Prediction of Total Nitrogen and Phosphorus in Surface Water by Deep Learning Methods Based on Multi-Scale Feature Extraction

A deep learning-based model for estimating pollution fluxes from rivers into the sea and its optimization

Pollution fluxes from rivers into the sea are currently the main source of pollutants in nearshore areas. Based on the source-sink process of the basin-estuary-coastal waters system, the pollution fluxes into the sea and their spatiotemporal heterogeneity were estimated. A deep learning-based model was established to simplify the estimation of pollution fluxes into the sea, with socio-economic drivers and meteorological data as input variables. A method for estimating the contribution rate of pollution fluxes from different spatial gradient was proposed. In this study, we found that (1) the pollution fluxes into the sea of total nitrogen (TN) and total phosphorus (TP) from the Bohai Sea Rim Basin (BSRB) in 1980, 1990, 2000, 2010, and 2020 were 25.38 × 104, 26.12 × 104, 27.27 × 104, 29.82 × 104, 25.31 × 104 and 1.32 × 104, 2.14 × 104, 2.09 × 104, 1.87 × 104, 1.68 × 104 tons, respectively. (2) The proportion of rural life and livestock to the TN was the highest, accounting for 39.18 % and 21.19 %, respectively. The proportion of livestock to the TP was the highest, accounting for 39.20 %, followed by rural life, accounting for 24.72 %. The results indicated that the pollution fluxes in the BSRB were related to human economic activities and relevant environmental protection measures. (3) The deep learning-based model established to estimate runoff pollution fluxes into the sea had the accuracy of over 90 %. (4) As for contribution rate, in terms of the elevation, the range of 0-100 m had the highest proportion, accounting for 39.65 %. The range of 50-100 km from the coastline had the highest proportion, accounting for 18.11 %. In terms of the district, coastal area has the highest proportion, accounting for 38.00 %. This study revealed the changing trends and driving mechanisms of pollution fluxes into the sea over the past 40 years and established a simplified deep learning-based model for estimating pollution fluxes into the sea. Then, we identified regions with high pollution contribution rate. The results can provide scientific references for the adaptive management of the nearshore areas based on the ecosystem.

Development of a basin-scale total nitrogen prediction model by integrating clustering and regression methods

Nutrient runoff into rivers caused by human activity has led to global eutrophication issues. The Nakdong River in South Korea is currently facing significant challenges related to eutrophication and harmful algal blooms, underscoring the critical importance of managing total nitrogen (T-N) levels. However, traditional methods of indoor analysis, which depend on sampling, are labor-intensive and face limitations in collecting high-frequency data. Despite advancements in sensor allowing for the measurement of various parameters, sensors still cannot directly measure T-N, necessitating surrogate regression methods. Therefore, we conducted T-N predictions using a water quality dataset collected from 2018 to 2022 at 157 observatories within the Nakdong River basin. To account for the water quality characteristics of each location, we employed a clustering technique to divide the basin and compared a Gaussian mixture model with K-means clustering. Moreover, optimal regressor for each cluster was selected by comparing multiple linear regression (MLR), random forest, and XGBoost. The results showed that forming four clusters via K-means clustering was the most suitable approach and MLR was reasonably accurate for all clusters. Subsequently, recursive feature elimination cross-validation was used to identify suitable parameters for T-N prediction, thus leading to the construction of high-accuracy T-N prediction models. Clustering was useful not only for improving the regressors but also for spatially analyzing the water quality characteristics of the Nakdong River. The MLR model can reveal causal relationships and thus is useful for decision-making. The results of this study revealed that the combination of a simple linear regression model and clustering method can be applied to a wide watershed. The clustering-based regression model showed potential for accurately predicting T-N at the basin level and is expected to contribute to nationwide water quality management through future applications in various fields.

Research progress in water quality prediction based on deep learning technology: a review

Water, an invaluable and non-renewable resource, plays an indispensable role in human survival and societal development. Accurate forecasting of water quality involves early identification of future pollutant concentrations and water quality indices, enabling evidence-based decision-making and targeted environmental interventions. The emergence of advanced computational technologies, particularly deep learning, has garnered considerable interest among researchers for applications in water quality prediction because of its robust data analytics capabilities. This article comprehensively reviews the deployment of deep learning methodologies in water quality forecasting, encompassing single-model and mixed-model approaches. Additionally, we delineate optimization strategies, data fusion techniques, and other factors influencing the efficacy of deep learning-based water quality prediction models, because understanding and mastering these factors are crucial for accurate water quality prediction. Although challenges such as data scarcity, long-term prediction accuracy, and limited deployments of large-scale models persist, future research aims to address these limitations by refining prediction algorithms, leveraging high-dimensional datasets, evaluating model performance, and broadening large-scale model application. These efforts contribute to precise water resource management and environmental conservation.

Parameter Identification of Lithium Battery Model Based on Chaotic Quantum Sparrow Search Algorithm

An accurate battery model is of great importance for battery state estimation. This study considers the parameter identification of a fractional-order model (FOM) of the battery, which can more realistically describe the reaction process of the cell and provide more precise predictions. Firstly, an improved sparrow search algorithm combined with the Tent chaotic mapping, quantum behavior strategy and Gaussian variation is proposed to regulate the early population quality, enhance its global search ability and avoid trapping into local optima. The effectiveness and superiority are verified by comparing the proposed chaotic quantum sparrow search algorithm (CQSSA) with the particle swarm optimization (PSO), genetic algorithm (GA), grey wolf optimization algorithm (GWO), Dingo optimization algorithm (DOA) and sparrow search algorithm (SSA) on benchmark functions. Secondly, the parameters of the FOM battery model are identified using six algorithms under the hybrid pulse power characterization (HPPC) test. Compared with SSA, CQSSA has 4.3%, 5.9% and 11.5% improvement in mean absolute error (MAE), root mean square error (RMSE) and maximum absolute error (MaAE), respectively. Furthermore, these parameters are used in the pulsed discharge test (PULSE) and urban dynamometer driving schedule (UDDS) test to verify the adaptability of the proposed algorithm. Simulation results show that the model parameters identified by the CQSSA algorithm perform well in terms of the MAE, RMSE and MaAE of the terminal voltages under all three different tests, demonstrating the high accuracy and good adaptability of the proposed algorithm.