References
- ASCE task committee on application of artificial neural networks in hydrology (Rao Govindaraju), 2000a. Artificial Neural Networks in Hydrology. I: Preliminary Concepts. ASCE Journal of Hydrologic Engineering 5(2): 115. https://doi.org/10.1061/(ASCE)1084-0699(2000)5:2(115)
- ASCE task committee on application of artificial neural networks in hydrology (Rao Govindaraju), 2000b. Artificial Neural Networks in Hydrology. II: Hydrologic Applications. ASCE Journal of Hydrologic Engineering 5(2): 124-137. https://doi.org/10.1061/(ASCE)1084-0699(2000)5:2(124)
- Asefa, T., N. Wanakule, and A. Adams, 2007. Field-scale application of three types of neural networks to predict groundwater levels. Journal of the American Water Resources Association 43(5): 1245-1256. https://doi.org/10.1111/j.1752-1688.2007.00107.x
- Coppola, E., F. Szidarovszky, M. Poulton, and E. Charles, 2003. Artificial neural network approach for predicting transient water levels in a multilayered groundwater system under variable state, pumping, and climate conditions. Journal of Hydrologic Engineering 8(6): 348-360. https://doi.org/10.1061/(ASCE)1084-0699(2003)8:6(348)
- Coulibaly, P., F. Anctil, R. Aravena, and B. Bobee, 2001. Artificial neural network modeling of water table depth fluctuations. Water Resources Research 37(4): 885-896. https://doi.org/10.1029/2000WR900368
- Hwang, S., 2012. Utility of gridded observations for statistical bias-correction of climate model outputs and its hydrologic implication over west central Florida. Journal of the Korean Scociety of Agricultural Engineers 54(5): 91-102. https://doi.org/10.5389/KSAE.2012.54.5.091
- Hwang, S., C. Martinez, and T. Asefa, 2012. Assessing the benefites of incorporating rainfall forecasts into monthly flow forecast system of Tampa Bay Water, Florida. Journal of the Korean Society of Agricultural Engineers 54(4): 127-135. https://doi.org/10.5389/KSAE.2012.54.4.127
- Johnson, V. M. and L. L. Rogers, 2000. Accuracy of neural network approximators in simulation-optimization. Journal Water Resources Planning and Management 126(2): 48-56. https://doi.org/10.1061/(ASCE)0733-9496(2000)126:2(48)