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Evaluating quality of surface water resources by ANN and ANFIS networks using Sentinel-2 satellite data

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Abstract

Using low-cost and fast technology with high accuracy to study the quality of surface water resources is one of the important goals in remote sensing studies. The main purpose of this research was to relate the Sentinel -2 A satellite images, 11 water quality parameters and a suitable model for the study area of Karun and Dez River. In this study, the Sentinel-2 satellite data were used to evaluate its capability for monitoring the quality of surface water. Also, the ground-based samples were collected from water quality sampling stations located on the Karun and Dez rivers at the same time as Sentinel-2 satellite images, and 11 water quality parameters of these samples were analyzed. After pre-processing of the used satellite images, the appropriate spectral bands and indicators were extracted by using sensitivity analysis. ANN and ANFIS neural networks were used to model the quality of water resources. In both models, remote sensing data were introduced as inputs and ground-based data as outputs. Accuracy of the modeling was estimated by calculation of the relative error and RMSE comparing the actual values and the values obtained from the modeling process. Finally, the zoning map of each water quality parameter for the Karun River was prepared using models with higher accuracy. The results of this research indicated a proper accuracy for estimating water quality parameters. According to numeral results, relative error of estimated parameters was varied from 0.049 for the concentration of Potassium (K) to with 0.152 for the concentration of Chlorine (Cl). On the other hand, ANN and ANFIS Model has shown different efficiencies regarding different parameters. The results of this study showed that ANN model performed modeling process with higher accuracy for Mg, HCO3, pH, EC, Turbidity, TDS and K parameters, and ANFIS model for Ca, Na, Cl, SO4 parameters. To be more precise, ANN model accounted for the highest modeling accuracy for K parameter and lowest accuracy for Ca. In contrast, ANFIS model formed the highest modeling accuracy for the SO4 and the lowest accuracy for the EC parameter. The results of this research indicated a capability of the remote sensing for monitoring quality of surface water resources.

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References

  • Abdelmalik KW (2018) Role of statistical remote sensing for inland water quality parameters prediction. Egyptian J Remote Sensing Space Sci 21:193–200

    Article  Google Scholar 

  • Alizadeh MJ, Kavianpoura MR, Danesh M, Adolf J, Shamshirband SH, Chau K-W (2018) Effect of river flow on the quality of estuarine and coastal waters using machine learning models. Eng Appl Comput Fluid Mech 12(1):810–823

    Google Scholar 

  • Alves MM, Pirmez L, Rossetto S, Delicato FC, de Farias CM, Pires PF, Zomaya AY (2017) Damage prediction for wind turbines using wireless sensor and actuator networks. J Netw Comput Appl 80:123–140

    Article  Google Scholar 

  • Avdan ZY, Kaplan G, Goncu S, Avdan U (2019) Monitoring the water quality of small water bodies using high resolution remote sensing data. Int J Geo-Info 8, 553:1–12

    Google Scholar 

  • Bagherian MM, Akhoundali AM, Moazed H, Jaafarzadeh N, Ahadian J, Hasoonizadeh H (2014) Evaluation of Karun river water quality scenarios using simulation model results. pp. 339–358

  • Baillarin S, Meygret A, Dechoz C, Petrucci B, Lacherade S, Tremas T, Spoto F (2012) Sentinel-2 level 1 products and image processing performances. Paper presented at the Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International

  • Barrett DC, Frazier AE (2016) Automated method for monitoring water quality using Landsat imagery. Water 8(6):257

    Article  Google Scholar 

  • Bonansea M, Rodriguez MC, Pinotti L, Ferrero S (2015) Using multi-temporal Landsat imagery and linear mixed models for assessing water quality parameters in río Tercero reservoir (Argentina). Remote Sens Environ 158:28–41

    Article  Google Scholar 

  • Bui DT, Pradhan B, Nampak H, Bui Q-T, Tran Q-A, Nguyen Q-P (2016) Hybrid artificial intelligence approach based on neural fuzzy inference model and metaheuristic optimization for flood susceptibility modeling in a high-frequency tropical cyclone area using gis. J Hydrol 540:317–330

    Article  Google Scholar 

  • Chen X-Y, Chau K-W (2019) Uncertainty analysis on hybrid double feed forward neural network model for sediment load estimation with LUBE method. Water Resour Manag 33(10):3563–3577

    Article  Google Scholar 

  • Chen K, Guo S, Lin Y, Ying Z (2010) Least absolute relative error estimation. J Am Stat Assoc 105(491):1104–1112

    Article  Google Scholar 

  • Elhag M, Gitas IZ, Othman A, Bahrawi J (2019) Effect of water surface area on the remotely sensed water quality parameters of Baysh dam lake, Saudi Arabia. Hydrol Earth Syst Sci Discus 308:1–20

    Google Scholar 

  • ESA5. (2016) Sentinel-2-msi user guides (product types)

  • ESA6. (2016) Science toolbox exploitation platform (sen2cor). Retrieved from http://step.esa.int/main/third-party-plugins-2/sen2cor

  • Feyisa GL, Meilby H, Fensholt R, Proud SR (2014) Automated water extraction index: a new technique for surface water mapping using Landsat imagery. Remote Sens Environ 140:23–35

    Article  Google Scholar 

  • Guo Q, Wu X, Bing Q, Pan Y, Wang Z, Fu Y, Liu J (2016) Study on retrieval of chlorophyll-a concentration based on Landsat OLI imagery in the Haihe River, China. Sustainability 8(8):758

    Article  Google Scholar 

  • Hanqiu X (2006) Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int J Remote Sens 27(14):3025–3033

    Article  Google Scholar 

  • https://scihub.copernicus.eu/dhus/#/home (Copernicus Open Access Hub)

  • Iran I. o. S. a. I. R. o (2011) Features of drinking water. In (Vol. 1053 pp. 25). Iran: Institute of Standards and Industrial Research of Iran (Persian)

  • Islamic Republic of Iran Vice Presidency For Strategic Planning and Supervision (2011) Instruction for surface water quality monitoring, vol 522. Ministry of Energy (Persian), Iran, pp 222

  • Jachner S, Van den Boogaart G, Petzoldt T (2007) Statistical methods for the qualitative assessment of dynamic models with time delay (r package qualv). J Stat Softw 22(8):1–30

    Article  Google Scholar 

  • Jang JSR (1993) ANFIS: adaptive network-based fuzzy inference system. IEEE Trans Syst Man Cybernet 23(3):665–685

    Article  Google Scholar 

  • Kargar K, Samadianfard S, Parsa J, Nabipour N, Shamshirband SH, Mosavi A, Chau K-W (2020) Estimating longitudinal dispersion coefficient in natural streams using empirical models and machine learning algorithms. Eng Appl Comput Fluid Mech 14(1):311–322

    Google Scholar 

  • Keshavarzi A, Sarmadian F, Shiri J, Iqbal M, Tirado-Corbalá R, Omran E-SE (2017) Application of ANFIS-based subtractive clustering algorithm in soil cation exchange capacity estimation using soil and remotely sensed data. Measurement 95:173–180

    Article  Google Scholar 

  • Kurtulus B, Flipo N, Goblet P (2010) Sensitivity analysis on an adaptive neuro fuzzy inference system (ANFIS) for hydraulic head interpolation: The orgeval experimental site/France. Paper presented at the XVIII International Conference on Water Resources CMWR

  • Laili N, Arafah F, Jaelani L, Subehi L, Pamungkas A, Koenhardono E, Sulisetyono A (2015) Development of water quality parameter retrieval algorithms for estimating total suspended solids and chlorophyll-a concentration using Landsat-8 imagery at Poteran island water. ISPRS Ann Photogrammetry Remote Sensing Spatial Info Sci 2(2):55

    Article  Google Scholar 

  • Liu J, Zhang Y, Yuan D, Song X (2015) Empirical estimation of total nitrogen and total phosphorus concentration of urban water bodies in China using high resolution Ikonos multispectral imagery. Water 7(11):6551–6573

    Article  Google Scholar 

  • Main-Knorn M, Pflug B, Debaecker V, Louis J (2015) Calibration and validation plan for the l2a processor and products of the sentinel-2 mission. Int Arch Photogrammetry Remote Sensing Spatial Info Sci 40(7):1249

    Article  Google Scholar 

  • Muller-Wilm U, Louis J, Richter R, Gascon F, Niezette M (2013) Sentinel-2 level 2a prototype processor: Architecture, algorithms and first results. Paper presented at the Proceedings of the 2013 ESA Living Planet Symposium, Edinburgh, UK

  • Mustaffa Z, Yusof Y (2011) A comparison of normalization techniques in predicting dengue outbreak. Paper presented at the International Conference on Business and Economics Research

  • Pyo J, Pachepsky Y, Baek S-S, Kwon Y, Kim M, Lee H, Nam G (2017) Optimizing semi-analytical algorithms for estimating chlorophyll-a and phycocyanin concentrations in inland waters in Korea. Remote Sens 9(6):542

    Article  Google Scholar 

  • Rangzan K, Kabolizadeh M, Karimi D (2020) Improved water quality mapping based on cross-fusion of Sentinel-2 and Landsat-8 imageries. IET Image Process 14(7):1382–1392

    Article  Google Scholar 

  • Sahu M, Mahapatra SS, Sahu HB, Patel RK (2011) Prediction of water quality index using neuro fuzzy inference system. Water Qual Expo Health 3(3–4):175–191

    Article  Google Scholar 

  • Shamshirband SH, Nodoushan EJ, Adolf JE, Manaf AA, Mosavi A, Chau K-W (2019) Ensemble models with uncertainty analysis for multi-day ahead forecasting of chlorophyll a concentration in coastal waters. Eng Appl Comput Fluid Mech 13(1):91–101

    Google Scholar 

  • Sherfey JS, Soplata AE, Ardid S, Roberts EA, Stanley DA, Pittman-Polletta BR, Kopell NJ (2018) DynaSim: a MATLAB toolbox for neural modeling and simulation. Front Neuro Info 12:10

    Google Scholar 

  • Su T-C, Chou H-T (2015) Application of multispectral sensors carried on unmanned aerial vehicle (UAV) to trophic state mapping of small reservoirs: a case study of Tain-pu reservoir in Kinmen. Taiwan Remote Sensing 7(8):10078–10097

    Article  Google Scholar 

  • Talebizadeh M, Moridnejad A (2011) Uncertainty analysis for the forecast of lake level fluctuations using ensembles of ANN and ANFIS models. Expert Syst Appl 38(4):4126–4135

    Article  Google Scholar 

  • Tiyasha, Tung TM, Yaseen ZM (2020) A survey on river water quality modelling using artificial intelligence models: 2000–2020. J Hydrol 585:124670

    Article  Google Scholar 

  • Toming K, Kutser T, Laas A, Sepp M, Paavel B, Nõges T (2016) First experiences in mapping lake water quality parameters with sentinel-2-msi imagery. Remote Sens 8(8):640

    Article  Google Scholar 

  • Uysal G, Şensoy A, Şorman AA (2016) Improving daily streamflow forecasts in mountainous upper euphrates basin by multi-layer perceptron model with satellite snow products. J Hydrol 543:630–650

    Article  Google Scholar 

  • Wang L, Kisi O, Zounemat-Kermani M, Li H (2017) Pan evaporation modeling using six different heuristic computing methods in different climates of China. J Hydrol 544:407–427

    Article  Google Scholar 

  • Xie T, Yu H, Wilamowski B (2011) Comparison between traditional neural networks and radial basis function networks. IEEE Int Symp Ind Electron:1194–1199

  • Yan H, Zou Z, Wang H (2010) Adaptive neuro fuzzy inference system for classification of water quality status. J Environ Sci 22(12):1891–1896

    Article  Google Scholar 

  • Zhou Y (2020) Real-time probabilistic forecasting of river water quality under data missing situation: deep learning plus post-processing techniques. J Hydrol 589:125164

    Article  Google Scholar 

  • Zhou G, Ma Z, Sathyendranath S, Platt T, Jiang C, Sun K (2018) Canopy reflectance modeling of aquatic vegetation for algorithm development: global sensitivity analysis. Remote Sens 10(6):837

    Article  Google Scholar 

  • Zolfaghari K, Duguay CR (2016) Estimation of water quality parameters in Lake Erie from Meris using linear mixed effect models. Remote Sens 8(6):473

    Article  Google Scholar 

Download references

Acknowledgments

We are grateful to the Research Council of Shahid Chamran University of Ahvaz for financial support (SCU.EG98.26151).

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Authors and Affiliations

  1. Department of Remote Sensing and GIS, Faculty of Earth Sciences, Shahid Chamran University of Ahvaz, Ahvaz, Iran

    Mostafa Kabolizadeh, Kazem Rangzan, Sajad Zareie & Hossein Delfan

  2. Surveying Engineering department, Civil Engineering faculty, Jundi Shapur University of Technology, Dezful, Iran

    Mohsen Rashidian

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  1. Mostafa Kabolizadeh
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  2. Kazem Rangzan
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  3. Sajad Zareie
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  4. Mohsen Rashidian
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Correspondence to Mostafa Kabolizadeh.

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Communicated by: H. Babaie

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Kabolizadeh, M., Rangzan, K., Zareie, S. et al. Evaluating quality of surface water resources by ANN and ANFIS networks using Sentinel-2 satellite data. Earth Sci Inform 15, 523–540 (2022). https://doi.org/10.1007/s12145-021-00741-z

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