[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2427":3},{"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":24,"tags":26,"view_count":32,"doi":33,"paper":34,"created_at":73},2427,"Machine learning based precipitation modeling using multi satellite data for climate resilient water resource management in Bundelkhand India","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44274-026-01047-x","Precipitation modeling can be improved by using spatially continuous climate data from satellite remote sensing; nevertheless, incorporating heterogeneous sensor-derived variables and understanding machine learning results continue to be significant hurdles. In this work, a multi-source climatic parameter-based satellite-driven machine learning system for precipitation prediction is presented. The dependent variable was precipitation, and the model inputs were satellite-derived predictors such as land surface temperature, atmospheric moisture, surface pressure, wind speed, relative humidity, soil wetness, and temporal indicators. Convolutional Neural Networks (CNN) and Extreme Gradient Boosting (XGBoost), two sophisticated machine learning models, were used to capture multiscale and nonlinear interactions between precipitation and climate factors. Standard statistical measures were used to evaluate the model's performance, and explicable machine learning methods were used to determine the relative significance of the input variables. The findings suggest that both models make good use of satellite-derived climate data, with CNN demonstrating a great capacity to learn intricate feature interactions and XGBoost demonstrating strong predictive ability. With R2 = 0.77, RMSE = 88.79, as well as MAE = 42.06, XGBoost scored far superior than CNN (R2 = 0.60, RMSE = 120.82, MAE = 73.46). According to the interpretability analysis, the main factors influencing precipitation variability are soil wetness, land surface temperature, and atmospheric moisture. The suggested system supports enhanced hydrological forecasting as well as sustainable water resource management by providing a transparent and scalable method for precipitation prediction, especially in areas with limited data. Overall, the findings show that a scalable and efficient framework for precipitation prediction in semi-arid, data-poor areas may be created by combining interpretable machine learning with multi-source Earth observation data. Graphical Abstract","利用卫星遥感提供的空间连续气候数据可以改进降水建模；然而，整合来自不同传感器的异构变量以及理解机器学习结果仍然是重大难题。本研究提出了一种基于多源气候参数的卫星驱动机器学习降水预测系统。因变量为降水，模型输入为卫星衍生的预测因子，包括地表温度、大气湿度、地表气压、风速、相对湿度、土壤湿度和时间指标。研究采用了两种先进的机器学习模型——卷积神经网络（CNN）和极端梯度提升（XGBoost），以捕捉降水与气候因子之间的多尺度和非线性交互作用。使用标准统计指标评估模型性能，并采用可解释机器学习方法确定输入变量的相对重要性。研究结果表明，两种模型均能有效利用卫星衍生的气候数据，其中CNN展现出学习复杂特征交互的强大能力，XGBoost则表现出强劲的预测性能。XGBoost的R² = 0.77、RMSE = 88.79、MAE = 42.06，显著优于CNN（R² = 0.60、RMSE = 120.82、MAE = 73.46）。可解释性分析表明，影响降水变率的主要因素为土壤湿度、地表温度和大气湿度。所提出的系统为降水预测提供了一种透明且可扩展的方法，有助于增强水文预报和可持续水资源管理，尤其在数据稀缺地区。总体而言，研究结果表明，将可解释机器学习与多源地球观测数据相结合，可为半干旱、数据匮乏地区构建一个可扩展且高效的降水预测框架。图形摘要",null,"Discover Environment","2026-09-11T00:00:00Z","论文",10,false,70,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,20,17,13,8,1,"基于多源卫星遥感与可解释机器学习构建降水预测框架，方法对比与结论可靠，对半干旱缺数据区水资源管理有参考价值，但属区域性学术成果，公共影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"农业人工智能","遥感","气候适应","水资源管理","降水预测",0,"10.1007\u002Fs44274-026-01047-x",{"doi":33,"openalex_id":35,"authors":36,"venue":10,"cited_by_count":32,"oa_url":65,"card":66,"direction":70,"ingested_from":72},"W7212224443",[37,40,42,45,47,50,52,55,57,60,62],{"name":38,"orcid":39},"Pavan Kumar","https:\u002F\u002Forcid.org\u002F0000-0003-3653-8163",{"name":41,"orcid":9},"Megha Paul",{"name":43,"orcid":44},"Prashant K. Srivastava","https:\u002F\u002Forcid.org\u002F0000-0002-4155-630X",{"name":46,"orcid":9},"Manmohan Dobriyal",{"name":48,"orcid":49},"Yogeshwar Singh","https:\u002F\u002Forcid.org\u002F0000-0002-3324-9289",{"name":51,"orcid":9},"Manish Srivastav",{"name":53,"orcid":54},"Ajay Singh","https:\u002F\u002Forcid.org\u002F0000-0003-2933-4058",{"name":56,"orcid":9},"Abu Salim",{"name":58,"orcid":59},"Shams Tabrez Siddiqui","https:\u002F\u002Forcid.org\u002F0000-0002-6567-3383",{"name":61,"orcid":9},"Aasif Aftab",{"name":63,"orcid":64},"Benson Turyasingura","https:\u002F\u002Forcid.org\u002F0000-0003-1325-4483","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44274-026-01047-x.pdf",{"tldr":67,"method":68,"finding":69,"direction":70,"opportunity":71},"用多源卫星数据和机器学习预测印度半干旱区降水，XGBoost优于CNN。","CNN与XGBoost，输入LST、湿度、风速等卫星变量，可解释性分析。","XGBoost预测最佳（R²=0.77），土壤湿度、地表温度、大气水汽最关键。","农业遥感与作物表型","可迁移至其他数据稀缺区，融合多源遥感与可解释AI提升水文预报。","openalex","2026-09-14T23:30:25.935081Z"]