
Research Square (Research Square), Год журнала: 2024, Номер unknown
Опубликована: Дек. 16, 2024
Язык: Английский
Research Square (Research Square), Год журнала: 2024, Номер unknown
Опубликована: Дек. 16, 2024
Язык: Английский
Journal of Environmental Management, Год журнала: 2024, Номер 352, С. 120091 - 120091
Опубликована: Янв. 15, 2024
Water is a vital resource supporting broad spectrum of ecosystems and human activities. The quality river water has declined in recent years due to the discharge hazardous materials toxins. Deep learning machine have gained significant attention for analysing time-series data. However, these methods often suffer from high complexity forecasting errors, primarily non-linear datasets hyperparameter settings. To address challenges, we developed an innovative HDTO-DeepAR approach predicting indicators. This proposed compared with standalone algorithms, including DeepAR, BiLSTM, GRU XGBoost, using performance metrics such as MAE, MSE, MAPE, NSE. NSE hybrid ranges between 0.8 0.96. Given value's proximity 1, model appears be efficient. PICP values (ranging 95% 98%) indicate that highly reliable Experimental results reveal close resemblance model's predictions actual values, providing valuable insights future trends. comparative study shows suggested surpasses all existing, well-known models.
Язык: Английский
Процитировано
12Scientific Reports, Год журнала: 2025, Номер 15(1)
Опубликована: Фев. 27, 2025
The Chi and Mun River Basins, the primary tributary of Mekong Basin in Thailand, is undergoing significant land use changes that impact water quality. Understanding relationship between quality crucial for effective river basin management, providing insights applicable to global systems. While past studies have examined Basin, research specifically focusing on Chi-Mun remains limited. This study analyzes spatial temporal effects from 2007 2021 using change estimation, 11 parameters, redundancy analysis (RDA). Water samples were collected January, March, May, August across multiple years. Seasonal variations assessed, with dry season January March wet May August. Key findings include: (1) pH, Biochemical Oxygen Demand, Total Coliform Bacteria, Fecal Phosphorus, Nitrate Nitrogen, Ammonia-Nitrogen, Suspended Solids increased during season, while (2) Dissolved Oxygen, Electrical Conductivity, Quality Index higher season. (3) Land had a greater driven by runoff expanding urban agricultural areas declining paddy forest cover. (4) Forests aquatic improved quality, expansion contributed its deterioration. These underscore need sustainable management strategies balance regional development ecological conservation Basin.
Язык: Английский
Процитировано
1Heliyon, Год журнала: 2024, Номер 10(13), С. e33695 - e33695
Опубликована: Июнь 28, 2024
The water quality index (WQI) is a widely used tool for comprehensive assessment of river environments. However, its calculation involves numerous parameters, making sample collection and laboratory analysis time-consuming costly. This study aimed to identify key parameters the most reliable prediction models that could provide maximum accuracy using minimal indicators. Water from 2020 2023 were collected including nine biophysical chemical indicators in seventeen rivers Yancheng Nantong, two coastal cities Jiangsu Province, China, adjacent Yellow Sea. Linear regression seven machine learning (Artificial Neural Network (ANN), Self-Organizing Maps (SOM), K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Random Forest (RF), Extreme Gradient Boosting (XGB) Stochastic (SGB)) developed predict WQI different groups input variables based on correlation analysis. results indicated improved 2022 but deteriorated 2023, with inland stations exhibiting better conditions than ones, particularly terms turbidity nutrients. environment was comparatively Nantong Yancheng, mean values approximately 55.3–72.0 56.4–67.3, respectively. classifications "Good" "Medium" accounted 80 % records, no instances "Excellent" 2 classified as "Bad". performance all models, except SOM, addition variables, achieving R2 higher 0.99 such SVM, RF, XGB, SGB. RF XGB total phosphorus (TP), ammonia nitrogen (AN), dissolved oxygen (DO) (R2 = 0.98 0.91 training testing phase) predicting values, TP AN (accuracy 85 %) grades. "Low" grades highest at 90 %, followed by level 70 %. model contribute efficient evaluation identifying facilitating effective management basins.
Язык: Английский
Процитировано
9Ecological Indicators, Год журнала: 2024, Номер 166, С. 112423 - 112423
Опубликована: Июль 29, 2024
Diverse land use patterns exhibit varying effects on water quality across different seasons and spatial scales. However, current studies the correlation between in single small-scale basins no longer meet needs of regional coordinated development. Simultaneous comparative analysis multiple large-scale can promote environmental protection basins, but there is currently limited relevant research. In this study, data from 86 sampling points seven major river China were analyzed. Multivariate statistical redundancy (RDA) employed to investigate influence at The results indicated notable differences various locations. Except for higher pH permanganate index (COD) concentrations wet season Songhua River Basin COD Pearl Basin, all parameters other are dry season. PH exhibited considerable variations within while dissolved oxygen (DO) ammonia nitrogen (NH4+-N) showed smaller variations. RDA that had a more pronounced effect during Yangtze, Liao Basins, impact was greater four Yellow, Huai, Hai Basins. At scale, 2000 m buffer zone most significant 1000 greatest Huai For Yellow 500 season, respectively. research findings offer scientific foundation development basin-specific management policies measures multi-scale perspective.
Язык: Английский
Процитировано
5Journal of Environmental Management, Год журнала: 2025, Номер 380, С. 124960 - 124960
Опубликована: Март 15, 2025
Язык: Английский
Процитировано
0Ceramics International, Год журнала: 2024, Номер unknown
Опубликована: Дек. 1, 2024
Язык: Английский
Процитировано
2Stochastic Environmental Research and Risk Assessment, Год журнала: 2024, Номер 38(8), С. 3007 - 3030
Опубликована: Май 18, 2024
Язык: Английский
Процитировано
1Environmental Monitoring and Assessment, Год журнала: 2024, Номер 196(7)
Опубликована: Июнь 17, 2024
Язык: Английский
Процитировано
1Journal of Hydrology Regional Studies, Год журнала: 2024, Номер 56, С. 102040 - 102040
Опубликована: Ноя. 1, 2024
Язык: Английский
Процитировано
0Research Square (Research Square), Год журнала: 2024, Номер unknown
Опубликована: Дек. 16, 2024
Язык: Английский
Процитировано
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