Development of a Runoff Pollution Empirical Model and Pollution Machine Learning Models of the Paddy Field in the Taihu Lake Basin Based on the Paddy In Situ Observation Method

Abstract

Agricultural non-point source (NPS) pollution has become a prominent problem for China’s water quality. Paddy fields pose a high risk of pollution to surrounding water bodies. The paddy in situ observation method (PIOM) can calculate the runoff pollution load of paddy fields in situ without changing the original runoff characteristics and agricultural water management measures. In this study, we carried out multisite field experiments during the rice growing period in the Taihu Lake basin and calculated the runoff pollution loads. Then, we developed a runoff pollution empirical model (RPEM) and runoff pollution machine learning models of paddy fields. Based on the PIOM, the average runoff volume was 342.1 mm, and the runoff pollution loads mainly occurred in the early-stage seedling and tillering stages. The mean TN, NH4+-N, TP and CODMn loads of paddy fields were 10.28, 3.35, 1.17 and 23.49 kg·ha−1, respectively. The mean N and P fertilizer loss rates were 4.09 and 1.95%, respectively. The RPEM mainly included the runoff model and surface water concentration model of paddy fields, the performance of which was validated based on the PIOM. The irrigation and runoff volumes of Zhoutie paddy (ZT) and Heqiao paddy (HQ) analyzed by RPEM and PIOM had an absolute difference of 1.2~3.1%. With the exception of the difference in CODMn loads of ZT, the absolute differences in TN, NH4+-N, TP and CODMn loads of ZT and HQ measured by two methods were less than 20%. This result illustrates the accuracy and feasibility of the RPEM for analysis of the water balance and runoff pollution loads of paddy fields. Based on 114 field runoff pollution datasets, RF provided the best machine learning model for TN, NH4+-N and TP, and SVM was the best model for CODMn. The training set R2 values of the best models for TN, NH4+-N and CODMn were above 0.8, and the testing set R2 values of the best models were above 0.7. The runoff pollution RF and SVM models can support the calculation and quantitative management of paddy field pollution load. This study provides a theoretical basis and technical support for the quantification of runoff pollution load and the formulation of pollution control measures in the Taihu Lake basin.