摘要
由机器人与机器学习每日新闻的新闻记者兼新闻编辑-研究人员详细介绍了人工智能的新数据。根据News Rx记者来自伊拉克苏莱曼尼亚的新闻，研究表明："文摘：水文建模是可持续水资源管理中最复杂的任务之一，特别是在预测降雨方面。"新闻记者引用苏来玛尼大学的一篇研究文章:“降雨预测是建设可持续社会的关键,从水电运行、农业规划和防洪等方面考虑,本文提出了一个基于k近邻(KNN)、XGBoos t(XGB)、决策树(DCT)的混合模型。”在澳大利亚悉尼机场首次开发并实现了随机森林(RF),以日降雨量、温度、蒸发量、湿度为输入参数,采用均方根误差(RMSE)、确定系数(2)、平均绝对误差(MAE)、并利用归一化均方根误差(NRMSE)检验模型的精度。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in artific ial intelligence. According to news originating from Sulaymaniyah, Iraq, by News Rx correspondents, research stated, “ABSTRACT: Hydrological modeling is one of t he most complicated tasks in sustainable water resources management, particularl y in terms of predicting rainfall.” The news correspondents obtained a quote from the research from University of Su laimani: “Predicting rainfall is critical to build a sustainable society in term s of hydropower operations, agricultural planning, and flood control. In this st udy, a hybrid model based on the integration of k-nearest neighbor (KNN), XGBoos t (XGB), decision tree (DCT), and Random Forest (RF) has been developed and impl emented for forecasting daily rainfall for the first time at Sydney airport, Aus tralia. Daily rainfall, temperature, evaporation, and humidity have been selecte d as input parameters. Three statistical measurements, namely, root mean square error (RMSE), Coefficient of determination (R2), mean absolute error (MAE), and Normalized Root Mean Square Error (NRMSE) have been utilized in order to check t he accuracy of the proposed model.”
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