A survey on spatial and temporal variations of Agricultural water quality in Gorganrood River using fuzzy rules

Document Type : Original Article

Authors

1 Department of Range and Watershed management, Faculty of Natural Resources, Gonbad Kavous University, Gonbad Kavous, Iran

2 Department Plant Production, Gonbadkavos college of Agricultural Sciences and Natural Resources, Gonbadkavos, Iran

Abstract
Objective:in this research, the development of a fuzzy model is proposed to enhance the Wilcox classification system.

Methods: The data recorded by the regional water organization of Golestan province in the years 1961 to 2014 at eight stations of the Gorganrood River were used for two inputs (SAR and EC), one output (water quality class) and 16 fuzzy rules developed in this research.

Results: The obtained results showed that the proposed developed model has a good compatibility with the Wilcox model and it is good in determining the agricultural water quality classes. It is expected that the possibility of predicting the risk of river water for use in the agricultural sector based on the proposed system will enable users to have a proper explanation for the changes in water quality during the study period. In addition, you can see temporal and spatial changes in water quality classes ahead of time and early forecast.

Conclusions: The proposed system is capable of classifying water quality into 16 classes and assigns a grade to each class, which is created in a fuzzy form between the interval [0,1]. While in Wilcox's model, he classifies them in only one class. In other words, the 16 different water quality classes of the Wilcox model have overlapping boundaries in the real world, which is clearly shown by the proposed fuzzy system.

Keywords


Aghaee M., Heshmatoor  A., Seyedind S. M. 2020. Investigation of Water Quality of Chehelchay River Using IRWQIsc Index. .(In Persian).
Alavi N., Nozari V., Mazloumzadeh S. M., Nezamabadi-pour  H. 2010. Irrigation water quality evaluation using adaptive network-based fuzzy inference system. Paddy and Water Environment, 8, 259-266.
Alizadeh M. R., Nikoo M. R., Rakhshandehroo, G. R. 2017. Hydro-environmental management of groundwater resources: A fuzzy-based multi-objective compromise approach. Journal of Hydrology, 551. https://doi.org/10.1016/j.jhydrol.2017.06.011
Araghinejad S. 2013. Data-driven modeling: Using MATLAB®in water resources and environmental engineering. Springer Science & Business Media.
Ahmadi S H, Heshmatpour A, Seydian S M, Komaki C B. 2024. Locating and prioritizing the suitable place to build a pond with analytic hierarchy process (Northern region of Gorgan). Journal of Rainwater Catchment Systems ; 12 (1) : 7
Dhaoui O., Agoubi B., Antunes I. M., Tlig L., Kharroubi A. 2023. Groundwater quality for irrigation in an arid region—Application of fuzzy logic techniques. Environmental Science and Pollution Research, 30(11), 29773–29789. https://doi.org/10.1007/s11356-022-24334-5
Ghaziani S., Heshmatpour A., Ferasati  M., Rostami F. 2020. Quality Assessment of Gorganrood River Using NSFWQI Index in Gonbad kavus Urban Area. Iranian journal of Ecohydrology, 7(2), 373-382. (In Persian).
Hashemi S.E, Mousavi S,F., Taheri S.M., Qarachahi. A., 2011. Evaluation of underground water quality in 9 cities of Isfahan province for drinking purposes using fuzzy inference system. Iran Water Resources Research, 6(3), 25-34.
Hekmatzadeh A., Asi P. 2013. Water and Wastewater: Groundwater Quality Assessment Using Fuzzy Inference System - A Case Study of Zarghan Plain. National Congress of Civil Engineering.(In Persian)
Heshmatpour  A., Jandaghi  N.,  Ghareh Mahmoodlu  M., Pasand S. 2020. Drought effects on surface water quality in Golestan province for Irrigation Purposes, Case study: Gorganroud River. Physical Geography Quarterly, 13(48), 75-88. .(In Persian)
Heshmatpour  A., Mohammadian, Y. 2024. Selecting the best location for constructing a reservoir to supply agricultural water using a combination of Boolean and fuzzy logic (case study: Qoyijq watershed), Water Management in Agricultur,( Accepted for publication).
Heshmatpour  A., Pasand  S., Sabouri H., Rostami F. 2019. Evaluation of water pollution from rice cultivation using Nitrogen fertilizer in North of Iran. Environmental Resources Research, 7(1), 20-28.
Li  B., Yang G., Wan R.,  Hörmann G. 2017. Dynamic water quality evaluation based on fuzzy matter–element model and functional data analysis, a case study in Poyang Lake. Environmental Science and Pollution Research, 24(23). https://doi.org/10.1007/s11356-017-9371-0
Liu D., Zou Z. 2012. Water quality evaluation based on improved fuzzy matter-element method. Journal of Environmental Sciences, 24(7), 1210–1216. https://doi.org/10.1016/S1001-0742(11)60938-8
LV  W. 1948. The Quality of Water for Irrigation Use. Technical Bulletin, 962(962), 40.
Mia  M. Y., Islam A. R. M. T., Jannat  J. N., Jion  M. M. M. F., Sarker A., Tokatli  C., Siddique  M. A. B., Ibrahim S. M.,  Senapathi V. 2023. Identifying factors affecting irrigation metrics in the Haor basin using integrated Shannon’s entropy, fuzzy logic and automatic linear model. Environmental Research, 226, 115688.
Thabrez  M., Parimalarenganayaki S. 2023. A Fuzzy Synthetic Evaluation based groundwater quality classification for the Irrigation purpose: A case study from Tumkur district, Karnataka, India. https://www.researchsquare.com/article/rs-2964486/latest
Zadeh L. A. 1996. Fuzzy sets. In Fuzzy Sets, Fuzzy Logic, And Fuzzy Systems: Selected Papers by Lotfi A Zadeh (pp. 394–432). World Scientific.