[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3281":3,"related-3281":56},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":18,"tags":20,"primary_topic":9,"secondary_topics":21,"classification":22,"search_phrases":38,"slug":39,"view_count":40,"doi":41,"paper":42,"created_at":55},3281,"Economic Growth Nowcasting in Mauritania using Machine Learning and Satellite Data","https:\u002F\u002Fdoi.org\u002F10.63620\u002Fmkijbaft.2026.1027","This paper examines the role of satellite data and machine learning techniques in improving the nowcasting of real GDP in Mauritania, a context characterized by limited data availability and delays in official statistics. By combining traditional macroeconomic indicators with satellite-based variables such as Nighttime Lights and the Normalized Difference Vegetation Index (NDVI), the study develops a framework capable of capturing real-time economic dynamics. The results show that the inclusion of satellite data improves predictive accuracy, particularly when using non-linear models such as XGBoost. In particular, XGBoost records a reduction in RMSE from 0.019 to 0.017 and an increase in R² from 0.372 to 0.483 when satellite variables are included. Nighttime Lights are strongly correlated with economic activity, while NDVI exhibits more limited explanatory power. Overall, the findings highlight the potential of integrating alternative data sources and machine learning methods to enhance economic monitoring and support decision-making in data-constrained environments.","本文考察了卫星数据和机器学习技术在改进毛里塔尼亚实际GDP即时预测（nowcasting）中的作用，该国面临数据可得性有限和官方统计数据滞后的背景。通过将传统宏观经济指标与夜间灯光（Nighttime Lights）和归一化植被指数（NDVI）等卫星变量相结合，本研究构建了一个能够捕捉实时经济动态的框架。结果表明，纳入卫星数据提高了预测精度，尤其是在使用XGBoost等非线性模型时。具体而言，当纳入卫星变量后，XGBoost的均方根误差（RMSE）从0.019降至0.017，R²从0.372提升至0.483。夜间灯光与经济活动高度相关，而NDVI的解释力则较为有限。总体而言，研究结果凸显了整合替代数据源和机器学习方法以增强经济监测并支持数据受限环境下决策的潜力。",null,"OpenAlex","2026-09-22T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"研究毛里塔尼亚GDP即时预测，与三农、农业信息化、智慧农业无关，不予入选。",[19],{"name":10,"url":6},[12],[],{"status":23,"locked":14,"version":24,"method":25,"evidence":26},"needs_review","2026-10-22.2","rules",{"candidates":27,"agricultural_evidence":14,"reason":37},[28],{"code":29,"score":30,"supported":31,"title":32,"abstract":34,"tags":36},"I2",14,true,[33],"machine learning",[35,33],"机器学习",[],"农业相关性需要复核",[],"EconomicGrowthNowcastinginMauritaniausin-3281",2,"10.63620\u002Fmkijbaft.2026.1027",{"doi":41,"openalex_id":43,"authors":44,"venue":9,"cited_by_count":15,"oa_url":6,"card":47,"direction":53,"ingested_from":54},"W7213984344",[45],{"name":46,"orcid":9},"Yahya Abou LY",{"tldr":48,"method":49,"finding":50,"direction":51,"opportunity":52},"结合卫星数据与机器学习改进毛里塔尼亚实时GDP预测。","使用夜间灯光和NDVI卫星变量，结合XGBoost等非线性模型。","加入卫星数据后XGBoost的RMSE降低、R²提升，夜间灯光解释力强。","农业遥感与作物表型","可探索多源卫星数据融合与可解释AI，提升数据稀缺地区经济监测精度。","农业人工智能与决策模型","openalex","2026-09-23T23:30:35.177095Z",{"total":15,"page":57,"page_size":57,"items":58},1,[]]