Modeling Multi-Event Non-Point Source Pollution in a Data-Scarce Catchment Using ANN and Entropy Analysis

Chen, Lei; Cheng, Sun; Wang, Guobo; Xie, Hui; Shen, Zhenyao

doi:10.3390/e19060265

Cited by 11 publications

(2 citation statements)

References 38 publications

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“…The inaccuracy of the time series can be accounted for by the type of NN model. The FNN models need more data for training (Chen et al 2017). The time-series analysis, however, had a higher percentage of data points used for model testing, compared to the randomization analysis.…”

Section: Fnn Model Analysesmentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Fnn Model Analysesmentioning

confidence: 99%

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

et al. 2022

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“…In this study, precipitation amount was used to classify rainfall-runoff events to be different types in previous research. This solution is computationally simple and straightforward but intended to facilitate streamflow forecasting and risk management [38,[41][42][43][44]. The specific criterion is presented in Table 1.…”

Section: Study Site and Data Collectionmentioning

confidence: 99%

Parameter Estimation and Uncertainty Analysis: A Comparison between Continuous and Event-Based Modeling of Streamflow Based on the Hydrological Simulation Program–Fortran (HSPF) Model

Xie

Shen

Chen

et al. 2019

Water

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Hydrologic modeling is usually applied to two scenarios: continuous and event-based modeling, between which hydrologists often neglect the significant differences in model application. In this study, a comparison-based procedure concerning parameter estimation and uncertainty analysis is presented based on the Hydrological Simulation Program–Fortran (HSPF) model. Calibrated parameters related to base flow and moisture distribution showed marked differences between the continuous and event-based modeling. Results of the regionalized sensitivity analysis identified event-dependent parameters and showed that gravity drainage and storage outflow were the primary runoff generation processes for both scenarios. The overall performance of the event-based simulation was better than that of the daily simulation for streamflow based on the generalized likelihood uncertainty estimation (GLUE). The GLUE analysis also indicated that the performance of the continuous model was limited by several extreme events and low flows. In the event-based scenario, the HSPF model performances decreased as the precipitation became intense in the event-based modeling. The structure error of the HSFP model was recognized at the initial phase of the rainfall-event period. This study presents a valuable opportunity to understand dominant controls in different hydrologic scenario and guide the application of the HSPF model.

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A Comparative Analysis of Data-Driven Models (SVR, ANFIS, and ANNs) for Daily Karst Spring Discharge Prediction

Rahbar

Mirarabi

Nakhaei

et al. 2022

Water Resour Manage

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Modeling Multi-Event Non-Point Source Pollution in a Data-Scarce Catchment Using ANN and Entropy Analysis

Cited by 11 publications

References 38 publications

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

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

Parameter Estimation and Uncertainty Analysis: A Comparison between Continuous and Event-Based Modeling of Streamflow Based on the Hydrological Simulation Program–Fortran (HSPF) Model

A Comparative Analysis of Data-Driven Models (SVR, ANFIS, and ANNs) for Daily Karst Spring Discharge Prediction

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