Abstract
Accurate prediction of chlorophyll-a is crucial for assessing eutrophication dynamics in reservoirs, lakes, estuaries, etc. This study focuses on developing and comparing two models (1. Artificial neural network, ANN; 2. Adaptive neuro-fuzzy inference system, ANFIS) for chlorophyll-a prediction in the Ashtamudi estuary, Kerala, India. Total Dissolved Solids, Turbidity, and Total phosphates were taken as input parameters for predicting chlorophyll-a for both ANN and ANFIS models considering their considerable influence on chlorophyll-a. The ANN model achieved a remarkable coefficient of determination (R2) of 1.0, indicating a perfect fit between the observed and predicted chlorophyll-a values. The performance of the ANFIS model was also good, with an R2 exceeding 0.95. In ANFIS, the membership functions, gbellmf, trimf, and gaussmf over-predicted the chlorophyll-a contents, while trapmf, gauss2mf, pimf, dsigmf, and psigmf under-predicted the chlorophyll-a contents. The ANN model performed better compared to the ANFIS model on chlorophyll-a prediction in the Ashtamudi estuary and can be used for future predictions of chlorophyll-a. The models generated can capture the complex relationships between turbidity, TDS, total phosphates, and chlorophyll-a concentrations. Moreover, these provide valuable insights into the temporal and spatial variations of chlorophyll-a concentrations, allowing for proactive management strategies to mitigate the impacts of eutrophication. The models generated find applications among water resource planners, environmental agencies, and policymakers.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Sruthy GS, Priya KL, Madhu AM, Chellappan S, Adarsh S, Haddout S (2021) Fuzzy logic approach for the assessment of trophic state of water bodies. 169:106314
Luo W, Zhu S, Shiqiang W, Dai J (2019) Comparing artificial intelligence techniques for chlorophyll-a prediction in US lakes
Chen W-B, Liu W-C (2015) Water quality modeling in reservoirs using multivariate linear regression and two neural network models, pp 1–12
Tian W, Liao Z, Wang X (2020) Transfer learning for neural network model in chlorophyll-a dynamics prediction. https://doi.org/10.1007/s11356-019-06156-0
Mulia IE, Tay H, Roopsekhar K, Tkalich P (2013) Hybrid ANNeGA model for predicting turbidity and chlorophyll-a concentrations. 7:277–299
Anthwal T, Chandola A, Thapliyal MP (2018) Performance analysis of ANN model for estimation of trophic, status index of lakes. IAES Int J Artif Intell (IJ-AI) 7:1–10
Liu Y, Zhao J, Tang Y, Jiang X, Liao J (2022) Water quality modeling in reservoirs using multivariate linear regression and two neural network models
Jang SR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685
Anderson DM, Glibert PM, Burkholder JM (2002) Harmful algal blooms and eutrophication: nutrient sources, composition, and consequences. Estuaries Coasts 25(704–26):2002. https://doi.org/10.1007/BF02804901
Luo W, Zhu S, Wu S, Dai J (2019) Comparing artificial intelligence techniques for chlorophyll-a prediction in US lakes
Suo X, Jiang Y, Yang M, Li S, Wang K, Wang C (2010) Artificial neural network to predict leaf population chlorophyll content from cotton plant image. 9:38–45
Liu Y, Zhao J, Tang Y, Jiang X, Liao J (2022) Construction of a chlorophyll content prediction model for predicting chlorophyll content in the pericarp of Korla fragrant pears during the storage period, vol 12
Hussain SM, Anbalagan S, Kumar KS, Neelavannan K, Pradhap D, Radhakrishnan K, Godson PS, Krishnakumar S (2020) A baseline study on elemental concentration and potential ecological risk status of the surface sediments of Ashtamudi Lake, South West Coast of India. https://doi.org/10.1016/j.marpolbul.2020.111410
Tang DYY, Chew KW, Ting H-Y, Sia Y-H, Gentili FG, Park Y-K, Banat F, Culaba AB, Ma Z, Show PL (2023) Application of regression and artificial neural network analysis of Red-Green-Blue image components in prediction of chlorophyll content in microalgae. 370:128503
Mamun Md, Kim J-J, Alam MdA, An K-G (2020) Prediction of algal chlorophyll-a and water clarity in monsoon-region reservoir using machine learning approaches. Water 12(1):30. https://doi.org/10.3390/w12010030
Bhagowati B, Talukdar B, Narzary BK, Ahamad K (2022) Prediction of lake eutrophication using ANN and ANFIS by artificial simulation of lake ecosystem. https://doi.org/10.1007/s40808-022-01377-8
Setshedi KJ, Mutingwende N, Ngqwala NP (2021) The use of artificial neural networks to predict the physicochemical characteristics of water quality in three district municipalities, Eastern Cape Province, South Africa. 18(10):5248
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Raj, M.R., Krishnapriya, K., Hisana, N., Priya, K.L., Azhikodan, G. (2024). Evaluating the Performance of ANN and ANFIS Models for the Prediction of Chlorophyll in the Ashtamudi Estuary, India. In: Das, S., Saha, S., Coello Coello, C.A., Bansal, J.C. (eds) Advances in Data-Driven Computing and Intelligent Systems. ADCIS 2023. Lecture Notes in Networks and Systems, vol 891. Springer, Singapore. https://doi.org/10.1007/978-981-99-9524-0_39
Download citation
DOI: https://doi.org/10.1007/978-981-99-9524-0_39
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-9523-3
Online ISBN: 978-981-99-9524-0
eBook Packages: EngineeringEngineering (R0)