Impact of Land Use Change on Seasonal Water Quality, Case Study in Chi-Mun River Basin in Thailand DOI Creative Commons
Kwanchai Pakoksung, Nantawoot Inseeyong,

Nattawin Chawaloesphonsiya

и другие.

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Дек. 16, 2024

Abstract This study investigates the correlation between land use changes and water quality in Chi-Mun River Basin, Thailand, from 2007 to 2021. It is first of its kind region Mekong providing critical insights for global river basin management. The research analyzes spatial temporal their multi-scale impacts on quality, utilizing change estimation, index analysis, redundancy analysis (RDA). results showed that stream variables displayed highly variations, with pH, Biochemical Oxygen Demand (BOD), Total Coliform Bacteria (TCB), Fecal (FCB), Phosphorus (TP), Nitrate Nitrogen (NO3-N), Ammonia-nitrogen (NH3-N), Suspended Solids (SS) all generally displaying higher levels wet season, while there were concentrations Dissolved (DO), Electrical Conductivity (EC), Water Quality Index (WQI) dry season. samples collected once January, March, May, August 2024. season represented May August, January March. total contribution patterns overall was stronger during shows a decline paddy forest areas alongside an expansion urban, agricultural, aqua agricultural land. significant seasonal forests bodies contributing purification, urban degraded quality. findings offer recommendations protection management policies align basin’s natural socio-economic characteristics, promoting coordinated regional development.

Язык: Английский

HDTO-DeepAR: A novel hybrid approach to forecast surface water quality indicators DOI Creative Commons
Rosysmita Bikram Singh, Kanhu Charan Patra, Biswajeet Pradhan

и другие.

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

12
Seasonal dynamics of water quality in response to land use changes in the Chi and Mun River Basins Thailand DOI Creative Commons
Kwanchai Pakoksung, Nantawoot Inseeyong,

Nattawin Chawaloesphonsiya

и другие.

Scientific 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

1
Assessing and predicting water quality index with key water parameters by machine learning models in coastal cities, China DOI Creative Commons

Jing Xu,

Yuming Mo,

Senlin Zhu

и другие.

Heliyon, 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

9
Effects of land use patterns on seasonal water quality in Chinese basins at multiple temporal and spatial scales DOI Creative Commons

Xinchen Yao,

Chunfen Zeng,

Xuejun Duan

и другие.

Ecological 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

5
Effect of riverfront utilization transitions on riparian water quality in the middle-lower Yangtze River DOI
Hui Zou, Junfeng Ge, Yongjiu Cai

и другие.

Journal of Environmental Management, Год журнала: 2025, Номер 380, С. 124960 - 124960

Опубликована: Март 15, 2025

Язык: Английский

Процитировано

0
Synthesis, Structural and Optical Characterizations, and Antibacterial Properties of (NiO)₀.₆(Ag₂O)₀.₄ Nanoparticles in Natural Water DOI
Eman Al-Absi, Naif Mohammed Al‐Hada,

Jwan H. Ibbini

и другие.

Ceramics International, Год журнала: 2024, Номер unknown

Опубликована: Дек. 1, 2024

Язык: Английский

Процитировано

2
Risk assessment of river water quality using long-memory processes subject to divergence or Wasserstein uncertainty DOI
Hidekazu Yoshioka, Yumi Yoshioka

Stochastic Environmental Research and Risk Assessment, Год журнала: 2024, Номер 38(8), С. 3007 - 3030

Опубликована: Май 18, 2024

Язык: Английский

Процитировано

1
Histological biomarkers and microbiological parameters of an estuarine fish from the Brazilian Amazon coast as potential indicators of risk to human health DOI
Gustavo Henrique Rodrigues Vale de Macedo, Jonatas da Silva Castro, Wanda Batista de Jesus

и другие.

Environmental Monitoring and Assessment, Год журнала: 2024, Номер 196(7)

Опубликована: Июнь 17, 2024

Язык: Английский

Процитировано

1
A diagnostic framework to reveal future clean water scarcity in a changing climate DOI Creative Commons
Shanlin Tong, Rui Xia, Jie Chen

и другие.

Journal of Hydrology Regional Studies, Год журнала: 2024, Номер 56, С. 102040 - 102040

Опубликована: Ноя. 1, 2024

Язык: Английский

Процитировано

0
Impact of Land Use Change on Seasonal Water Quality, Case Study in Chi-Mun River Basin in Thailand DOI Creative Commons
Kwanchai Pakoksung, Nantawoot Inseeyong,

Nattawin Chawaloesphonsiya

и другие.

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Дек. 16, 2024

Abstract This study investigates the correlation between land use changes and water quality in Chi-Mun River Basin, Thailand, from 2007 to 2021. It is first of its kind region Mekong providing critical insights for global river basin management. The research analyzes spatial temporal their multi-scale impacts on quality, utilizing change estimation, index analysis, redundancy analysis (RDA). results showed that stream variables displayed highly variations, with pH, Biochemical Oxygen Demand (BOD), Total Coliform Bacteria (TCB), Fecal (FCB), Phosphorus (TP), Nitrate Nitrogen (NO3-N), Ammonia-nitrogen (NH3-N), Suspended Solids (SS) all generally displaying higher levels wet season, while there were concentrations Dissolved (DO), Electrical Conductivity (EC), Water Quality Index (WQI) dry season. samples collected once January, March, May, August 2024. season represented May August, January March. total contribution patterns overall was stronger during shows a decline paddy forest areas alongside an expansion urban, agricultural, aqua agricultural land. significant seasonal forests bodies contributing purification, urban degraded quality. findings offer recommendations protection management policies align basin’s natural socio-economic characteristics, promoting coordinated regional development.

Язык: Английский

Процитировано

0