摘要
由一名新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-机器学习的新数据在一份新的报告中提供。根据NewsRx记者来自印度西孟加拉邦的新闻报道，研究称：“目前的研究旨在确定河流的水文丰富度与河岸湿地生境和破碎化分析之间的关系。”八个相关参数，如水存在频率、水期和靠近河流的程度，为了研究湿地水文丰富度，将Bee N纳入了四个模型&两个统计模型（香农熵和逻辑回归）和两个机器学习模型（人工神经网络和随机森林）。我们的新闻编辑从Geogr Aphy系的研究中获得了一句话：“这些模型使用ROC曲线等统计技术进行评估，并进行现场验证。关于最佳PE回归模型(机器学习随机森林和统计模型逻辑回归)的信息对于理解所应用模型的预测能力很有价值。RF模型识别168.43 km2，110.91 km2，1990年、2000年、2010年和2020年分别为70.13 km2和39.15 km2，贫困地区和特贫困地区的比例从1990年的29.7%迅速上升到2020年的55.35%。摘要:分析了湿地破碎化与水文丰富度之间的关系。湿地破碎化和人为入侵导致的核心面积缩小对湿地水文丰富度有显著影响。该研究将为揭示湿地在过去的变化状态,特别是人类活动对水文丰富度的影响提供重要依据。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on Machine Learning are pre sented in a new report. According to news reporting originating from West Bengal , India, by NewsRx correspondents, research stated, "The current study aims to q uantify the relationship between hydrological richness in river and riparian wet land habitats and fragmentation analysis. Eight relevant parameters, such as the frequency of water presence, hydroperiod, and proximity to the river, have bee n incorporated into four models-two statistical models (Shannon entropy and Logi stic Regression) and two machine learning models (artificial neural network and random forest)-in order to investigate wetland hydrological richness." Our news editors obtained a quote from the research from the Department of Geogr aphy, "The models are evaluated using statistical techniques such as ROC curves, and field-based validation is also performed. The information about the best-pe rforming models (random forest for machine learning and logistic regression for statistical models) is valuable for understanding the predictive capabilities of the models applied. RF model identified 168.43 km2, 110.91 km2, 70.13 km2, and 39.15 km2 areas as having very rich and rich water richness zones in 1990, 2000, 2010, and 2020, respectively. The percentage of poor and very poor areas has ra pidly increased from 29.7% in 1990 to 55.35% in 2020 . Additionally, the relationship between wetland fragmentation and hydrological richness is assessed. Wetland fragmentation and shrinking core areas due to anth ropogenic intrusion significantly impact the hydrological richness of wetlands. This study will provide important insights into the changing state of wetlands o ver time, especially concerning the impact of anthropogenic activities on hydrol ogical richness."
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