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通过开发基于人工智能的概率框架,绘制气候适合性指数,用于雨水培养药用植物

Sina Sadeghfam¹, Mohammad Sina Rahmani², Marjan Moazamnia³

¹Department of Civil Engineering, Faculty of Engineering, University of Maragheh, P.O. Box 55136-553, Maragheh, East Azerbaijan, Iran. s.sadeghfam@maragheh.ac.ir.

Scientific reports

|September 2, 2024

概括

这项研究引入了一个新的气候适应性指数 (CSI) 框架,使用频率分析和粒子群优化 (PSO) 来确定种植药用植物的最佳区域,减少农业规划中的主观性.

科学领域:

农业科学农业科学
环境科学环境科学
数据科学数据科学数据科学

背景情况:

气候适应性评估对于农业效率至关重要.
现有的框架往往依赖于主观的专家判断.
在雨水下种植药用植物需要精确的气候适应性分析.

研究的目的:

开发一个概率框架来计算气候适应性指数 (CSI).
根据气候数据,确定12种药用植物的高潜力种植区.
通过使用先进的计算方法减少适合性评估的主观性.

主要方法:

利用频率分析计算每个工厂的降水和温度发生概率.
用于无监督聚类和CSI量化而使用的粒子群集优化 (PSO).
综合地理信息系统 (GIS) 流程用于气候数据的空间插值.

主要成果:

开发了一个新的,不那么主观的CSI计算框架.
确定了在乌尔米亚湖流域中种植药用植物的高CSI值的特定集群.
证明了该框架在面临水资源管理挑战的地区的适用性.

结论:

概率CSI框架有助于识别合适的种植面积.
通过利用CSI洞察力,可以实现优化种植模式.
这种方法支持增加农业产量,降低成本,提高农民和管理人员的利能力.

关键词:

农业气候 - 农业气候频率分析频率分析乌尔米亚湖是乌尔米亚湖的一部分.粒子小群优化优化粒子小群优化雨水农业雨水农业

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