结合长时序遥感监测和机器学习算法预测藻类增殖风险时空变化

饮用水源地藻类增殖监测和预测对于改善水生态系统环境和保护人类健康具有重要意义。利用多源遥感数据能够获取高时空分辨率的藻类动态信息,结合长时序遥感监测与机器学习算法能够适应藻类生长复杂的影响机制和非线性特征,实现藻类增殖风险时空变化的预测。本文利用Landsat与MODIS长时间序列卫星遥感数据,采用FAI与NDVI两种方法提取2000—2020年丹江口水库藻类浓度的时空变化信息,在此基础上分析藻类增殖对气象因子(气温、气压、相对湿度、风速和累计日照时间)的时间滞后效应。利用支持向量机、朴素贝叶斯与随机森林3种机器学习算法预测藻类增殖风险,并对3种算法的预测性能进行评价和对比。结果表明:丹江口水库藻类浓度年际变化呈现出先增后降的趋势,呈现出明显的季节性周期变化,春末夏初是藻类快速增殖时期。空间上入库支流和库湾等局部地区藻类浓度相对较高,为藻类增殖高风险区,丹江口水库藻类增殖风险预测模型能够较为准确地确定藻类增殖高风险区位置并反映短期内的空间变化情况,3种算法的预测结果呈现出整体上的一致性,其中支持向量机与朴素贝叶斯算法表现出更高的精度,提前4～5天是最佳预测时间。

Combining long-term remote sensing monitoring and machine learning algorithms to predict spatiotemporal changes in algal proliferation risk

Monitoring and predicting algal proliferation in drinking water sources holds significant importance for enhancing the environmental integrity of aquatic ecosystems and safeguarding human health. Utilizing multisource remote sensing data enables the acquisition of high spatiotemporal resolution information regarding algal dynamics. The integration of long-term remote sensing monitoring with machine learning algorithms allows for the adaptation to the complex growth mechanisms and nonlinear characteristics associated with algal proliferation, thereby facilitating the prediction of spatiotemporal variations in algal proliferation risk. In this study, Landsat and MODIS long time-series satellite remote sensing data were employed to extract spatiotemporal variations in algal content in the Danjiangkou Reservoir from 2000 to 2020, utilizing the fraction of algal inversion and normalized difference vegetation index methods. Furthermore, the time lag effects of meteorological factors, including temperature, atmospheric pressure, relative humidity, wind speed, and cumulative sunshine duration, on algal proliferation were analyzed. Three machine learning algorithms, namely Support Vector Machine, Naïve Bayes, and Random Forest, were deployed to predict algal proliferation risk. The predictive performance of these algorithms was evaluated and compared. The results indicated that the annual variation in algal content in the Danjiangkou Reservoir exhibited a trend of initial increase followed by a decrease, characterized by distinct seasonal periodic fluctuations, with late spring to early summer representing a period of rapid algal proliferation. Spatially, relatively higher algal content was observed in inflow tributaries and bay areas, indicating high-risk zones for algal proliferation. The algal proliferation risk prediction model for the Danjiangkou Reservoir accurately identified high-risk areas for algal proliferation and reflected short-term spatial variations. The predictions from the three algorithms exhibited overall consistency, with Support Vector Machine and Naïve Bayes algorithms demonstrating higher accuracy, and a lead time of 4 to 5 days was identified as the optimal prediction window.