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index bb0cce1..5a3d232 100644 --- a/README.md +++ b/README.md @@ -3,7 +3,7 @@ ![PyPI - License](https://img.shields.io/pypi/l/gnnwr) ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/gnnwr) [![PyPI - Version](https://img.shields.io/pypi/v/gnnwr)](https://pypi.org/project/gnnwr/) -![PyPI - Downloads](https://img.shields.io/pypi/dm/gnnwr) +[![Downloads](https://static.pepy.tech/badge/gnnwr)](https://pepy.tech/project/gnnwr) A PyTorch implementation of the spatiotemporal intelligent regression (STIR) models and the repository contains: @@ -164,7 +164,7 @@ An optimized spatial proximity measure was integrated into GNNWR. The optimized OSP

-> Ding, J., Cen, W., Wu, S., Chen, Y., Qi, J., Huang, B., & Du, Z. (2024). A neural network model to optimize the measure of spatial proximity in geographically weighted regression approach: a case study on house price in Wuhan. *International Journal of Geographical Information Science*, 1–21. https://doi.org/10.1080/13658816.2024.2343771 +> Ding, J., Cen, W., Wu, S., Chen, Y., Qi, J., Huang, B., & Du, Z. (2024). A neural network model to optimize the measure of spatial proximity in geographically weighted regression approach: a case study on house price in Wuhan. *International Journal of Geographical Information Science*, 1–21. #### 4.3.2 Land Surface Temperature @@ -176,6 +176,18 @@ Spatial downscaling is an important approach to obtain high-resolution land surf > Liang, M., Zhang, L., Wu, S., Zhu, Y., Dai, Z., Wang, Y., ... & Du, Z. (2023). A High-Resolution Land Surface Temperature Downscaling Method Based on Geographically Weighted Neural Network Regression. *Remote Sensing*, *15*(7), 1740. +### 4.4 Geology + +#### 4.4.1 mineral prospectivity + +In the field of mineral forecasting, accurate prediction of mineral resources is essential to meet the energy needs of modern society. A geographically neural network-weighted logistic regression is used for mineral prospect mapping. The model combines spatial patterns and neural networks with Shapley's additive theory of interpretation, which effectively handles the anisotropy of variables and nonlinear relationships between variables to achieve accurate predictions and provide explanations of mineralization in a complex spatial environment. + +

+mineral prospectivity +

+ +> Wang, L., Yang, J., Wu, S., Hu, L., Ge, Y., & Du, Z. (2024). Enhancing mineral prospectivity mapping with geospatial artificial intelligence: A geographically neural network-weighted logistic regression approach. *International Journal of Applied Earth Observation and Geoinformation*, 128, 103746. + **!!Further, these spatiotemporal intelligent regression models can be applied to other spatiotemporal modeling problems and socioeconomic phenomena.** ## 5 Related Research Papers @@ -194,6 +206,8 @@ Spatial downscaling is an important approach to obtain high-resolution land surf 5. Liu, C., Wu, S., Dai, Z., Wang, Y., Du, Z., Liu, X., & Qiu, C. (2023). High-Resolution Daily Spatiotemporal Distribution and Evaluation of Ground-Level Nitrogen Dioxide Concentration in the Beijing–Tianjin–Hebei Region Based on TROPOMI Data. *Remote Sensing*, *15*(15), 3878. 6. Wang, Z., Wang, Y., Wu, S., & Du, Z. (2022). House Price Valuation Model Based on Geographically Neural Network Weighted Regression: The Case Study of Shenzhen, China. *ISPRS International Journal of Geo-Information*, *11*(8), 450. 7. Wu, S., Du, Z., Wang, Y., Lin, T., Zhang, F., & Liu, R. (2020). Modeling spatially anisotropic nonstationary processes in coastal environments based on a directional geographically neural network weighted regression. *Science of the Total Environment*, *709*, 136097. +8. Wang, L., Yang, J., Wu, S., Hu, L., Ge, Y., & Du, Z. (2024). Enhancing mineral prospectivity mapping with geospatial artificial intelligence: A geographically neural network-weighted logistic regression approach. *International Journal of Applied Earth Observation and Geoinformation*, 128, 103746. +9. Ding, J., Cen, W., Wu, S., Chen, Y., Qi, J., Huang, B., & Du, Z. (2024). A neural network model to optimize the measure of spatial proximity in geographically weighted regression approach: a case study on house price in Wuhan. *International Journal of Geographical Information Science*, 1–21. ## 6 Contributing