BOD
|
0.32 |
Ahmadi et al. (2018)Ahmadi, A., Fatemi, Z., & Nazari, S. (2018). Assessment of input data selection methods for BOD simulation using data-driven models: a case study. Environmental Monitoring and Assessment, 190(4), 239. http://dx.doi.org/10.1007/s10661-018-6608-4. http://dx.doi.org/10.1007/s10661-018-660...
|
1.67 |
Ahmed & Shah (2017)Ahmed, A. A. M., & Shah, S. M. S. (2017). Application of adaptive neuro-fuzzy inference system (ANFIS) to estimate the biochemical oxygen demand (BOD) of Surma River. Journal of King Saud University - Engineering Sciences,, 29(3), 237-243.
|
0.42 |
Banejad & Olyaie (2011)Banejad, H. H., & Olyaie, E. H. (2011). Application of an artificial neural network model to rivers water quality indexes prediction: a case study. The Journal of American Science, 7(1), 60-65.
|
POC
|
0.10 |
Buchard-Levine et al. (2014)Buchard-Levine, A., Liu, S., Vince, F., Li, M., & Ostfeld, A. (2014). A hybrid evolutionary data driven model for river water quality early warning. Journal of Environmental Management, 143(1), 8-16. http://dx.doi.org/10.1016/j.jenvman.2014.04.017. http://dx.doi.org/10.1016/j.jenvman.2014...
|
4.43 |
Zhang et al. (2021)Zhang, Y., Yao, X., Wu, Q., Huang, Y., Zhou, Z., Yang, J., & Liu, X. (2021). Turbidity prediction of lake-type raw water using random forest model based on meteorological data: a case study of Tai lake, China. Journal of Environmental Management, 290, 112657. http://dx.doi.org/10.1016/j.jenvman.2021.112657. http://dx.doi.org/10.1016/j.jenvman.2021...
|
DOC
|
0.20 |
Buchard-Levine et al. (2014)Buchard-Levine, A., Liu, S., Vince, F., Li, M., & Ostfeld, A. (2014). A hybrid evolutionary data driven model for river water quality early warning. Journal of Environmental Management, 143(1), 8-16. http://dx.doi.org/10.1016/j.jenvman.2014.04.017. http://dx.doi.org/10.1016/j.jenvman.2014...
|
0.30 |
Zhou (2020)Zhou, Y. (2020). Real-time probabilistic forecasting of river water quality under data missing situation: deep learning plus post-processing techniques. Journal of Hydrology, 589, 125164. http://dx.doi.org/10.1016/j.jhydrol.2020.125164. http://dx.doi.org/10.1016/j.jhydrol.2020...
|
NH4
|
2.05 |
Suen & Eheart (2003)Suen, J. P., & Eheart, W. (2003). Evaluation of neural networks for modelling nitrate concentration in rivers. Journal of Water Resources Planning and Management, 129(6), 505-510. http://dx.doi.org/10.1061/(ASCE)0733-9496(2003)129:6(505). http://dx.doi.org/10.1061/(ASCE)0733-949...
|
0.33 |
Buchard-Levine et al. (2014)Buchard-Levine, A., Liu, S., Vince, F., Li, M., & Ostfeld, A. (2014). A hybrid evolutionary data driven model for river water quality early warning. Journal of Environmental Management, 143(1), 8-16. http://dx.doi.org/10.1016/j.jenvman.2014.04.017. http://dx.doi.org/10.1016/j.jenvman.2014...
|
NOrg
|
0.15 |
Zhou (2020)Zhou, Y. (2020). Real-time probabilistic forecasting of river water quality under data missing situation: deep learning plus post-processing techniques. Journal of Hydrology, 589, 125164. http://dx.doi.org/10.1016/j.jhydrol.2020.125164. http://dx.doi.org/10.1016/j.jhydrol.2020...
|
NO2
|
0.10 |
Ha et al. (2020)Ha, N. T., Nguyen, H. Q., Truong, N. C. Q., Le, T. L., Thai, V. N., & Pham, T. L. (2020). Estimation of nitrogen and phosphorus concentrations from water quality surrogates using machine learning in the Tri An Reservoir, Vietnam. Environmental Monitoring and Assessment, 192(789), 789. http://dx.doi.org/10.1007/s10661-020-08731-2. http://dx.doi.org/10.1007/s10661-020-087...
|
0.66 |
Li et al. (2020a)Li, S., Bhattari, R., Cooke, R. A., Verma, S., Huang, X., Markus, M., & Christianson, L. (2020a). Relative performance of different data mining techniques for nitrate concentration and load estimation in different type of watersheds. Environmental Pollution, 263, 114618.
|
NO3
|
0.16 |
Ha et al. (2020)Ha, N. T., Nguyen, H. Q., Truong, N. C. Q., Le, T. L., Thai, V. N., & Pham, T. L. (2020). Estimation of nitrogen and phosphorus concentrations from water quality surrogates using machine learning in the Tri An Reservoir, Vietnam. Environmental Monitoring and Assessment, 192(789), 789. http://dx.doi.org/10.1007/s10661-020-08731-2. http://dx.doi.org/10.1007/s10661-020-087...
|
0.56 |
Li et al. (2020b)Li, W., Fang, H., Qin, G., Tan, X., Huang, Z., Zeng, F., Du, H., & Li, S. (2020b). Concentration estimation of dissolved oxygen in Pearl River Basin using input variable selection and machine learning techniques. The Science of the Total Environment, 731, 139099. http://dx.doi.org/10.1016/j.scitotenv.2020.139099. http://dx.doi.org/10.1016/j.scitotenv.20...
|
TN
|
1.34 |
Shen et al. (2019)Shen, J., Qin, Q., Wang, Y., & Sisson, M. (2019). A data-driven modeling approach for simulating algal blooms in the tidal freshwater of James River in response to riverine nutrient loading. Ecological Modelling, 398, 44-54. http://dx.doi.org/10.1016/j.ecolmodel.2019.02.005. http://dx.doi.org/10.1016/j.ecolmodel.20...
|
0.11 |
Lu et al. (2022)Lu, H., Yang, L., Fan, Y., Qian, X., & Liu, T. (2022). Novel simulation of aqueous total nitrogen and phosphorus concentrations in Taihu Lake with machine learning. Environmental Research, 204, 111940. http://dx.doi.org/10.1016/j.envres.2021.111940. http://dx.doi.org/10.1016/j.envres.2021....
|
TP
|
0.25 |
Shen et al. (2019)Shen, J., Qin, Q., Wang, Y., & Sisson, M. (2019). A data-driven modeling approach for simulating algal blooms in the tidal freshwater of James River in response to riverine nutrient loading. Ecological Modelling, 398, 44-54. http://dx.doi.org/10.1016/j.ecolmodel.2019.02.005. http://dx.doi.org/10.1016/j.ecolmodel.20...
|
0.02 |
Lu et al. (2022)Lu, H., Yang, L., Fan, Y., Qian, X., & Liu, T. (2022). Novel simulation of aqueous total nitrogen and phosphorus concentrations in Taihu Lake with machine learning. Environmental Research, 204, 111940. http://dx.doi.org/10.1016/j.envres.2021.111940. http://dx.doi.org/10.1016/j.envres.2021....
|
PO4
|
0.01 |
Ha et al. (2020)Ha, N. T., Nguyen, H. Q., Truong, N. C. Q., Le, T. L., Thai, V. N., & Pham, T. L. (2020). Estimation of nitrogen and phosphorus concentrations from water quality surrogates using machine learning in the Tri An Reservoir, Vietnam. Environmental Monitoring and Assessment, 192(789), 789. http://dx.doi.org/10.1007/s10661-020-08731-2. http://dx.doi.org/10.1007/s10661-020-087...
|
DO
|
0.47 |
Heddam (2016)Heddam, S. (2016). Simultaneous modelling and forecasting of hourly dissolved oxygen concentration (DO) using radial basis function neural network (RBFNN) based approach: a case study from the Klamath River, Oregon, USA. Modeling Earth Systems and Environment, 2(135), 135. http://dx.doi.org/10.1007/s40808-016-0197-4. http://dx.doi.org/10.1007/s40808-016-019...
|
0.73 |
Csábrági et al. (2019)Csábrági, A., Molnár, S., Tanos, P., Kovács, J., Molnár, M., Szabó, I., & Hatvani, I. G. (2019). Estimation of dissolved oxygen in riverine ecosystems: comparison of differently optimized neural networks. Ecological Engineering, 138, 298-309. http://dx.doi.org/10.1016/j.ecoleng.2019.07.023. http://dx.doi.org/10.1016/j.ecoleng.2019...
|
0.88 |
Abba et al. (2017)Abba, S. I., Hadi, S. J., & Abdullahi, J. (2017). River water modelling prediction using multi-linear regression, artificial neural network, and adaptive neuro-fuzzy inference system techniques. Procedia Computer Science, 120, 75-82. http://dx.doi.org/10.1016/j.procs.2017.11.212. http://dx.doi.org/10.1016/j.procs.2017.1...
|
0.84 |
Banejad & Olyaie (2011)Banejad, H. H., & Olyaie, E. H. (2011). Application of an artificial neural network model to rivers water quality indexes prediction: a case study. The Journal of American Science, 7(1), 60-65.
|