Agricultural Non-Point Source Pollution: Comprehensive Analysis of Sources and Assessment Methods

Agriculture, Journal Year: 2025, Volume and Issue: 15(5), P. 531 - 531

Published: Feb. 28, 2025

Agricultural non-point source pollution (ANPSP) significantly affects worldwide water quality, soil integrity, and ecosystems. Primary factors are nutrient runoff, pesticide leaching, inadequate livestock waste management. Nonetheless, a thorough assessment of ANPSP sources efficient control techniques is still lacking. This research delineates the origins present state ANPSP, emphasizing its influence on agricultural practices, livestock, rural It assesses current evaluation models, encompassing field- watershed-scale methodologies, investigates novel technologies such as Artificial Intelligence (AI), Machine Learning (ML), Internet Things (IoT) that possess potential to enhance monitoring predictive precision. The examines strategies designed alleviate sustainable fertilizer reduction, management technology, highlighting necessity for integrated, real-time systems. report presents comprehensive analysis tactics, finds significant gaps, offers recommendations enhancing both policy initiatives tackle foster farming practices.

Language: Английский

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A novel interval decomposition correlation particle swarm optimization-extreme learning machine model for short-term and long-term water quality prediction

Journal of Hydrology, Journal Year: 2023, Volume and Issue: 625, P. 130034 - 130034

Published: Aug. 11, 2023

Language: Английский

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A unified deep learning framework for water quality prediction based on time-frequency feature extraction and data feature enhancement

Journal of Environmental Management, Journal Year: 2023, Volume and Issue: 351, P. 119894 - 119894

Published: Dec. 27, 2023

Language: Английский

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A state-of-the-art review of long short-term memory models with applications in hydrology and water resources

Applied Soft Computing, Journal Year: 2024, Volume and Issue: unknown, P. 112352 - 112352

Published: Oct. 1, 2024

Language: Английский

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Spatiotemporal estimation of groundwater and surface water conditions by integrating deep learning and physics-based watershed models

Water Research X, Journal Year: 2024, Volume and Issue: 23, P. 100228 - 100228

Published: May 1, 2024

The impacts of climate change on hydrology underscore the urgency understanding watershed hydrological patterns for sustainable water resource management. conventional physics-based fully distributed models are limited due to computational demands, particularly in case large-scale watersheds. Deep learning (DL) offers a promising solution handling large datasets and extracting intricate data relationships. Here, we propose DL modeling framework, incorporating convolutional neural networks (CNNs) efficiently replicate model outputs at high spatial resolution. goal was estimate groundwater head surface depth Sabgyo Stream Watershed, South Korea. consisted input variables, including elevation, land cover, soil type, evapotranspiration, rainfall, initial conditions. conditions target were obtained from HydroGeoSphere (HGS), whereas other inputs actual measurements field. By optimizing training sample size, design, CNN structure, hyperparameters, found that CNNs with residual architectures (ResNets) yielded superior performance. optimal reduces computation time by 45 times compared HGS monthly estimations over five years (RMSE 2.35 0.29 m water, respectively). In addition, our framework explored predictive capabilities responses future scenarios. Although proposed is cost-effective simulations, further enhancements needed improve accuracy long-term predictions. Ultimately, has potential facilitate decision-making, complex

Language: Английский

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Exploring the Impact of Land Use Scales on Water Quality Based on the Random Forest Model: A Case Study of the Shaying River Basin, China

Water, Journal Year: 2024, Volume and Issue: 16(3), P. 420 - 420

Published: Jan. 27, 2024

Optimizing the land use structure is one of most effective means improving surface water aquatic environment. The relationship between patterns and quality complex due to influence dams sluices. To further investigate impact on in different basins, we Shaying River as an example, which a typical tributary Huai Basin. Utilizing 2020 data monitoring from two periods, this study employs GIS spatial analysis, Random Forest Model, redundancy Partial Least-Squares Regression quantitatively explore how different-scale buffer zone quality. key findings include: (1) notable seasonal differences indicators within basin. Water Quality Index (WQI) significantly better non-flood season compared flood season, with deteriorating towards lower reaches. Key affecting include dissolved oxygen (DO), ammonia nitrogen (NH3-N), total phosphorus (TP), turbidity (Tur) NH3-N, permanganate index (CODMn), electrical conductivity (EC) season. (2) Cultivated construction are main uses sub-basin was identified scale for River. (3) (PLSR) analysis revealed that cultivated land, grass primary types influencing changes, PLSR model during lands show positive correlation indicators, while forest bodies, grasslands correlate positively DO negatively other indicators. underscores rational planning crucial enhancing

Language: Английский

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Bidirectional Long Short-Term Memory (BILSTM) - Support Vector Machine: A new machine learning model for predicting water quality parameters

Ain Shams Engineering Journal, Journal Year: 2023, Volume and Issue: 15(3), P. 102510 - 102510

Published: Oct. 10, 2023

Water pollution threatens human health, agriculture, and ecosystems. Accurate prediction of water quality parameters is crucial for effective protection. We suggest a novel hybrid deep learning model that enhances the efficiency Support Vector Machines (SVMs) in predicting Electrical Conductivity (EC) Total Dissolved Solids (TDS). Our combines Bidirectional Long Short-Term Memory (BILSTM) SVMs to extract essential features predict output variables. evaluated models using input (PH, Ca++, Mg++, Na+, K+, HCO3, SO4, Cl) one, two, three-day predictions. Employing Ali Baba Forty Thieves (AFT) optimization algorithm, we identified optimal combinations. The BILSTM-SVM accurately estimated TDS values, with MAPE values 2%, outperforming other models. Similarly, it successfully predicted EC exhibiting an R2 value 0.94. proposed processes complex relationships captures from data, contributing improved prediction.

Language: Английский

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Recent Advances in Surface Water Quality Prediction Using Artificial Intelligence Models

Water Resources Management, Journal Year: 2023, Volume and Issue: 38(1), P. 235 - 250

Published: Nov. 30, 2023

Language: Английский

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Hybrid deep learning based prediction for water quality of plain watershed

Environmental Research, Journal Year: 2024, Volume and Issue: 262, P. 119911 - 119911

Published: Sept. 2, 2024

Language: Английский

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Using Artificial Intelligence and Deep Learning Algorithms to Extract Land Features from High-Resolution Pléiades Data

Journal of the Indian Society of Remote Sensing, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 2, 2025

Language: Английский

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