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Ecological states of watercourses regarding water quality parameters and hydromorphological parameters: deriving empirical equations by machine learning models

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Abstract

Environmental biomonitoring techniques have been widely applied to assess the quality states of toxic chemical compounds in surface freshwater quality. The methods based on macrophytes are generally recruited to present trustworthy assessments of ecological status for surface water bodies. The chief goal of the present investigation was to improve robust machine learning models (MLMs) to establish relationships among macrophyte indices and the simultaneous effects of water quality parameters (WQPs) and hydromorphological factors. In the present research, the dataset included monthly WQPs, which have been accumulated from 200 sample points located in the bottomland of studied rivers, placed in Poland. This research utilized three ecological indices, namely the macrophyte index for rivers (MIR), the macrophyte biological index for rivers (IBMR), and river macrophyte nutrient index (RMNI) whereas species richness (N) and Simpson index (D) were considered as diversity indices. 12 WQPs and two hydromorphological indices have been considered as input variables for the MLMs namely gene-expression programming (GEP), evolutionary polynomial regression (EPR), multivariate adaptive regression spline (MARS), and model tree (MT). In terms of the best performance of MLMs’ results, RMNI predictions were obtained by EPR (correlation coefficient [R] = 0.6061 and root mean square error [RMSE] = 0.5355) whereas MIR, IBMR, species richness, and Simpson index were predicted by MARS (R = 0.4333 and RMSE = 11.5046), EPR (R = 0.6020 and RMSE = 1.6923), GEP (R = 0.6089 and RMSE = 7.7229), and MARS (R = 0.5066 and RMSE = 0.2217), respectively. The great impact of these indexes was evaluated by the statistical parameters. This study showed that the biological assessment of rivers through macrophyte indexes not only helps to broaden knowledge related to the ecological status of the river but also aids in managing natural and anthropogenic activities’ impacts on the water body.

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References

  • Birk SB, Willby NJ (2010) Towards harmonization of ecological quality classification: establishing common grounds in European macrophyte assessment for rivers. Hydrobiologia 652:149e163. https://doi.org/10.1007/s10750-010-0327-3

    Article  Google Scholar 

  • Birk S, Bonne W, Borja A, Brucet S, Courrat A, Poikane S, Solimini A, van de Bund W, Zampoukas N, Hering D (2012) Three hundred ways to assess Europe’s surface waters: an almost complete overview of biological methods to implement the Water Framework Directive. Ecol Ind 18:31–41. https://doi.org/10.1016/j.ecolind.2011.10.009

    Article  Google Scholar 

  • Birk S, Chapman D, Carvalho L, Spears BM, Andersen HE, Argillier C, Auer S, Baattrup-Pedersen A, Banin L, Beklioğlu M, Bondar-Kunze E, Borja A, Branco P, Bucak T, Buijse AD, Cardoso AC, Couture R-M, Cremona F, de Zwart D, Feld CK, Ferreira MT, Feuchtmayr H, Gessner MO, Gieswein A, Globevnik L, Graeber D, Graf W, Gutiérrez-Cánovas C, Hanganu J, Işkın U, Järvinen M, Jeppesen E, Kotamäki N, Kuijper M, Lemm JU, Lu S, Solheim AL, Mischke U, Moe SJ, Nõges P, Nõges T, Ormerod SJ, Panagopoulos Y, Phillips G, Posthuma L, Pouso S, Prudhomme C, Rankinen K, Rasmussen JJ, Richardson J, Sagouis A, Santos JM, Schäfer RB, Schinegger R, Schmutz S, Schneider SC, Schülting L, Segurado P, Stefanidis K, Sures B, Thackeray SJ, Turunen J, Uyarra MC, Venohr M, von der Ohe PC, Willby N, Hering D (2020) Impacts of multiple stressors on freshwater biota across spatial scales and ecosystems. Nat Ecol Evol 4(8):1060–1068. https://doi.org/10.1038/s41559-020-1216-4

    Article  PubMed  Google Scholar 

  • Bornette G, Puijalon S (2011) Response of aquatic plants to abiotic factors. Aquat Sci 73:1–14. https://doi.org/10.1007/s00027-010-0162-7

    Article  CAS  Google Scholar 

  • Bytyçi P, Shala-Abazi A, Zhushi-Etemi F, Bonifazi G, Hyseni-Spahiu M, Fetoshi O, Çadraku H, Feka F, Millaku F (2022) The macrophyte indices for rivers to assess the ecological conditions in the Klina River in the Republic of Kosovo. Plants (basEl). https://doi.org/10.3390/plants11111469

    Article  PubMed  Google Scholar 

  • Bytyqi P, Czikkely M, Shala-Abazi A, Fetoshi O, Ismaili M, Hyseni-Spahiu M, Ymeri P, Kabashi-Kastrati E, Millaku F (2020) Macrophytes as biological indicators of organic pollution in the Lepenci River Basin in Kosovo. J Freshw Ecol 35(1):105–121. https://doi.org/10.1080/02705060.2020.1745913

    Article  CAS  Google Scholar 

  • Chen W-B, Liu W-C (2014) Artificial neural network modeling of dissolved oxygen in reservoir. Environ Monit Assess 186(2):1203–1217. https://doi.org/10.1007/s10661-013-3450-6

    Article  CAS  PubMed  Google Scholar 

  • Ciecierska H, Kolada A (2014) ESMI: a macrophyte index for assessing the ecological status of lakes. Environ Monit Assess 186(9):5501–5517. https://doi.org/10.1007/s10661-014-3799-1

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Demars BOL, Edwards AC (2009) Distribution of aquatic macrophytes in contrasting river systems: a critique of compositional-based assessment of water quality. Sci Total Environ 407(2):975–990. https://doi.org/10.1016/j.scitotenv.2008.09.012

    Article  ADS  CAS  PubMed  Google Scholar 

  • Dudgeon D, Arthington AH, Gessner MO, Kawabata Z-I, Knowler DJ, Lévêque C, Naiman RJ, Prieur-Richard A-H, Soto D, Stiassny MLJ, Sullivan CA (2006) Freshwater biodiversity: importance, threats, status and conservation challenges. Biol Rev 81(2):163–182. https://doi.org/10.1017/S1464793105006950

    Article  PubMed  Google Scholar 

  • Eftekhari M, Mehrpooya A, Saberi-Movahed F, Torra V (2022) How fuzzy concepts contribute to machine learning. Springer, Cham

    Book  Google Scholar 

  • Fedor P, Zvaríková M (2019) Biodiversity indices. In: Fath B (ed) Encyclopedia of ecology, 2nd edn. Elsevier, Oxford, pp 337–346

    Chapter  Google Scholar 

  • Friedman JH (1991) Multivariate adaptive regression splines. Ann Stat 19(1):1–67. https://doi.org/10.1214/aos/1176347963

    Article  MathSciNet  Google Scholar 

  • Gebler D, Kayzer D, Szoszkiewicz K, Budka A (2014) Artificial neural network modelling of macrophyte indices based on physico-chemical characteristics of water. Hydrobiologia 737(1):215–224. https://doi.org/10.1007/s10750-013-1585-7

    Article  CAS  Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • Gebler D, Wiegleb G, Szoszkiewicz K (2018) Integrating river hydromorphology and water quality into ecological status modelling by artificial neural networks. Water Res 139:395–405. https://doi.org/10.1016/j.watres.2018.04.016

    Article  CAS  PubMed  Google Scholar 

  • Giustolisi O, Savic DA (2006) A symbolic data-driven technique based on evolutionary polynomial regression. J Hydroinf 8(3):207–222. https://doi.org/10.2166/hydro.2006.020b

    Article  Google Scholar 

  • Gorgan-Mohammadi F, Rajaee T, Zounemat-Kermani M (2022) Decision tree models in predicting water quality parameters of dissolved oxygen and phosphorus in lake water. Sustain Water Resour Manag 9(1):1. https://doi.org/10.1007/s40899-022-00776-0

    Article  Google Scholar 

  • Haury J, Peltre MC, Trémolières M, Barbe J, Thiébaut G, Bernez I, Daniel H, Chatenet P, Haan-Archipof G, Muller S, Dutartre A, Laplace-Treyture C, Cazaubon A, Lambert-Servien E (2006) A new method to assess water trophy and organic pollution—the Macrophyte Biological Index for Rivers (IBMR): its application to different types of river and pollution. Hydrobiologia 570(1):153–158. https://doi.org/10.1007/s10750-006-0175-3

    Article  CAS  Google Scholar 

  • Heddam S, Kisi O (2018) Modelling daily dissolved oxygen concentration using least square support vector machine, multivariate adaptive regression splines and M5 model tree. J Hydrol 559:499–509. https://doi.org/10.1016/j.jhydrol.2018.02.061

    Article  CAS  Google Scholar 

  • Hering D, Borja A, Carstensen J, Carvalho L, Elliott M, Feld CK, Heiskanen A-S, Johnson RK, Moe J, Pont D, Solheim AL, van de Bund W (2010) The European Water Framework Directive at the age of 10: a critical review of the achievements with recommendations for the future. Sci Total Environ 408:4007–4019. https://doi.org/10.1016/j.scitotenv.2010.05.031

    Article  ADS  CAS  PubMed  Google Scholar 

  • Hobbs NT, Hilborn R (2006) Alternatives to statistical hypothesis testing in ecology: a guide to self teaching. Ecol Appl 16(1):5–19. https://doi.org/10.1890/04-0645

    Article  PubMed  Google Scholar 

  • Jahani MS, Aghamollaei G, Eftekhari M, Saberi-Movahed F (2023) Unsupervised feature selection guided by orthogonal representation of feature space. Neurocomputing 516:61–76

    Article  Google Scholar 

  • Karami S, Saberi-Movahed F, Tiwari P, Marttinen P, Vahdati S (2023) Unsupervised feature selection based on variance-covariance subspace distance. Neural Netw

  • Kiester AR (2013) Species diversity, overview. In: Levin SA (ed) Encyclopedia of biodiversity, 2nd edn. Academic Press, Waltham, pp 706–714

    Chapter  Google Scholar 

  • Kim S, Alizamir M, Zounemat-Kermani M, Kisi O, Singh VP (2020) Assessing the biochemical oxygen demand using neural networks and ensemble tree approaches in South Korea. J Environ Manag 270:110834. https://doi.org/10.1016/j.jenvman.2020.110834

    Article  CAS  Google Scholar 

  • Kisi O, Sanatipour M, Hashemi A, Teimourzadeh K, Shiri J (2013) Modeling of dissolved oxygen in river water using artificial intelligence techniques. J Environ Inf 22(2):92–101

    Article  Google Scholar 

  • Kisi O, Alizamir M, Docheshmeh Gorgij A (2020) Dissolved oxygen prediction using a new ensemble method. Environ Sci Pollut Res 27(9):9589–9603. https://doi.org/10.1007/s11356-019-07574-w

    Article  CAS  Google Scholar 

  • Kuhar U, Germ M, Gaberščik A, Urbanič G (2011) Development of a River Macrophyte Index (RMI) for assessing river ecological status. Limnologica 41(3):235–243. https://doi.org/10.1016/j.limno.2010.11.001

    Article  Google Scholar 

  • Mehrpooya A et al (2022) High dimensionality reduction by matrix factorization for systems pharmacology. Brief Bioinform 23(1):410

    Article  Google Scholar 

  • Millie DF, Weckman GR, Young WA, Ivey JE, Carrick HJ, Fahnenstiel GL (2012) Modeling microalgal abundance with artificial neural networks: demonstration of a heuristic ‘Grey-Box’ to deconvolve and quantify environmental influences. Environ Model Softw 38:27–39. https://doi.org/10.1016/j.envsoft.2012.04.009

    Article  Google Scholar 

  • Mokhtar A, Elbeltagi A, Gyasi-Agyei Y, Al-Ansari N, Abdel-Fattah MK (2022) Prediction of irrigation water quality indices based on machine learning and regression models. Appl Water Sci 12(4):76. https://doi.org/10.1007/s13201-022-01590-x

    Article  ADS  CAS  Google Scholar 

  • Moore JC (2013) Diversity, Taxonomic versus Functional. In: Levin SA (ed) Encyclopedia of biodiversity, 2nd edn. Academic Press, Waltham, pp 648–656

    Chapter  Google Scholar 

  • Moss B (2010) Ecology of freshwaters. Wiley-Blackwell, Oxford

    Google Scholar 

  • Najafzadeh M, Homaei F, Farhadi H (2021) Reliability assessment of water quality index based on guidelines of national sanitation foundation in natural streams: integration of remote sensing and data-driven models. Artif Intell Rev 54(6):4619–4651. https://doi.org/10.1007/s10462-021-10007-1

    Article  Google Scholar 

  • O’Hare MT, Aguiar FC, Asaeda T, Bakker ES, Chambers PA, Clayton JS, Elger A, Ferreira TM, Gross EM, Gunn ID, Gurnell AM, Hellsten S, Hofstra DE, Li W, Mohr S, Puijalon S, Szoszkiewicz K, Willby NJ, Wood KA (2018) Plants in aquatic ecosystems: current trends and future directions. Hydrobiologia 812:1–11

    Article  Google Scholar 

  • Olaya-Marín EJ, Martínez-Capel F, Vezza P (2013) A comparison of artificial neural networks and random forests to predict native fish species richness in Mediterranean rivers. Knowl Manag Aquatic Ecosyst. https://doi.org/10.1051/kmae/2013052

    Article  Google Scholar 

  • Ormerod SJ (2014) Rebalancing the philosophy of river conservation. Aquat Conserv Mar Freshwat Ecosyst 24(2):147–152. https://doi.org/10.1002/aqc.2452

    Article  ADS  Google Scholar 

  • Park YS, Lek S (2016) Chapter 7—Artificial neural networks: multilayer perceptron for ecological modeling. In: Jørgensen SE (ed) Developments in environmental modelling. Elsevier, Amsterdam, pp 123–140

    Google Scholar 

  • Quinlan JR (1992) Learning with continuous classes

  • Rajaee T, Shahabi A (2016) Evaluation of wavelet-GEP and wavelet-ANN hybrid models for prediction of total nitrogen concentration in coastal marine waters. Arab J Geosci 9(3):176. https://doi.org/10.1007/s12517-015-2220-x

    Article  CAS  Google Scholar 

  • Raven P, Holmes NTH, Dawson FH, Everard M (1998) Quality assessment using river habitat survey data. Aquat Conserv Mar Freshw Ecosyst 8:477–499

    Article  ADS  Google Scholar 

  • Reyjol Y, Argillier C, Bonne W, Borja A, Buijse AD, Cardoso AC, Daufresne M, Kernan M, Ferreira MT, Poikane S (2014) Assessing the ecological status in the context of the European Water Framework Directive: where do we go now? Sci Total Environ 497:332–344

    Article  ADS  PubMed  Google Scholar 

  • Rocha JC, Peres CK, Buzzo JLL, de Souza V, Krause EA, Bispo PC, Frei F, Costa LSM, Branco CCZ (2017) Modeling the species richness and abundance of lotic macroalgae based on habitat characteristics by artificial neural networks: a potentially useful tool for stream biomonitoring programs. J Appl Phycol 29(4):2145–2153

    Article  Google Scholar 

  • Saberi-Movahed F et al (2022) Decoding clinical biomarker space of Covid-19: exploring matrix factorization-based feature selection methods. Comput Biol Med 146:105426

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Seliger C, Zeiringer B (2018) River connectivity, habitat fragmentation and related restoration measures. In: Schmutz S, Sendzimir J (eds) Riverine ecosystem management. Aquatic ecology series, vol 8. Springer, Cham

    Google Scholar 

  • Shah V, Jagupilla SCK, Vaccari DA, Gebler D (2021) Non-linear visualization and importance ratio analysis of multivariate polynomial regression ecological models based on river hydromorphology and water quality. Water 13(19):2708

    Article  CAS  Google Scholar 

  • Simpson EH (1949) Measurement of diversity. Nature 163(4148):688–688

    Article  ADS  Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • Staniszewski R, Szoszkiewicz K, Zbierska J, Lesny J, Jusik S, Clarke RT (2006) Assessment of sources of uncertainty in macrophyte surveys and the consequences for river classification. Hydrobiologia 566(1):235–246. https://doi.org/10.1007/s10750-006-0093-4

    Article  Google Scholar 

  • Szoszkiewicz K, Zgola T, Giełczewski M, Stelmaszczyk M (2009) Zastosowanie metody River Habitat Survey do waloryzacji hydromorfologicznej i oceny skutkow planowanych dzialan renaturyzacyjnych. Górnictwo i Geologia 6(3):25

    Google Scholar 

  • Szoszkiewicz K, Budka A, Pietruczuk K, Kayzer D, Gebler D (2016) Is the macrophyte diversification along the trophic gradient distinct enough for river monitoring?. Environ Monit Assess 189(1):4

    Article  PubMed Central  PubMed  Google Scholar 

  • Szoszkiewicz K, Jusik S, Pietruczuk K, Gebler D (2019) The macrophyte index for rivers (MIR) as an advantageous approach to running water assessment in local geographical conditions. Water 12:108

    Article  Google Scholar 

  • Szoszkiewicz K, Jusik S, Pietruczuk K, Gebler D (2020) The macrophyte index for rivers (MIR) as an advantageous approach to running water assessment in local geographical conditions. Water 12(1):108

    Article  CAS  Google Scholar 

  • Wang Y, Witten IH (1996) Induction of model trees for predicting continuous classes (Working paper 96/23). University of Waikato, Department of Computer Science, Hamilton, New Zealand

  • Wiegleb G, Bröring U, Filetti M, Brux H, Herr W (2014) Long-term dynamics of macrophyte dominance and growth—form types in two north-west German lowland streams. Freshw Biol 59:1012–1025

    Article  Google Scholar 

  • Wiegleb G, Herr W, Zander B, Bröring U, Brux H, van de Weyer K (2015) Natural variation of macrophyte vegetation of lowland streams at the regional level. Limnologica 51:53–62

    Article  Google Scholar 

  • Willby N, Pitt JA, Phillips G (2009) The ecological classification of UK rivers using aquatic macrophytes. Environment Agency, Science Report

  • Willmott CJ (1981) On the validation of models. Phys Geogr 2(2):184–194

    Article  Google Scholar 

  • Wu N, Huang J, Schmalz B, Fohrer N (2014) Modeling daily chlorophyll a dynamics in a German lowland river using artificial neural networks and multiple linear regression approaches. Limnology 15(1):47–56

    Article  Google Scholar 

  • Zieliński P, Suchowolec T (2013) Hydromorphological assessment of the anastomosing section of the Narew River after restoration. Limnol Rev 13(1):51–59

    Article  Google Scholar 

  • Zounemat-Kermani M, Seo Y, Kim S, Ghorbani MA, Samadianfard S, Naghshara S, Kim NW, Singh VP (2019) Can decomposition approaches always enhance soft computing models? Predicting the dissolved oxygen concentration in the St. Johns River Florida. Appl Sci 9(12):2534

    Article  CAS  Google Scholar 

  • Zounemat-Kermani M, Alizamir M, Fadaee M, Sankaran Namboothiri A, Shiri J (2021) Online sequential extreme learning machine in river water quality (turbidity) prediction: a comparative study on different data mining approaches. Water Environ J 35(1):335–348

    Article  Google Scholar 

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Funding

The study of Polish rivers was funded by the National Science Centre (Poland) (decision number DEC-2012/05/N/NZ9/02375).

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Authors and Affiliations

  1. Department of Water Engineering, Faculty of Civil and Surveying Engineering, Graduate University of Advanced Technology, Kerman, 76315-117, Iran

    Mohammad Najafzadeh & Elahe Sadat Ahmadi-Rad

  2. Department of Ecology and Environmental Protection, Poznan University of Life Sciences, Wojska Polskiego 28, 60-637, Poznan, Poland

    Daniel Gebler

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  1. Mohammad Najafzadeh
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  2. Elahe Sadat Ahmadi-Rad
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  3. Daniel Gebler
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Contributions

MN; Performing AI models and statistical analysis of the results, Wrting-original draft presentation; Conceptualizing; Writing—review and editing; ESA-R; Performing statistical analysis; Writing—draft preparation, Writing—review and editing; DG; Field research, Collecting and providing data; Writing—review and editing, and Conceptualizing.

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Correspondence to Mohammad Najafzadeh.

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Najafzadeh, M., Ahmadi-Rad, E.S. & Gebler, D. Ecological states of watercourses regarding water quality parameters and hydromorphological parameters: deriving empirical equations by machine learning models. Stoch Environ Res Risk Assess 38, 665–688 (2024). https://doi.org/10.1007/s00477-023-02593-z

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