解决干旱地区的缺水挑战:一种新的方法,采用基于人类的元启发学和机器学习算法来绘制地下水潜力的地图
Seyed Vahid Razavi-Termeh1, Abolghasem Sadeghi-Niaraki1, Farbod Farhangi2
1Dept. of Computer Science & Engineering and Convergence Engineering for Intelligent Drone, XR Research Center, Sejong University, Seoul, Republic of Korea.
Chemosphere
|July 18, 2024
概括
这项研究通过使用优化的随机森林 (RF) 模型,增强了干旱地区的地下水潜力绘制 (GPM). 俄罗斯帝国主义竞争算法 (ICA) 模型实现了最高的准确性,为水资源短缺挑战提供了强大的解决方案.
科学领域:
- 水文和水资源管理 水文和水资源管理
- 地理空间分析的研究.
- 环境科学中的人工智能
背景情况:
- 在干旱地区,缺水是一个关键问题,需要准确地绘制地下水潜力 (GPM).
- 传统的GPM方法往往缺乏有效水资源管理所需的精度.
- 先进的建模技术对于提高GPM的准确性和可靠性至关重要.
研究的目的:
- 评估和提高干旱环境中的地下水潜力测绘 (GPM) 的精度.
- 通过将随机森林 (RF) 算法与元启发优化技术集成,引入和评估一种创新的GPM方法.
- 为了比较针对GPM的非优化射频模型的优化射频模型的性能.
主要方法:
- 一个包含16个影响因素和175个井数据点的空间数据库被编制为伊朗Zayandeh Rood流域.
- 随机森林 (RF) 算法通过整合三个受人类行为启发的元启发算法来增强:帝国主义竞争算法 (ICA),基于教学的优化 (TLBO) 和基于学生心理学的优化 (SPBO).
- 数据预处理涉及多对线性测试和频率比 (FR) 技术,随后使用统计指标 (RMSE,MAE,AUC) 和统计测试 (弗里德曼,奇平方,威尔科森) 进行模型评估.
主要成果:
- 该RF-ICA模型表现出卓越的性能,曲线下的面积 (AUC) 为0.865,明显优于其他模型.
- 该RF-SPBO模型也显示出强的结果,AUC为0.842,表明有效的分类准确度.
- 射频TLBO (AUC=0.813) 和非优化射频模型 (AUC=0.810) 的性能相似,但更低,突出了元启发式优化的好处.
结论:
- 优化随机森林模型,特别是RF-ICA和RF-SPBO,在干旱地区提供高度准确和可靠的地下水潜力测绘.
- 这些先进的建模方法为应对水资源短缺挑战提供了一个强大的框架,并为水资源管理政策提供了信息.
- 将元启发算法与机器学习集成为提高GPM精度和决策提供了一个有希望的方向.
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