Prediction of daily chlorophyll a concentration in rivers by water quality parameters using an efficient data driven model: online sequential extreme learning machine

• Autorzy: Alizamir Meysam , Heddam Salim , Kim Sungwon , Gorgij Alireza Docheshmeh , Li Peiyue , Ahmed Kaywan Othman , Singh Vijay P.

Identyfikatory

DOI

10.1007/s11600-021-00678-3

• Języki publikacji: EN

Abstrakty

EN

One of the main concerns of environmental and ecological managers for rivers, lakes, reservoirs, and marine ecosystems is developing a reliable and efficient predictive model for chlorophyll a concentration. In this study, the online sequential extreme learning machine, M5 Prime tree, multilayer perceptron artificial neural network, response surface methodology, and multivariate adaptive regression spline models were investigated for daily chlorophyll a concentration prediction by assessing the relations between Chl-a and several water quality parameters, including water temperature, pH, specific conductance, and turbidity. Different scenarios based on TE, pH, SC, and TU were defined. Also, this study evaluated the influence of periodicity input as the last scenario to obtain more accurate predictions of Chl-a values. Daily data measured for 2009–2019 from USGS no. 14207200 and USGS no. 14211720 stations were used. For assessing the prediction performance of the proposed techniques, three different objective indicators were employed, namely RMSE, R² , and NSE. Moreover, the Taylor diagram was employed for evaluating the accuracy and generalization capability of the applied models for the prediction of Chl-a. Results indicated that OS-ELM with input parameters of TE, pH, SC, TU, Y (year), M (month), and D (day) showed higher accuracy in predicting Chl-a with RMSE of 3.151, NSE of 0.798, and R of 0.894 for USGS no. 14207200 and with RMSE of 0.907, NSE of 0.820, and R of 0.912 for USGS no. 14211720 than the other models, respectively. Additionally, MLPNN ranked as the second best method for the estimation of Chl-a values at both stations. As an interesting point, it was quite evident that adding periodicity as an input parameter could significantly enhance the performance of all models in predicting the daily Chl-a concentration at both stations. Results proved that OS-ELM models can be a reliable tool for the prediction of the Chl-a values in aquatic environments, benefiting ecological and environmental management, and algal bloom control.

Słowa kluczowe

EN

OS-ELM; water quality; prediction; chlorophyll a concentration

PL

OS-ELM; jakość wody; prognoza

• Wydawca: Instytut Geofizyki PAN ; Springer
• Czasopismo: Acta Geophysica
• Rocznik: 2021
• Tom: Vol. 69, no. 6
• Strony: 2339--2361
• Opis fizyczny: Bibliogr. 64 poz..

Twórcy

autor

Alizamir Meysam

• Department of Civil Engineering, Hamedan Branch, Islamic Azad University, Hamedan, Iran

autor

Heddam Salim

• Faculty of Science, Agronomy Department, Hydraulics Division, Laboratory of Research in Biodiversity Interaction Ecosystem and Biotechnology, University 20 Août 1955, Route El Hadaik, BP 26, Skikda, Algeria

autor

Kim Sungwon

• Department of Railroad Construction and Safety Engineering, Dongyang University, Yeongju, Republic of Korea

autor

Gorgij Alireza Docheshmeh

• Faculty of Industry and Mining (Khash), University of Sistan and Baluchestan, Khash, Iran

autor

Li Peiyue

• School of Water and Environment, Chang’an University, No. 126 Yanta Road, Xi’an 710054, Shaanxi, China
• Key Laboratory of Subsurface Hydrology and Ecological Efects in Arid Region of the Ministry of Education, Chang’an University, No. 126 Yanta Road, Xi’an 710054, Shaanxi, China

autor

Ahmed Kaywan Othman

• Department of Civil Engineering, Faculty of Engineering, Tishk International University, Sulaimani, Iraq

autor

Singh Vijay P.

• Department of Biological and Agricultural Engineering & Zachry Department of Civil and Environmental Engineering, Texas A&M University, 321 Scoates Hall, 2117 TAMU, College Station, TX 77843-2117, USA
• National Water Center, UAE University, Al Ain 17666, UAE

Bibliografia

• 1. Alizamir M, Kisi O, Zounemat-Kermani M (2018) Modelling long-term groundwater fluctuations by extreme learning machine using hydro-climatic data. Hydrol Sci J 63(1):63–73
• 2. Alizamir M, Kim S, Kisi O, Zounemat-Kermani M (2020a) Deep echo state network: a novel machine learning approach to model dew point temperature using meteorological variables. Hydrol Sci J 65(7):1173–1190
• 3. Alizamir M, Kim S, Zounemat-Kermani M, Heddam S, Kim NW, Singh VP (2020c) Kernel extreme learning machine: an efficient model for estimating daily dew point temperature using weather data. Water 12(9):2600
• 4. Alizamir M, Kim S, Zounemat-Kermani M, Heddam S, Shahrabadi AH, Gharabaghi B (2021b) Modelling daily soil temperature by hydro-meteorological data at different depths using a novel data-intelligence model: deep echo state network model. Artif Intell Rev 54(4):2863–2890
• 5. Alizamir M, Kim S, Kisi O, Zounemat-Kermani M (2020b) A comparative study of several machine learning based non-linear regression methods in estimating solar radiation: case studies of the USA and Turkey regions. Energy 197:117239
• 6. Alizamir M, Heddam S, Kim S, Mehr AD (2021a) On the implementation of a novel data-intelligence model based on extreme learning machine optimized by bat algorithm for estimating daily chlorophyll-a concentration: case studies of river and lake in USA. J Clean Prod 285:124868
• 7. Askarzadeh A (2016) A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput Struct 169:1–12
• 8. Çelik Ö, Teke A, Yıldırım HB (2016) The optimized artificial neural network model with Levenberg–Marquardt algorithm for global solar radiation estimation in Eastern Mediterranean Region of Turkey. J Clean Prod 116:1–12
• 9. Chen H, Kim AS (2006) Prediction of permeate flux decline in crossflow membrane filtration of colloidal suspension: a radial basis function neural network approach. Desalination 192:415–428
• 10. Cho S, Lim B et al (2014) Factors affecting algal blooms in a man-made lake and prediction using an artificial neural network. Measurement 53:224–233
• 11. de Oliveira TF, de Sousa Brandao IL, Mannaerts CM, Hauser-Davis RA, de Oliveira AAF, Saraiva ACF, Ishihara JH (2020) Using hydrodynamic and water quality variables to assess eutrophication in a tropical hydroelectric reservoir. J Environ Manag 256:109932
• 12. Deo RC, Tiwari MK, Adamowski JF, Quilty JM (2017) Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model. Stoch Env Res Risk Assess 31(5):1211–1240
• 13. Dimberg PH, Bryhn AC, Hytteborn JK (2013) Probabilities of monthly median chlorophyll-a concentrations in subarctic, temperate and subtropical lakes. Environ Model Softw 41:199–209
• 14. Dornier M, Decloux M, Trystram G, Lebert A (1995) Dynamic modeling of crossflow microfiltration using neural networks. J Membr Sci 98:263–273
• 15. Du Z, Qin M, Zhang F, Liu R (2018) Multistep-ahead forecasting of chlorophyll a using a wavelet nonlinear autoregressive network. Knowl-Based Syst 160:61–70
• 16. Fallah H, Kisi O, Kim S, Rezaie-Balf M (2019) A new optimization approach for the least-cost design of water distribution networks: improved crow search algorithm. Water Resour Manage 33(10):3595–3613
• 17. Fijani E, Barzegar R, Deo R, Tziritis E, Konstantinos S (2019) Design and implementation of a hybrid model based on two-layer decomposition method coupled with extreme learning machines to support real-time environmental monitoring of water quality parameters. Sci Total Environ 648:839–853
• 18. Friedman JH (1991) Multivariate adaptive regression splines. Ann Stat 19(1):1–67
• 19. García-Nieto PJ, García-Gonzalo E, Fernández JA, Muñiz CD (2019) Modeling of the algal atypical increase in La Barca reservoir using the DE optimized least square support vector machine approach with feature selection. Math Comput Simul 166:461–480
• 20. García-Nieto PJ, García-Gonzalo E, Lasheras FS, Fernández JA, Muñiz CD (2020) A hybrid DE optimized wavelet kernel SVR-based technique for algal atypical proliferation forecast in La Barca reservoir: a case study. J Comput Appl Math 366:112417
• 21. Goldberg DE, Holland JH (1988) Genetic algorithms and machine learning. Mach Learn 3(2):95–99
• 22. Guo W, Xu T, Tang K, Yu J, Chen S (2018) Online sequential extreme learning machine with generalized regularization and adaptive forgetting factor for time-varying system prediction. Math Probl Eng 2018:1–22
• 23. He X, Li P (2020) Surface water pollution in the middle Chinese Loess Plateau with special focus on hexavalent chromium (Cr 6+): occurrence, sources and health risks. Expo Health. https://doi.org/10.1007/s12403-020-00344-x
• 24. He J, Chen Y, Wu J, Stow DA, Christakos G (2020) Space-time chlorophyll-a retrieval in optically complex waters that accounts for remote sensing and modeling uncertainties and improves remote estimation accuracy. Water Res 171:115403
• 25. 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
• 26. Hintze JL, Nelson RD (1998) Violin plots: a box plot-density trace synergism. Am Stat 52(2):181–184
• 27. Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501
• 28. Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Liu HH (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc Lond A Math Phys Eng Sci 454(1971):903–995
• 29. Keshtegar B, Heddam S (2018) Modeling daily dissolved oxygen concentration using modified response surface method and artificial neural network: a comparative study. Neural Comput Appl 30(10):2995–3006
• 30. Keshtegar B, Mert C, Kisi O (2018) Comparison of four heuristic regression techniques in solar radiation modeling: Kriging method vs RSM, MARS and M5 model tree. Renew Sustain Energy Rev 81:330–341
• 31. Khosravi K, Cooper JR, Daggupati P, Pham BT, Bui DT (2020) Bedload transport rate prediction: application of novel hybrid data mining techniques. J Hydrol 585:124774
• 32. Kim HG, Hong S, Jeong KS, Kim DK, Joo GJ (2019) Determination of sensitive variables regardless of hydrological alteration in artificial neural network model of chlorophyll a: case study of Nakdong River. Ecol Model 398:67–76
• 33. Kisi O, Alizamir M (2018) Modelling reference evapotranspiration using a new wavelet conjunction heuristic method: Wavelet extreme learning machine vs wavelet neural networks. Agric for Meteorol 263:41–48
• 34. Kisi O, Alizamir M, Zounemat-Kermani M (2017) Modeling groundwater fluctuations by three different evolutionary neural network techniques using hydroclimatic data. Nat Hazards 87(1):367
• 35. Kisi O, Alizamir M, Trajkovic S, Shiri J, Kim S (2020a) Solar radiation estimation in Mediterranean climate by weather variables using a novel Bayesian model averaging and machine learning methods. Neural Process Lett 52(3):2297–2318
• 36. Kisi O, Alizamir M, Gorgij AD (2020b) Dissolved oxygen prediction using a new ensemble method. Environ Sci Pollut Res 27:9589–9603. https://doi.org/10.1007/s11356-019-07574-w
• 37. Li P, Feng W, Xue C, Tian R, Wang S (2017) Spatiotemporal variability of contaminants in lake water and their risks to human health: a case study of the Shahu Lake tourist area, northwest China. Expo Health 9(3):213–225
• 38. Liang N, Huang G, Saratchandran P, Sundararajan N (2006) A fast and accurate online sequential learning algorithm for feedforward networks. IEEE Trans Neural Netw Learning Syst 17(6):1411–1423
• 39. Liang S, Han S, Sun Z (2015) Parameter optimization method for the water quality dynamic model based on data-driven theory. Mar Pollut Bull 98(1–2):137–147
• 40. Liang Z, Zou R, Chen X, Ren T, Su H, Liu Y (2020) Simulate the forecast capacity of a complicated water quality model using the long short-term memory approach. J Hydrol 581:124432
• 41. Lin G, Li K, Liang S, Li Y, Su Y, Wang X (2020) Compound eutrophication index: an integrated approach for assessing ecological risk and identifying the critical element controlling harmful algal blooms in coastal seas. Mar Pollut Bull 150:110585. https://doi.org/10.1016/j.marpolbul.2019.110585
• 42. Lozano VA, de la Peña AM et al (2013) Four-way multivariate calibration using ultra-fast high-performance liquid chromatography with fluorescence excitation–emission detection. Application to the direct analysis of chlorophylls a and b and pheophytins a and b in olive oils. Chemom Intell Lab Syst 125:121–131
• 43. Mansour K, Decesari S, Bellacicco M, Marullo S, Santoleri R, Bonasoni P, Facchini MC, Ovadnevaite J, Ceburnis D, O’Dowd C, Rinaldi M (2020) Particulate methane sulfonic acid over the central Mediterranean Sea: source region identification and relationship with phytoplankton activity. Atmos Res 237:104837
• 44. Myers RH, Montgomery DC, Anderson-Cook CM (2016) Response surface methodology. Copyright by John Wiley & Sons Inc
• 45. Park Y, Cho KH, Park J, Cha SM, Kim JH (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
• 46. Pyo J, Duan H, Baek S, Kim MS, Jeon T, Kwon YS, Lee H, Cho KH (2019) A convolutional neural network regression for quantifying cyanobacteria using hyperspectral imagery. Remote Sens Environ 233:111350
• 47. Quinlan JR (1992) Learning with continuous classes 5th Australian joint conference on artificial intelligence. pp. 343–348
• 48. Rezaie-Balf M, Kim S, Fallah H, Alaghmand S (2019) Daily river flow forecasting using ensemble empirical mode decomposition based heuristic regression models: application on the perennial rivers in Iran and South Korea. J Hydrol 572:470–485
• 49. Seo Y, Kim S, Kisi O, Singh VJ (2015) Daily water level forecasting using wavelet decomposition and artificial intelligence techniques. J Hydrol 520:224–243
• 50. Sharma E, Deo RC et al (2020) A hybrid air quality early-warning framework: an hourly forecasting model with online sequential extreme learning machines and empirical mode decomposition algorithms. Sci Total Environ 709:135934
• 51. Sotirov S (2005) A method of accelerating neural network learning. Neural Process Lett 22(2):163–169
• 52. Suribabu CR, Neelakantan TR (2006) Design of water distribution networks using particle swarm optimization. Urban Water J 3(2):111–120
• 53. Wang L, Pu H, Sun DW (2016) Estimation of chlorophyll-a concentration of different seasons in outdoor ponds using hyperspectral imaging. Talanta 147:422–429
• 54. Wang Y, Witten IH (1997) Inducing model trees for continuous classes. Proceedings of the Ninth European Conference on Machine Learning, pp. 128–137
• 55. Wu Z, Huang NE (2009) Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv Adapt Data Anal 1(01):1–41
• 56. Wu J, Xue C, Tian R, Wang S (2017) Lake water quality assessment: a case study of Shahu Lake in the semi-arid loess area of northwest China. Environ Earth Sci 76:232. https://doi.org/10.1007/s12665-017-6516-x
• 57. Yadav B, Ch S, Mathur S, Adamowski J (2016) Discharge forecasting using an online sequential extreme learning machine (OS-ELM) model: a case study in Neckar River, Germany. Measurement 92:433–445
• 58. Yi HS, Park S, An KG, Kwak KC (2018) Algal bloom prediction using extreme learning machine models at artificial weirs in the Nakdong River, Korea. Int J Environ Res Public Health 15(10):2078
• 59. Yim I, Shin J, Lee H, Park S, Nam G, Kang T, Cho KH, Cha Y (2020) Deep learning-based retrieval of cyanobacteria pigment in inland water for in-situ and airborne hyperspectral data. Ecol Indicators 110:105879
• 60. Yu K, Lenz-Wiedemann V, Chen X, Bareth G (2014) Estimating leaf chlorophyll of barley at different growth stages using spectral indices to reduce soil background and canopy structure effects. ISPRS J Photogramm Remote Sens 97:58–77
• 61. Yu Z, Yang K, Luo Y, Shang C (2020) Spatial-temporal process simulation and prediction of chlorophyll-a concentration in Dianchi lake based on wavelet analysis and long-short term memory network. J Hydrol 582:124488
• 62. Zakhrouf M, Bouchelkia H, Stamboul M, Kim S (2020) Novel hybrid approaches based on evolutionary strategy for streamflow forecasting in the Chellif River. Algeria. Acta Geophysica 68(1):167–180
• 63. Zhan C, Gan A, Hadi M (2011) Prediction of lane clearance time of freeway incidents using the M5P tree algorithm. IEEE Trans Intell Transp Syst 12:1549–1557
• 64. Zhang W (2020) MARS applications in geotechnical engineering systems. Science Press and Springer Nature Singapore Pte Ltd