A systematic review of spatial disaggregation methods for climate action planning DOI Creative Commons
Shruthi Patil, Noah Pflugradt, Jann Michael Weinand

et al.

Energy and AI, Journal Year: 2024, Volume and Issue: 17, P. 100386 - 100386

Published: June 17, 2024

National-level climate action plans are often formulated broadly. Spatially disaggregating these to individual municipalities can offer substantial benefits, such as enabling regional strategies and for assessing the feasibility of national objectives. Numerous spatial disaggregation approaches be found in literature. This study reviews categorizes these. The review is followed by a discussion relevant methods plans. It seen that employing proxy data, machine learning models, geostatistical ones most energy analysis offers guidance selecting appropriate based on factors data availability at municipal level presence autocorrelation data. As urgency addressing change escalates, understanding aspects becomes increasingly important. will serve valuable guide researchers practitioners applying this crucial field.

Language: Английский

Design of an Automatic Classification System for Educational Reform Documents Based on Naive Bayes Algorithm DOI Creative Commons
Peng Zhang, Zifan Ma, Zeyuan Ren

et al.

Mathematics, Journal Year: 2024, Volume and Issue: 12(8), P. 1127 - 1127

Published: April 9, 2024

With the continuous deepening of educational reform, a large number policies, programs, and research reports have emerged, bringing heavy burden information processing management to educators. Traditional manual classification archiving methods are inefficient susceptible subjective factors. Therefore, an automated method is needed quickly accurately classify archive documents into their respective categories. Based on this, this paper proposes design automatic document system for reform based Naive Bayes algorithm address challenges in education field. Firstly, relevant literature data field collected organized establish annotated dataset model detection. Secondly, raw preprocessed by cleaning transforming original text make them more suitable input machine learning algorithms. Thirdly, various algorithms trained selected determine best classifying documents. Finally, determined algorithm, corresponding software designed automatically analysis. Through experimental evaluation result analysis, demonstrates effectiveness accuracy algorithm. This can efficiently categories accurately, thereby improving efficiency educators capabilities. In future, further exploration feature extraction be conducted optimize performance apply practical decision-making

Language: Английский

Citations

5
Refining daily precipitation estimates using machine learning and multi-source data in alpine regions with unevenly distributed gauges DOI Creative Commons

Huajin Lei,

Hongyi Li, Hongyu Zhao

et al.

Journal of Hydrology Regional Studies, Journal Year: 2025, Volume and Issue: 58, P. 102272 - 102272

Published: March 1, 2025

Language: Английский

Citations

0
Exploring machine learning approaches for precipitation downscaling DOI Creative Commons

Honglin Zhu,

Qiming Zhou, Jukka M. Krisp

et al.

Geo-spatial Information Science, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 17

Published: March 27, 2025

Language: Английский

Citations

0
Bayesian Model Averaging for Satellite Precipitation Data Fusion: From Accuracy Estimation to Runoff Simulation DOI Creative Commons
Shaowei Ning, Cheng Yang, Yuliang Zhou

et al.

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(7), P. 1154 - 1154

Published: March 25, 2025

Precipitation plays a vital role in the hydrological cycle, directly affecting water resource management and influencing flood drought risk prediction. This study proposes Bayesian Model Averaging (BMA) framework to integrate multiple precipitation datasets. The enhances estimation accuracy for simulations. BMA synthesizes four products—Climate Hazards Group Infrared with Station (CHIRPS), fifth-generation ECMWF Atmospheric Reanalysis (ERA5), Global Satellite Mapping of (GSMaP), Integrated Multi-satellitE Retrievals (IMERG)—over China’s Ganjiang River Basin from 2008 2020. We evaluated merged dataset’s performance against its constituent datasets Multi-Source Weighted-Ensemble (MSWEP) at daily, monthly, seasonal scales. Evaluation metrics included correlation coefficient (CC), root mean square error (RMSE), Kling–Gupta efficiency (KGE). Variable Infiltration Capacity (VIC) model was further applied assess how these affect runoff results indicate that BMA-merged dataset substantially improves when compared individual inputs. product achieved optimal daily (CC = 0.72, KGE 0.70) showed superior skill, notably reducing biases autumn winter. In applications, BMA-driven VIC effectively replicated observed patterns, demonstrating efficacy regional long-term predictions. highlights BMA’s potential optimizing inputs, providing critical insights sustainable reduction complex basins.

Language: Английский

Citations

0
Blending daily satellite precipitation product and rain gauges using stacking ensemble machine learning with the consideration of spatial heterogeneity DOI

Chuanfa Chen,

Jiaoyang Hao,

Shufan Yang

et al.

Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 133223 - 133223

Published: March 1, 2025

Language: Английский

Citations

0
Explainable artificial intelligence framework for urban global digital elevation model correction based on the SHapley additive explanation-random forest algorithm considering spatial heterogeneity and factor optimization DOI Creative Commons

Chuanfa Chen,

Yan Liu, Yanyan Li

et al.

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2024, Volume and Issue: 129, P. 103843 - 103843

Published: April 17, 2024

Satellite global digital elevation models (GDEMs) suffer from positive biases in urban areas due to building artifacts. While various machine learning (ML)-based methods have been proposed remove these biases, their generalizability is limited by spatial heterogeneity and redundancy prediction factors across different regions. Therefore, investigate the of address problem factor ML-based model prediction, this paper proposes an explainable artificial intelligence framework (XAI) for correcting GDEMs using SHapley additive explanation (SHAP)-random forest (RF) algorithm. The performance 30-m COPDEM (COPDEM30) was demonstrated New York City. results were compared with first Forests-And-Buildings removed DEM (FABDEM) three classical RF-based without considering (or) optimization. indicate that each contributes differently correction COPDEM30 regions, showing distinct regional characteristics heterogeneity. constructed more applicable regions similar features training In comparison traditional points, method obtains high accuracy. Specifically, while Root Mean Square Error (RMSE) Absolute (MAE) values RF ranged between 2.601 m 2.724 m, 1.686 1.785 respectively, achieves RMSE 2.258 MAE 1.436 m. Moreover, reduces original 7.652 (4.858 m) 3.797 (2.404 m), when applied area providing points. summary, XAI based on SHAP-RF can effectively quantify contribution GDEM correction, both globally locally, which conducive construction improvement system It also provides a reference improving geosciences.

Language: Английский

Citations

2
A systematic review of spatial disaggregation methods for climate action planning DOI Creative Commons
Shruthi Patil, Noah Pflugradt, Jann Michael Weinand

et al.

Energy and AI, Journal Year: 2024, Volume and Issue: 17, P. 100386 - 100386

Published: June 17, 2024

National-level climate action plans are often formulated broadly. Spatially disaggregating these to individual municipalities can offer substantial benefits, such as enabling regional strategies and for assessing the feasibility of national objectives. Numerous spatial disaggregation approaches be found in literature. This study reviews categorizes these. The review is followed by a discussion relevant methods plans. It seen that employing proxy data, machine learning models, geostatistical ones most energy analysis offers guidance selecting appropriate based on factors data availability at municipal level presence autocorrelation data. As urgency addressing change escalates, understanding aspects becomes increasingly important. will serve valuable guide researchers practitioners applying this crucial field.

Language: Английский

Citations

1