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Modeling the Relationship between Catchment Attributes and In-stream Water Quality

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Abstract

The physical attributes of catchments have a significant influence on the chemistry and physical features of in-stream water quality. Consequently, modeling this relationship is important for informing management strategies aimed at improving regional water quality. This study used a machine learning approach (Artificial Neural Networks or ANNs) to model the relationship between land use/cover, associated with other physical attributes of the catchment such as geological permeability and hydrologic soil groups, and in-stream water quality parameters (e.g., K+, Na+, Mg2+, Ca2+, SO4 2−, Cl−, HCO3 −, SAR, pH, EC, TDS). Eighty-eight catchments in the southern basins of the Caspian Sea were explored. To enhance the architecture of ANNs, the study applied backward elimination-based multiple linear regression, through which the optimum input nodes of ANNs can be determined amongst the most relevant variables. A transformation approach was also applied to qualify the performance of ANNs in four quality classes, ranging from unsatisfactory to very good. According to the findings, ANN based TDS model performance improved from unsatisfactory to very good. However, the linear regression-based pH model resulted in a decrease in performance, from “very good” to satisfactory. Moreover, among all catchment attributes, urban areas had the greatest impact on K+, Na+, Mg2+, Cl−, SO4 2−, EC and SAR concentration values. K+, TDS and EC were influenced by agricultural area (%). Bare land areas (%) had the largest impact on Na+, Ca2+ and HCO3 −. Assessing the performance of the ANN-based models developed in this study indicates that 10 out of 11 models had “very good” quality ratings and can be reliably used in practice.

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References

  • Ahearn DS, Sheibley RW, Dahlgren RA, Anderson M, Johnson J, Tate KW (2005) Land use and land cover influence on water quality in the last free-flowing river draining the western Sierra Nevada. Calif J Hydrol 313:234–247. doi:10.1016/j.jhydrol.2005.02.038

    Article  Google Scholar 

  • Anderson JR (1976) A land use and land cover classification system for use with remote sensor data. Vol. 964. US Government Printing Office

  • Adamowski J, Chan H, Prasher S, Sharda VN (2012) Comparison of multivariate adaptive regression splines with coupled wavelet transform artificial neural networks for runoff forecasting in Himalayan micro-watersheds with limited data. J Hydroinf 3:731–744

    Article  Google Scholar 

  • Allan JD, Erickson DL, Fay J (1997) The influence of catchment land use on stream integrity across multiple spatial scales. Freshw Biol 37:149–161

    Article  Google Scholar 

  • Alvisi S, Mascellani G, Franchini M, Bardossy A (2006) Water level forecasting through fuzzy logic and artificial neural network approaches. Hydrol Earth Syst Sci Discuss 10:1–17

    Article  Google Scholar 

  • Amiri B, Nakane K (2009) Modeling the linkage between river water quality and landscape metrics in the Chugoku district of Japan. Water Resour Manag 23:931–956. doi:10.1007/s11269-008-9307-z

    Article  Google Scholar 

  • Amiri B, Sudheer K, Fohrer N (2012) Linkage between in-stream total phosphorus and land cover in Chugoku district, Japan: an ANN approach. J Hydrol Hydromechanics 60:33–44

    Google Scholar 

  • Basnyat P, Teeter L, Lockaby BG, Flynn KM (2000) RESEARCH: land Use characteristics and water quality: a methodology for valuing of forested buffers. Environ Manag 26:153–161. doi:10.1007/s002670010078

    Article  Google Scholar 

  • Belayneh A, Adamowski J, Khalil B, Ozga-Zielinski B (2014) Long-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet-support vector regression models. J Hydrol 508:418–429

    Article  Google Scholar 

  • Bolstad PV, Swank WT (1997) Cumulative impacts of landuse on water quality in a southern Appalachian watershed. J Am Water Resour Assoc 33:519–534

    Article  Google Scholar 

  • Bormann FH, Likens GE (1969) The watershed-ecosystem concept and studies of nutrient cycles. In: VanDyne GM (ed) Ecosystem concept in natural resources management, Chapter IV. Academic Press, Inc., New York, pp 49–79

  • Brakensiek D, Rawls W (1983) Agricultural management effects on soil-water Processes 2: Green and Ampt Parameters for Crusting Soils. Trans ASAE 26:1753–1757

    Article  Google Scholar 

  • Campisi S, Adamowski J, Oron G (2012) Forecasting urban water demand via wavelet- denoising and neural network models. Case study: city of Syracuse, Italy. Water Resour Manag 26:3539–3558

    Article  Google Scholar 

  • Chu T, Shirmohammadi A (2004) Evaluation of the SWAT model's hydrology component in the Piedmont physiographic region of Maryland. Trans ASAE 47:1057–1073

    Article  Google Scholar 

  • Committee AT (2000) Artificial neural networks in hydrology. I: preliminary concepts. J Hydrol Eng 5:115–123

    Article  Google Scholar 

  • Correll D, Friebele E, Miklas J (1977) Nutrients in land runoff from Rhode river catchments in 1975 and 1976 Nonpoint source studies on Cheasapeake Bay 2

  • Cuffney TF, Meador MR, Porter SD, Gurtz ME (2000) Responses of physical, chemical, and biological indicators of water quality to a gradient of agricultural land use in the Yakima River Basin, Washington. In: Monitoring Ecological Condition in the Western United States. Springer, pp 259–270

  • Daniell T (1991) Neural networks. Applications in hydrology and water resources engineering. In: National Conference Publication- Institute of Engineers. Australia

  • Dawson C, Wilby R (2001) Hydrological modelling using artificial neural networks. Prog Phys Geogr 25:80–108

    Article  Google Scholar 

  • de Paul Obade V, Lal R, Moore R (2014) Assessing the accuracy of soil and water quality characterization using remote sensing. Water Resour Manag 28:5091–5109. doi:10.1007/s11269-014-0796-7

    Article  Google Scholar 

  • Dibike YB, Solomatine DP (2001) River flow forecasting using artificial neural networks. Phys Chem Earth Part B: Hydrol, Oceans Atmos 26:1–7

    Article  Google Scholar 

  • Dohner E, Markowitz A, Barbour M, Simpson J, Byrne J, Dates G (1997) Volunteer Stream Monitoring: A Methods Manual Environmental Protection Agency: Office of Water (EPA 841-B-97-003)

  • Efroymson MA (1960) Multiple regression analysis. Mathematical Methods for Digital Computers. Wiley

  • Gage E, Cooper DJ (2015) The Influence of Land Cover, Vertical Structure, and Socioeconomic Factors on Outdoor Water Use in a Western US City Water Resour Manag. 1–14

  • Haidary A, Amiri B, Adamowski J, Fohrer N, Nakane K (2013) Assessing the impacts of four land Use types on the water quality of wetlands in Japan. Water Resour Manag 27:2217–2229. doi:10.1007/s11269-013-0284-5

    Article  Google Scholar 

  • Haidary A, Jabbarian Amiri B, Adamowski J, Fohrer N, Nakane K (2014) Modelling the relationship between catchment attributes and wetland water quality in Japan. Ecohydrology. doi:10.1002/eco.1539

    Google Scholar 

  • Halbe J, Pahl-Wostl C, Sendzimir J, Adamowski J (2013) Towards adaptive and integrated management paradigms to meet the challenges of water governance. Water Sci Technol Water Supply 67:2651–2660

    Article  Google Scholar 

  • Harrel RC, Dorris TC (1968) Stream order, morphometry, physico-chemical conditions, and community structure of benthic macroinvertebrates in an intermittent stream system. Am Midl Nat 80:220–251

    Article  Google Scholar 

  • Hartmann J, Moosdorf N, Lauerwald R, Hinderer M, West AJ (2014) Global chemical weathering and associated P-release. In: EGU General Assembly Conference Abstracts. p 15640

  • Hecht-Nielsen R (1987) Kolmogorov’s mapping neural network existence theorem. Paper presented at the Proceedings of the First IEEE International Joint Conference on Neural Networks, San Diego, California

  • Higashi T, Nakane K (1985) Relationship between turbidity in a river water and land use types in the basin, Series IV Mem Faculty of Integrated Arts and Science, Hiroshima University:69–83

  • Hill AR (1981) Stream phosphorus exports from watersheds with contrasting land uses in Southern Ontario1. Wiley Online Library

  • Huang C, Wang G, Zheng X, Yu J, Xu X (2015) Simple linear modeling approach for linking hydrological model parameters to the physical features of a River Basin. Water Resour Manag 29:3265–3289. doi:10.1007/s11269-015-0996-9

    Article  Google Scholar 

  • Hynes HBN, Pentelow F (1978) The biology of polluted waters. Liverpool University

  • Iliadis LS, Maris F (2007) An artificial neural network model for mountainous water-resources management: the case of Cyprus mountainous watersheds. Environ Model Softw 22:1066–1072

    Article  Google Scholar 

  • Ji Y, Huang G, Sun W (2015) Nonpoint-source water quality management under uncertainty through an inexact double-sided chance-constrained model. Water Resour Manag 29:3079–3094. doi:10.1007/s11269-015-0983-1

    Article  Google Scholar 

  • Kalin L, Isik S, Schoonover JE, Lockaby BG (2010) Predicting water quality in unmonitored watersheds using artificial neural networks All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. J Environ Qual 39:1429–1440. doi:10.2134/jeq2009.0441

    Article  Google Scholar 

  • Karran D, Morin E, Adamowski J (2014) Multi-step streamflow forecasting using data-driven non-linear methods in contrasting climate regimes. J Hydroinf 16(3):671–689

    Article  Google Scholar 

  • Kuehne RA (1962) A classification of streams, illustrated by fish distribution in an eastern Kentucky creek. Ecology 43:608–614

    Article  Google Scholar 

  • Kuehne RA (1966) Depauperate fish faunas of sinking creeks near Mammoth Cave, Kentucky Copeia. 306–311

  • Lek S, Delacoste M, Baran P, Dimopoulos I, Lauga J, Aulagnier S (1996) Application of neural networks to modelling nonlinear relationships in ecology. Ecol Model 90:39–52

    Article  Google Scholar 

  • Likens GE, Bormann FH, Johnson NM, Fisher D, Pierce RS (1970) Effects of forest cutting and herbicide treatment on nutrient budgets in the Hubbard Brook watershed-ecosystem. Ecol Monogr 40:23–47

    Article  Google Scholar 

  • 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. doi:10.1016/j.envsoft.2010.02.003

    Article  Google Scholar 

  • Moriasi D, Arnold J, Van Liew M, Bingner R, Harmel R, Veith T (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans ASABE 50:885–900

    Article  Google Scholar 

  • Nash J, Sutcliffe J (1970) River flow forecasting through conceptual models part I—A discussion of principles. J Hydrol 10:282–290

    Article  Google Scholar 

  • Ngoye E, Machiwa JF (2004) The influence of land-use patterns in the Ruvu river watershed on water quality in the river system. Phys Chem Earth Parts A/B/C 29:1161–1166

    Article  Google Scholar 

  • Noori R, Hoshyaripour G, Ashrafi K, Araabi BN (2010) Uncertainty analysis of developed ANN and ANFIS models in prediction of carbon monoxide daily concentration. Atmos Environ 44:476–482. doi:10.1016/j.atmosenv.2009.11.005

    Article  Google Scholar 

  • Omernik JM (1976) The influence of land use on stream nutrient levels vol 76. vol 14. US Environmental Protection Agency, Office of Research and Development, Corvallis Environmental Research Laboratory, Eutrophication Survey Branch

  • Parson SC (1999) Development of an Internet watershed educational tool (INTERWET) for the Spring Creek Watershed of central Pennsylvania. Pennsylvania State University

  • Pelletier GJ, Chapra SC, Tao H (2006) QUAL2Kw–A framework for modeling water quality in streams and rivers using a genetic algorithm for calibration. Environ Model Softw 21:419–425

    Article  Google Scholar 

  • Pratt B, Chang H (2012) Effects of land cover, topography, and built structure on seasonal water quality at multiple spatial scales. J Hazard Mater 209–210:48–58. doi:10.1016/j.jhazmat.2011.12.068

    Article  Google Scholar 

  • Qi M, Zhang GP (2001) An investigation of model selection criteria for neural network time series forecasting. Eur J Oper Res 132:666–680. doi:10.1016/S0377-2217(00)00171-5

    Article  Google Scholar 

  • Reimann C, Finne TE, Nordgulen Ø, Sæther OM, Arnoldussen A, Banks D (2009) The influence of geology and land-use on inorganic stream water quality in the Oslo region, Norway. Appl Geochem 24:1862–1874. doi:10.1016/j.apgeochem.2009.06.007

    Article  Google Scholar 

  • Ren L, Zhao Z (2002) An optimal neural network and concrete strength modeling. Adv Eng Softw 33:117–130. doi:10.1016/S0965-9978(02)00005-4

    Article  Google Scholar 

  • Ryu J-S, Lee K-S, Chang H-W (2007) Hydrogeochemical and isotopic investigations of the Han River basin, South Korea. J Hydrol 345:50–60. doi:10.1016/j.jhydrol.2007.08.001

    Article  Google Scholar 

  • Salas JD, Markus M, Tokar AS (2000) Streamflow forecasting based on artificial neural networks. In: Govindaraju RS, Rao AR (eds) Artificial neural networks in hydrology, vol 36. Water science and technology library. Springer, Netherlands, pp 23–51. doi:10.1007/978-94-015-9341-0_3

    Chapter  Google Scholar 

  • Sangani MH, Amiri BJ, Shabani AA, Sakieh Y, Ashrafi S (2015) Modeling relationships between catchment attributes and river water quality in southern catchments of the Caspian Sea. Environ Sci Pollut Res 22:4985–5002

    Article  Google Scholar 

  • Seyb L, Randolph K (1977) North American project--A study of US water bodies

  • Singh SK et al (2004) Identification of human brain tumour initiating cells. Nature 432:396–401

    Article  Google Scholar 

  • Supharatid S (2003) Application of a neural network model in establishing a stage–discharge relationship for a tidal river. Hydrol Process 17:3085–3099

    Article  Google Scholar 

  • Tiwari M, Adamowski J (2014) Urban water demand forecasting and uncertainty assessment using ensemble wavelet-bootstrap-neural network models. Water Resour Res 49(10):6486–6507

    Article  Google Scholar 

  • Tong STY, Chen W (2002) Modeling the relationship between land use and surface water quality. J Environ Manag 66:377–393. doi:10.1006/jema.2002.0593

    Article  Google Scholar 

  • Usda S (1986) Urban hydrology for small watersheds. Tech Release 55:2–6

    Google Scholar 

  • Vazquez-Amabile G, Engel B (2005) Use of SWAT to compute groundwater table depth and streamflow in the Muscatatuck River watershed. Trans ASAE 48:991–1003

    Article  Google Scholar 

  • White H (1989) Learning in artificial neural networks: a statistical perspective. Neural Comput 1:425–464. doi:10.1162/neco.1989.1.4.425

    Article  Google Scholar 

  • Wilcock R (1986) Agricultural run-off: a source of water pollution in New Zealand? N Z Agric Sci 20:98–103

    Google Scholar 

  • Williams RA, Onsted CA, Bosch DD, Andrson WP (2001) Soil and water quality. Lewis pub, Boca Ration

    Google Scholar 

  • Yang X, Jin W (2010) GIS-based spatial regression and prediction of water quality in river networks: a case study in Iowa. J Environ Manag 91:1943–1951. doi:10.1016/j.jenvman.2010.04.011

    Article  Google Scholar 

  • Zhao Z, Zhang Y, Liao H (2008) Design of ensemble neural network using the Akaike information criterion. Eng Appl Artif Intell 21:1182–1188. doi:10.1016/j.engappai.2008.02.007

    Article  Google Scholar 

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Acknowledgments

The authors thank the WRMS, Watershed Management Organization and Geological Survey of Iran, for providing the initial datasets for this study. The authors are also thankful to Drs. S. Feiznia and F. Sarmadian for their assistance in lithological and soil type classifications. A special thanks to Mr. M.H. Sangani for providing related data for Mazandaran province in Iran, which was used in this study and also to the anonymous reviewers, whose comments and views helped improve this paper.

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Authors and Affiliations

  1. Department of Environmental Science, Faculty of Natural Resources, University of Tehran, Karaj, Iran

    Iman Fatehi, Bahman Jabbarian Amiri & Afshin Alizadeh

  2. Faculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue, QC, H9X 3 V9, Canada

    Jan Adamowski

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  1. Iman Fatehi
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  2. Bahman Jabbarian Amiri
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Correspondence to Bahman Jabbarian Amiri.

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Fatehi, I., Amiri, B.J., Alizadeh, A. et al. Modeling the Relationship between Catchment Attributes and In-stream Water Quality. Water Resour Manage 29, 5055–5072 (2015). https://doi.org/10.1007/s11269-015-1103-y

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