Neural network model predictions for phosphorus management strategies on tile-drained organic soils

Abstract

The organic soils of Holland Marsh, Ontario are used for intensive vegetable production, which demands high-phosphorus (P) fertilizer applications. Such high-fertilizer applications on these tile-drained lands lead to eutrophication in surrounding water bodies. This study investigated the application of neural network (NN) models for deriving P management strategies. Seven NN models were assessed using the following two approaches: a time series with 1-year training and 1-year testing of the models and a randomization analysis where a random 80% of data were used for model training and the remainder for model testing. The feed-forward model using the randomization and the long-short-term memory model using time-series outperformed all other models. Two strategies for P management were evaluated: a direct approach that predicts P loads using new fertilizer rates or controlled drainage discharge rates and a particle swarm optimization (PSO) that used percent reduction of actual P loads to predict an optimal water table management strategy. Overall, the direct approach identified a water table level of 30 cm from the soil surface during the spring and 80 cm during the summer period as optimal to reduce P loads. The PSO analysis showed that a reduction of P loads by 20% in the spring and up to 40% in the summer through water table control would not compromise crop production.

One hundred years of drainage development in the Holland Marsh, Canada, and implications for long‐term sustainability

“…Many have promoted the use of water table management via water control structures on tile‐drained mineral soils to reduce N and P pollution (Cooke & Verma, 2012; King et al, 2022; Skaggs et al, 2012; Tan et al, 1998). Research by Grenon et al (2022) has shown that water table control on the tile‐drained organic soils of the Holland Marsh can also be effective in reducing P loads from tile drain outlets. They found that a reduction of 20% in the spring TP load can be achieved by raising the control gate level to 30 cm from the soil surface.…”

Prediction of the river water environment carrying capacity using LSTM networks

“…Forecasting results using the LSTM model have shown darker results than other methods (Hayder et al 2022). In addition to hydrological forecasting, LSTM models are also applied to predict substance concentrations (Grenon et al 2022), wastewater flow control (Seshan et al 2024), and precipitation (Alqahtani 2024).…”