Secchi Disk Depth Estimation from Water Quality Parameters: Artificial Neural Network versus Multiple Linear Regression Models?
Tóm tắt
Từ khóa
Tài liệu tham khảo
Aarup T (2002) Transparency of the North Sea and Baltic Sea a Secchi Depth data mining study. Oceanologia 44(3):323–337
Adamala S, Raghuwanshi NS, Mishra A (2015) Generalized quadratic synaptic neural networks for ET0 modeling. Environ Process 2(2):309–329. doi: 10.1007/s40710-015-0066-6
Alexakis D, Tsihrintzis VA, Tsakiris G, Gikas GD (2016) Suitability of water quality indices for application in lakes in the Mediterranean. Water Resour Manag. doi: 10.1007/s11269-016-1240-y
Antonopoulos VZ, Georgiou PE, Antonopoulos ZV (2015) Dispersion coefficient prediction using empirical models and ANNs. Environ Process 2(2):379–394. doi: 10.1007/s40710-015-0074-6
Azad S, Debnath S, Rajeevan M (2015) Analysing predictability in Indian monsoon rainfall: a data analytic approach. Environ Process 2(4):717–727. doi: 10.1007/s40710-015-0108-0
Brezonik PL (1978) Effect of organic color and turbidity of secchi disk transparency. J Fish Res Board Can 35(11):1410–1416. doi: 10.1139/f78-222
Carlson RE (1977) A trophic state index for lakes. Limnol Oceanogr 22:361–369. doi: 10.4319/lo.1977.22.2.0361
Das DB, Thirakulchaya T, Deka L, Hanspal NS (2015) Artificial neural network to determine dynamic effect in capillary pressure relationship for two-phase flow in porous media with micro-heterogeneities. Environ Process 2(1):1–18. doi: 10.1007/s40710-014-0045-3
Davies-Colley RJ (1988) Measuring water clarity with a black disc. Limnol Oceanogr 33:616–623. doi: 10.4319/lo.1988.33.4.0616
Davies-Colley RJ, Smith DG (2001) Turbidity suspended sediment, and water clarity: a review. JAWRA J Am Water Resour Assoc 35-5:1085–1101. doi: 10.1111/j.1752-1688.2001.tb03624.x
Fahnenstiel GL, Lang GA, Nalepa TF, Johengen TH (1995a) Effects of zebra mussel (Dreissena polymorpha) colonization on water quality parameters in Saginaw Bay, Lake Huron. J Great Lakes Res 21:435–448. doi: 10.1016/S0380-1330(95)71057-7
Fahnenstiel GL, Bridgeman TB, Lang GA, McCormick MJ, Nalepa TF (1995b) Phytoplankton productivity in Saginaw Bay, Lake Huron: effects of zebra mussel (Dreissena polymorpha) colonization. J Great Lakes Res 21:465–475. doi: 10.1016/S0380-1330(95)71059-0
Gikas GD, Yiannakopoulou T, Tsihrintzis VA (2006) Water quality trends in a coastal lagoon impacted by non-point source pollution after implementation of protective measures. Hydrobiologia 563:385–406. doi: 10.1007/s10750-006-0034-2
Gikas GD, Tsihrintzis VA, Akratos CS, Haralambidis G (2009) Water quality trends in Polyphytos reservoir, Aliakmon River, Greece. Environ Monit Assess 149:163–181. doi: 10.1007/s10661-008-0191-z
Heddam S, Bermad A, Dechemi N (2011) Applications of radial basis function and generalized regression neural networks for modelling of coagulant dosage in a drinking water treatment: a comparative study. ASCE J Environ Eng 137(12):1209–1214. doi: 10.1061/(ASCE)EE.1943-7870.0000435
Heddam S, Bermad A, Dechemi N (2012) ANFIS-based modelling for coagulant dosage in drinking water treatment plant: a case study. Environ Monit Assess 184:1953–1971. doi: 10.1007/s10661-011-2091-x
Heddam S, Lamda H, Filali S (2016) Predicting effluent biochemical oxygen demand in a wastewater treatment plant using generalized regression neural network based approach: A comparative study. Environ Process 3(1):153–165. doi: 10.1007/s40710-016-0129-3
Hellweger FL, Schlosser P, Lall U, Weissel JK (2004) Use of satellite imagery for water quality studies in New York Harbor. Estuar Coast Shelf Sci 61:437–448. doi: 10.1016/j.ecss.2004.06.019
Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2:359–366. doi: 10.1016/0893-6080(89)90020-8
Hornik K, Stinchcombe M, White H (1990) Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks. Neural Netw 3:551–560. doi: 10.1016/0893-6080(90)90005-6
Ibáñez Civera J, Garcia Breijo E, Laguarda Miró N, Gil Sánchez L, Garrigues Baixauli J, Romero Gil I, Masot Peris R, Alcañiz Fillol M (2011) Artificial neural network onto eight bit microcontroller for secchi depth calculation. Sensors Actuators B 156:132–139. doi: 10.1016/j.snb.2011.04.001
Johengen TH, Nalepa TF, Fahnentiel GL, Goudy G (1995) Nutrient changes in Saginaw Bay, Lake Huron after the establishment of the zebra mussel (Dreissena polymorpha). J Great Lakes Res 21:449–464. doi: 10.1016/S0380-1330(95)71058-9
Johengen TH, Nalepa TF, Lang GA, Fanslow DL, Vanderploeg HA, Agy MA (2000) Physical and Chemical Variables of Saginaw Bay, Lake Huron in 1994-1996. NOAA Technical Memorandum TM-115, Chlorophyll, nutrients, alkalinity, carbon, and total suspended solids data collected in Saginaw Bay, Lake Huron from 1994 to 1996. Builds upon TM-091 http://www.glerl.noaa.gov/ftp/publications/tech_reports/glerl-115/ .
Kloiber SM, Brezonik PL, Olmanson LG, Bauer ME (2002) A procedure for regional lake water clarity assessment using Landsat multispectral data. Remote Sens Environ 82:38–47. doi: 10.1016/S0034-4257(02)00022-6
Larson GL, Hoffman RL, Hargreaves BR, Collier RW (2007) Predicting Secchi disk depth from average beam attenuation in a deep, Ultra-clear lake. Hydrobiologia 574:141–148. doi: 10.1007/s10750-006-0349-z
Legates DR, McCabe GJ (1999) Evaluating the use of “goodness of fit” measures in hydrologic and hydroclimatic model validation. Water Resour Res 35:233–241. doi: 10.1029/1998WR900018
Li R, Li J (2004) Satellite remote sensing technology for lake water clarity monitoring: an overview. Environmental Informatics Archives 2:893–901
Li X, Zecchin AC, Maier HR (2015) Improving partial mutual information-based input variable selection by consideration of boundary issues associated with bandwidth estimation. Environ Model Softw 71:78–96. doi: 10.1016/j.envsoft.2015.05.013
Luhtala H, Tolvanen H (2013) Optimizing the use of Secchi depth as a proxy for euphotic depth in coastal waters: An empirical study from the Baltic Sea. ISPRS Int J Geo-Inf 2:1153–1168. doi: 10.3390/ijgi2041153
Maier HR, Jain A, Dandy GC, Sudheer KP (2010) Methods used for the development of neural networks for the prediction of water resource variables in river systems: current status and future directions. Environ Model Softw 25(8):891–909. doi: 10.1016/j.envsoft.2010.02.003
Mandal S, Mahapatra SS, Adhikari S, Patel RK (2015) Modeling of arsenic (III) removal by evolutionary genetic programming and Least Square support vector machine models. Environ Process 2(1):145–172. doi: 10.1007/s40710-014-0050-6
MATLAB (2010) the MathWorks Inc., Natick, MA. http://www.mathworks.com .
May R, Dandy G, Maier H (2011) Review of input variable selection methods for artificial neural networks. In: InTech (ed) Artificial neural networks - methodological advances and biomedical applications, Rijeka, pp. 19–44. doi: 10.5772/16004
McCulloch WS, Pitts W (1943) A logical calculus of the ideas imminent in nervous activity. Bull Math Biophys 5:115–133. doi: 10.1007/BF02478259
Michaud JP (1991) A Citizens’ guide to understanding and monitoring lakes and-streams. Washington State Department of Ecology. www.ecy.wa.gov/programs/wq .
Myre E, Shaw R (2006) The turbidity tube: simple and accurate measurement of turbidity in the field. Michigan Technology University, Houghton
Olmanson LG, Brezonik PL, Bauer ME (2015) Remote sensing for regional lake water quality assessment: capabilities and limitations of current and upcoming satellite systems. In T. Younos, T.E. Parece (eds.) Advances in watershed science and assessment. The Handbook of Environmental Chemistry 33. doi: 10.1007/978-3-319-14212-8_5
Santisukkasaem U, Olawuyi F, Oye P, Das DB (2015) Artificial neural network (ANN) for evaluating permeability decline in permeable reactive barrier (PRB). Environ Process 2(2):291–307. doi: 10.1007/s40710-015-0076-4
USGS (2014) Estimation of Secchi Depth from turbidity Data in the Willamette River at Portland, OR (14211720). http://or.water.usgs.gov/will_morrison/secchi_depth_model.html .
Wu G, Leeuw JD, Skidmore AK, Prins HT, Liu Y (2008) Comparison of MODIS and Landsat TM5 images for mapping tempo-spatial dynamics of Secchi disk depths in Poyang Lake National Nature Reserve, China. Int J Remote Sens 29(8):2183–2198. doi: 10.1080/01431160701422254