Abstract
Chilika lagoon is the first Ramsar site in India located along the East Coast. Prediction of the eutrophication of such ecosystems is a key approach for a sustainable management perspective as it helps to formulate a management action plan. In the present study, a data-driven modeling approach, an artificial neural network (ANN), was used to predict eutrophication in the Chilika lagoon. Back-propagation neural network model was used to relate the major parameters that influence eutrophication indicators such as total nitrogen (TN), total phosphorus (TP), Secchi disc depth (SD), dissolved oxygen (DO), biological oxygen demand (BOD), pH, water temperature (WT), and Turbidity (TURB). The model evidenced an acceptable level of prediction when compared with the results of the field observations. This model’s most important determinant variables were those with a high Random Forest (RF) model permutation relevance ranking, which reduced the network’s structure and led to a more accurate and effective process. It demonstrated a high agreement between BOD and Turbidity. As per the TLI (trophic level index) estimation, the Chilika lagoon was observed to maintain an oligotrophic condition. However, there was a trophic switchover between the seasons and sectors. The study evidenced that the ANN was able to predict the indicators with reasonable accuracy, which could be proved as a valuable tool for the Chilika lagoon. This approach can be considered when developing a sustainable management and conservation action plan for Chilika and other similar aquatic ecosystems around the world.
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References
Acharya P, Muduli PR, Mishra DR, Kumar A, Kanuri VV, Das M (2022) Imprints of COVID-19 lockdowns on total petroleum hydrocarbon levels in Asia’s largest brackish water lagoon. Mar Pollut Bull 174:113137. https://doi.org/10.1016/j.marpolbul.2021.113137
Adnan RM, Zounemat-Kermani M, Kuriqi A, Kisi O (2021) Machine learning method in prediction streamflow considering periodicity component. In: Deo R, Samui P, Kisi O, Yaseen Z (eds) Intelligent data analytics for decision-support systems in hazard mitigation. Springer, Singapore. https://doi.org/10.1007/978-981-15-5772-9_18
Agwu OE, Akpabio JU, Dosunmu A (2020) Artificial neural network model for predicting drill cuttings settling velocity. Petroleum 6(4):340–352. https://doi.org/10.1016/j.petlm.2019.12.003
Akagha SC, Nwankwo DI, Yin K (2020) Dynamics of nutrient and phytoplankton in Epe Lagoon, Nigeria: possible causes and consequences of reoccurring cyanobacterial blooms. Appl Water Sci 10(5):1–16. https://doi.org/10.1007/s13201-020-01190-7
Aria S, Asadollahfardi G, Heidarzadeh N (2019) Eutrophication modelling of Amirkabir Reservoir (Iran) using an artificial neural network approach. Lakes Reserv Res Manag 24:48–58. https://doi.org/10.1111/lre.12254
Barik SK, Muduli PR, Mohanty B, Behera AT, Mallick S, Das A, Samal RN, Rastogi G, Pattnaik AK (2017) Spatio-temporal variability and the impact of Phailin on water quality of Chilika lagoon. Cont Shelf Res 136:39–56. https://doi.org/10.1016/j.csr.2017.01.019
Béjaoui B, Armi Z, Ottaviani E et al (2016) Random Forest model and TRIX used in combination to assess and diagnose the trophic status of Bizerte Lagoon, southern Mediterranean. Ecol Indic 71:293–301. https://doi.org/10.1016/j.ecolind.2016.07.010
Béjaoui B, Ottaviani E, Barelli E et al (2018) Machine learning predictions of trophic status indicators and plankton dynamic in coastal lagoons. Ecol Indic 95:765–774. https://doi.org/10.1016/j.ecolind.2018.08.041
Bennett C, Stewart RA, Beal CD (2013) ANN-based residential water end-use demand forecasting model. Expert Syst Appl 40(4):1014–1023. https://doi.org/10.1016/j.eswa.2012.08.012
Bhagowati B, Talukdar B, Narzary B, Ahamad K (2022) Prediction of lake eutrophication using ANN and ANFIS by artificial simulation of lake ecosystem. Model Earth Syst Environ 8:5289–5304. https://doi.org/10.1007/s40808-022-01377-8
Borowiak M, Borowiak D, Nowinski K (2020) Spatial differentiation and multiannual dynamics of water conductivity in lakes of the Suwalki landscape park. Water (Switzerland) 12(5):1277. https://doi.org/10.3390/W12051277
Brown MGL, Skakun S, He T, Liang S (2020) Intercomparison of machine-learning methods for estimating surface shortwave and photosynthetically active radiation. Remote Sens 12(3):1–13. https://doi.org/10.3390/rs12030372
Bui MH, Pham TL, Dao TS (2017) Prediction of cyanobacterial blooms in the Dau Tieng Reservoir using an artificial neural network. Mar Freshw Res 68(11):2070–2080. https://doi.org/10.1071/MF16327
Cigizoglu HK, Kişi Ö (2005) Flow prediction by three back propagation techniques using k-fold partitioning of neural network training data. Nord Hydrol 36(1):49–64. https://doi.org/10.2166/nh.2005.0005
CPCB (1986) Central Pollution Control Board. Primary water quality criteria for class SW-1 waters. In: The Environment (Protection) Rules 1986
Dynowski P, Senetra A, Źróbek-Sokolnik A, Kozłowski J (2019) The impact of recreational activities on aquatic vegetation in alpine lakes. Water (Switzerland) 11(1):173. https://doi.org/10.3390/w11010173
EL Idrissi T, Idri A, Bakkoury Z (2019) Systematic map and review of predictive techniques in diabetes self-management. Int J Inf Manag 46:263–277. https://doi.org/10.1016/j.ijinfomgt.2018.09.011
Frolov S, Ryan JP, Chavez FP (2012) Predicting euphotic-depth-integrated chlorophyll-a from discrete-depth and satellite-observable chlorophyll-a off Central California. J Geophys Res Ocean 117:1–7. https://doi.org/10.1029/2011JC007322
Gebler D, Szoszkiewicz K, Pietruczuk K (2017) Modeling of the river ecological status with macrophytes using artificial neural networks. Limnologica 65:46–54. https://doi.org/10.1016/j.limno.2017.07.004
Goethals PLM, Dedecker AP, Gabriels W, Lek S, De Pauw N (2007) Applications of artificial neural networks predicting macroinvertebrates in freshwaters. Aquat Ecol 41(3):491–508. https://doi.org/10.1007/s10452-007-9093-3
Grasshoff K, Kremling K, Ehrhardt M (eds) (1999) Methods of seawater analysis, 3rd edn. Wiley-VCH, Weinheim, 632 pages
Hadjisolomou E, Stefanidis K, Papatheodorou G, Papastergiadou E (2018) Assessment of the eutrophication-related environmental parameters in two Mediterranean lakes by integrating statistical techniques and self-organizing maps. Int J Environ Res Public Health 15(3):547. https://doi.org/10.3390/ijerph15030547
Hadjisolomou E, Stefanidis K, Papatheodorou G, Papastergiadou E (2017) Evaluating the contributing environmental parameters associated with eutrophication in a shallow lake by applying artificial neural networks techniques. Fresenius Environ Bull 26:3200–3208
Hagan R, Manktelow R, Taylor BJ, Mallett J (2014) Reducing loneliness amongst older people: a systematic search and narrative review. Aging Ment Health 18(6):683–693
Heisler J, Glibert PM, Burkholder JM, Anderson DM, Cochlan W, Dennison WC, Dortch Q, Gobler CJ, Heil CA, Humphries E, Lewitus A, Magnien R, Marshall HG, Sellner K, Stockwell DA, Stoecker DK, Suddleson M (2008) Eutrophication and harmful algal blooms: a scientific consensus. Harmful Algae 8(1):3–13. https://doi.org/10.1016/j.hal.2008.08.006
Hew TS, Kadir SLSA (2017) Applying channel expansion and self-determination theory in predicting use behaviour of cloud-based VLE. Behav Inform Technol 36(9):875–896. https://doi.org/10.1080/0144929X.2017.1307450
Huang J, Gao J, Zhang Y (2015) Combination of artificial neural network and clustering techniques for predicting phytoplankton biomass of Lake Poyang, China. Limnology 16:179–191. https://doi.org/10.1007/s10201-015-0454-7
Huo S, He Z, Su J, Xi B, Zhu C (2013) Using artificial neural network models for eutrophication prediction. Procedia Environ Sci 18:310–316. https://doi.org/10.1016/j.proenv.2013.04.040
Jally KS, Kumar Mishra A, Balabantaray S (2020) Estimation of trophic state index of Chilika Lake using Landsat-8 OLI and LISS-III satellite data. Geocarto Int 35:759–780. https://doi.org/10.1080/10106049.2018.1533593
Jimeno-Sáez P, Senent-Aparicio J, Cecilia JM, Pérez-Sánchez J (2020) Using machine-learning algorithms for eutrophication modeling: case study of Mar Menor lagoon (Spain). Int J Environ Res Public Health 17(4):1189. https://doi.org/10.3390/ijerph17041189
Karul C, Soyupak S, Çilesiz AF, Akbay N, Germen E (2000) Case studies on the use of neural networks in eutrophication modeling. Ecol Model 134(2–3):145–152. https://doi.org/10.1016/S0304-3800(00)00360-4
Kim DK, Park K, Jo H, Kwak IS (2019) Comparison of water sampling between environmental DNA metabarcoding and conventional microscopic identification: a case study in Gwangyang Bay, South Korea. Appl Sci (Switzerland) 9(16):3272. https://doi.org/10.3390/app9163272
Kuo JT, Hsieh MH, Lung WS, She N (2007) Using artificial neural network for reservoir eutrophication prediction. Ecol Model 200(1–2):171–177. https://doi.org/10.1016/j.ecolmodel.2006.06.018
Lee JHW, Huang Y, Dickman M, Jayawardena AW (2003a) Neural network modelling of coastal algal blooms. Ecol Model 159(2–3):179–201. https://doi.org/10.1016/S0304-3800(02)00281-8
Lee S, Ryu JH, Min K, Won JS (2003b) Landslide susceptibility analysis using GIS and artificial neural network. Earth Surf Process Landform 28(12):1361–1376. https://doi.org/10.1002/esp.593
Liu X, Zhang G, Sun G, Wu Y, Chen Y (2019) Assessment of Lake water quality and eutrophication risk in an agricultural irrigation area: a case study of the Chagan Lake in Northeast China. Water (Switzerland) 11(11). https://doi.org/10.3390/w11112380
Li X, Sha J, Wang ZL (2017) Chlorophyll-A prediction of lakes with different water quality patterns in China based on hybrid neural networks. Water (Switzerland) 9:524. https://doi.org/10.3390/w9070524
Luo W, Chen H, Lei A, Lu J, Hu Z (2014) Estimating cyanobacteria community dynamics and its relationship with environmental factors. Int J Environ Res Public Health 11(1):1141–1160. https://doi.org/10.3390/ijerph110101141
Lu F, Chen Z, Liu W, Shao H (2016) Modeling chlorophyll-a concentrations using an artificial neural network for precisely eco-restoring lake basin. Ecol Eng 95:422–429. https://doi.org/10.1016/j.ecoleng.2016.06.072
Maier HR, Morgan N, Chow CWK (2004) Use of artificial neural networks for predicting optimal alum doses and treated water quality parameters. Environ Model Softw 19(5):485–494. https://doi.org/10.1016/S1364-8152(03)00163-4
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 25:891–909. https://doi.org/10.1016/j.envsoft.2010.02.003
Mamun M, Kim JJ, Alam MA, An KG (2020) Prediction of algal chlorophyll-a and water clarity in monsoon-region reservoir using machine learning approaches. Water (Switzerland) 12(1):30. https://doi.org/10.3390/w12010030
Menendez RGDP, Andino SG, Lantz G, Michel CM, Landis T (2001) Noninvasive localization of electromagnetic epileptic activity. I. Method descriptions and simulations. Brain Topogr 14:131–137
Mitchell MW (2011) Bias of the random forest out-of-bag (OOB) error for certain input parameters. Open J Stat 01:205–211. https://doi.org/10.4236/ojs.2011.13024
Mowe MAD, Mitrovic SM, Lim RP, Furey A, Yeo DCJ (2007) Tropical cyanobacteria blooms: a review. https://doi.org/10.4081/jlimnol.2014
Muduli PR, Pattnaik AK (2020) Spatio-temporal variation in physicochemical parameters of water in the Chilika lagoon. In: Finlayson C, Rastogi G, Mishra D, Pattnaik A (eds) Ecology, conservation, and restoration of Chilika lagoon, India. Wetlands: ecology, conservation and management, vol 6. Springer, Cham. https://doi.org/10.1007/978-3-030-33424-6_9
Muduli PR, Barik M, Acharya P, Sahoo I (2022) Variability of nutrient and their stoichiometry in Chilika lagoon India. In: Coastal ecosystems, pp 139–173. https://doi.org/10.1007/978-3-030-84255-0_7
Muduli PR, Barik SK, Mahapatro D, Samal R, Rastogi G, Tripathy M, Bhatt K, Pattnaik A (2017) The impact of tropical cyclone ‘Phailin’ on the hydrology of Chilika lagoon, India. Int J Environ Sci Nat Res 4(2). https://doi.org/10.19080/IJESNR.2017.04.555632
Mulia IE, Tay H, Roopsekhar K, Tkalich P (2013) Hybrid ANN-GA model for predicting turbidity and chlorophyll-a concentrations. J Hydro-Environ Res 7:279–299. https://doi.org/10.1016/j.jher.2013.04.003
Motoda H, Liu H (2002) Feature selection, extraction and construction. Commun IICM 5:67–72
Napiórkowska-Krzebietke A, Kalinowska K, Bogacka-Kapusta E, Stawecki K, Traczuk P (2020) Cyanobacterial blooms and zooplankton structure in lake ecosystem under limited human impact. Water (Switzerland) 12(5). https://doi.org/10.3390/W12051252
Nayak BK, Acharya BC, Panda UC, Nayak BB, Acharya SK (2004) Variation of water quality in Chilika lake, Orissa. Indian J Mar Sci 33(2):164–169
Negnevitsky M (2011) Artificial intelligence: a guide to intelligent systems, 4th edn. Addison-Wesley
Neill SP, Hashemi MR (2018) Fundamentals of ocean renewable energy: generating electricity from the sea. Academic Press
Ooi K-B, Tan G (2016) Mobile technology acceptance model: an investigation using mobile users to explore smartphone credit card. Expert Syst Appl 59. https://doi.org/10.1016/j.eswa.2016.04.015
Oyebode O, Stretch D (2019) Neural network modeling of hydrological systems: a review of implementation techniques. Nat Resour Model 32(1):e12189. https://doi.org/10.1111/nrm.12189
Paerl HW (2006) Assessing and managing nutrient-enhanced eutrophication in estuarine and coastal waters: interactive effects of human and climatic perturbations. Ecol Eng 26(1):40–54. https://doi.org/10.1016/j.ecoleng.2005.09.006
Park Y, Cho KH, Park J et al (2015) Development of early-warning protocol for predicting chlorophyll-a concentration using machine learning models in freshwater and estuarine reservoirs, Korea. Sci Total Environ 502:31–41. https://doi.org/10.1016/j.scitotenv.2014.09.005
Panigrahi S, Acharya BC, Panigrahy RC, Nayak BK, Banarjee K, Sarkar SK (2007) Anthropogenic impact on water quality of Chilika lagoon RAMSAR site: a statistical approach. Wetl Ecol Manag 15(2):113–126. https://doi.org/10.1007/s11273-006-9017-3
Patra JK, Das G, Paramithiotis S, Shin HS (2016) Kimchi and other widely consumed traditional fermented foods of Korea: a review. Front Microbiol 7:1493
Peetabas N, Panda RP (2015) Conservation and management of bioresources of Chilika Lake, Odisha, India. Int J Sci Res Publ 5(7):1–4
Phillips G, Pietiläinen OP, Carvalho L et al (2008) Chlorophyll-nutrient relationships of different lake types using a large European dataset. Aquat Ecol 42:213–226. https://doi.org/10.1007/s10452-008-9180-0
Sahu BK, Pati P, Panigrahy RC (2014) Environmental conditions of Chilika Lake during pre and post hydrological intervention: an overview. J Coast Conserv 18(3):285–297. https://doi.org/10.1007/s11852-014-0318-z
Singh KP, Basant A, Malik A, Jain G (2009) Artificial neural network modeling of the river water quality—a case study. Ecol Model 220(6):888–895. https://doi.org/10.1016/j.ecolmodel.2009.01.004
Smith JL, Boyer GL, Zimba PV (2008) A review of cyanobacterial odorous and bioactive metabolites: impacts and management alternatives in aquaculture. Aquaculture 280(1–4):5–20. https://doi.org/10.1016/j.aquaculture.2008.05.007
Smith JA, Jarman M, Osborn M (1999) Doing interpretative phenomenological analysis. In: Murray M, Chamberlain K (eds) Qualitative health psychology: theories and methods, Sage, London, pp 218–241. https://doi.org/10.4135/9781446217870.n14
Smith VH, Joye SB, Howarth RW (2006) Eutrophication of freshwater and marine ecosystems. Limnol Oceanogr 51(1 Part 2):351–355
Srisuksomwong P, Pekkoh J (2020) Artificial neural network model to prediction of eutrophication and Microcystis aeruginosa bloom. Emerg Sci J 4(2):129–135. https://doi.org/10.28991/esj-2020-01217
Stefanidis K, Papastergiadou E (2019) Linkages between macrophyte functional traits and water quality: insights from a study in freshwater lakes of Greece. Water (Switzerland) 11(5):1047. https://doi.org/10.3390/w11051047
Strobl RO, Forte F, Pennetta L (2007) Application of artificial neural networks for classifying lake eutrophication status. Lakes Reser Res Manag 12(1):15–25. https://doi.org/10.1111/j.1440-1770.2007.00317.x
Teles LO, Vasconcelos V, Pereira E, Saker M, Vasconcelos V (2006) Time series forecasting of cyanobacteria blooms in the Crestuma Reservoir (Douro River, Portugal) using artificial neural networks. Environ Manag 38(2):227–237. https://doi.org/10.1007/s00267-005-0074-9
Tian Z, Gu B, Yang L, Lu Y (2015) Hybrid ANN-PLS approach to scroll compressor thermodynamic performance prediction. Appl Therm Eng 77:113–120. https://doi.org/10.1016/j.applthermaleng.2014.12.023
Verstijnen YJM, Maliaka V, Catsadorakis G, Lürling M, Smolders AJP (2021) Colonial nesting waterbirds as vectors of nutrients to Lake Lesser Prespa (Greece). Inland Waters 11(2):191–207. https://doi.org/10.1080/20442041.2020.1869491
Viaroli P, Bartoli M, Giordani G, Naldi M, Orfanidis S, Zaldivar JM (2008) Community shifts, alternative stable states, biogeochemical controls and feedbacks in eutrophic coastal lagoons: a brief overview. Aquat Conserv Mar Freshwat Ecosyst 18(S1):S105–S117
Wang L, Wang X, Jin X, Xu J, Zhang H, Yu J, Sun Q, Gao C, Wang L (2017) Analysis of algae growth mechanism and water bloom prediction under the effect of multi-affecting factor. Saudi J Biol Sci 24(3):556–562. https://doi.org/10.1016/j.sjbs.2017.01.026
Wei B, Sugiura N, Maekawa T (2001) Use of artificial neural network in the prediction of algal blooms. Water Res 35(8):2022–2028. https://doi.org/10.1016/S0043-1354(00)00464-4
Were K, Bui DT, Dick ØB, Singh BR (2015) A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape. Ecol Indic 52:394–403. https://doi.org/10.1016/j.ecolind.2014.12.028
Young WA, Millie DF, Weckman GR, Anderson JS, Klarer DM, Fahnenstiel GL (2011) Modeling net ecosystem metabolism with an artificial neural network and Bayesian belief network. Environ Model Softw 26(10):1199–1210. https://doi.org/10.1016/j.envsoft.2011.04.004
Zhang Q, Stanley SJ (1997) Forecasting raw-water quality parameters for the north Saskatchewan river by neural network modeling. Water Res 31(9):2340–2350. https://doi.org/10.1016/S0043-1354(97)00072-9
Acknowledgments
The authors are grateful to the CE, Chilika Development Authority (CDA), for providing required facilities at WRTC. This work was carried out with the funding support from the World Bank (ICZMP, Odisha) (CREDIT No. 4765-IN) and NPCA (National Plan for Conservation of Aquatic Ecosystems). Thanks are also due to Mr. Bibhuti B. Dora for preparing the Chilika GIS map. The supporting staff and researchers of CDA are also acknowledged for their generous help during the study.
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Acharya, P., Muduli, P.R., Das, M. (2023). Eutrophication Modeling of Chilika Lagoon Using an Artificial Neural Network Approach. In: Dhyani, S., Adhikari, D., Dasgupta, R., Kadaverugu, R. (eds) Ecosystem and Species Habitat Modeling for Conservation and Restoration. Springer, Singapore. https://doi.org/10.1007/978-981-99-0131-9_27
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