基于海鸥优化决策树的模糊排序算法短期/长期降雨预测

A. A., Latha C. A.
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引用次数: 0

摘要

准确的降雨预报是农业从属国评估作物生产力、水资源利用和水资产预规划的主要挑战。此外,由于气候性质的不同,降雨预测系统不能很好地进行短期和长期降雨预测。因此,为了提高短期和长期降雨预测的准确性,在该方法中使用了混合机器学习技术。首先,我们提出了模糊排序算法来选择最优的特征子集。利用所选择的特征,通过提出优化的决策树(DT)来预测短期和长期降雨量。采用海鸥优化算法(SOA)对决策节点或DT的上层进行优化选择。结果表明,本文提出的降雨预测模型比现有的预测模型具有更好的精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fuzzy Ranking Algorithm with Seagull Optimization-Based Decision Tree for Short-Term/Long-Term Rainfall Prediction
An exact rainfall prediction is a major challenge for agriculture subordinate nations for evaluating the productivity of crop, utilization of water resources and preplanning of water assets. Besides, because of different climate nature, rainfall prediction system cannot execute well for short-term and long-term rainfall prediction. Thus, to enhance the accuracy of short-term and long-term rainfall prediction, hybrid machine learning techniques are used in this approach. At first, we present fuzzy ranking algorithm to select the optimal subset of features. Using the selected features, short-term and long-term rainfalls are predicted by presenting optimized Decision Tree (DT). The decision node or upper level of the DT is chosen optimally using seagull optimization algorithm (SOA). Results of the article prove that the proposed rainfall prediction model obtains better accuracy than the existing prediction models.
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