A comparative study of fuzzy logic-based models for groundwater quality evaluation based on irrigation indices

• Autorzy: Vadiati Meysam , Nalley Deasy , Adamowski Jan , Nakhaei Mohammad , Asghari-Moghaddam Asghar

Identyfikatory

DOI

10.2478/jwld-2019-0074

Warianty tytułu

PL

Analiza porównawcza modeli opartych na logice rozmytej do oceny jakości wód podziemnych na podstawie wskaźników nawadniania

• Języki publikacji: EN

Abstrakty

EN

Groundwater quality modelling plays an important role in water resources management decision making processes. Accordingly, models must be developed to account for the inherent uncertainty that arises from the sample measurement stage through to the data interpretation stages. Artificial intelligence models, particularly fuzzy inference systems (FIS), have been shown to be effective in groundwater quality evaluation for complex aquifers. Applying fuzzy set theory to groundwater-quality related decision-making in an agricultural production context, the Mamdani, Sugeno, and Larsen fuzzy logic-based models (MFL, SFL, and LFL, respectively) were used to develop a series of new, generalized, rule-based fuzzy models for water quality evaluation using widely accepted irrigation indices. Rather than drawing upon physiochemical groundwater quality parameters, the present study employed widely accepted agricultural indices (e.g., irrigation criteria) when developing the MFL, SFL and LFL groundwater quality models. These newly-developed models, generated significantly more consistent results than the United States Soil Laboratory (USSL) diagram, addressed the inherent uncertainty in threshold data, and were effective in assessing groundwater quality for agricultural uses. The SFL model is recommended because it had the best performance in terms of accuracy when assessing groundwater quality using irrigation indices.

PL

Modelowanie jakości wód podziemnych odgrywa ważną rolę w procesach podejmowania decyzji dotyczących zarządzania zasobami wodnymi. W związku z tym należy opracować modele uwzględniające naturalną niepewność, która pojawia się od etapu pomiaru próbki, aż do interpretacji danych. Wykazano, że modele sztucznej inteligencji, w szczególności systemy wnioskowania rozmytego (FIS), są skuteczne w ocenie jakości wód podziemnych w odniesieniu do złożonych warstw wodonośnych. Zastosowanie teorii zbiorów rozmytych do podejmowania decyzji związanych z jakością wód podziemnych w kontekście produkcji rolnej, modele oparte na logice rozmytej Mamdaniego, Sugeno i Larsena (odpowiednio MFL, SFL i LFL) zostały wykorzystane do opracowania serii nowych, uogólnionych modeli, opartych na regułach rozmytych, do oceny jakości wody z wykorzystaniem powszechnie akceptowanych wskaźników nawadniania. Zamiast czerpać z jakościowych parametrów fizykochemicznych wód gruntowych, w niniejszym badaniu zastosowano powszechnie przyjęte wskaźniki rolne (np. kryteria nawadniania) podczas opracowywania modeli jakości wód podziemnych MFL, SFL i LFL. Za pomocą tych nowo opracowanych modeli, wygenerowano znacznie bardziej spójne wyniki niż z zastosowaniem diagramu Amerykańskiego Laboratorium Gleby (USSL), uwzględniono nieodłączną niepewność danych progowych. Modele te były skuteczne w ocenie jakości wód podziemnych do zastosowań rolniczych. Model SFL jest zalecany, ponieważ miał najlepszą efektywność pod względem dokładności w ocenie jakości wód podziemnych z użyciem wskaźników nawadniania.

Słowa kluczowe

EN

fuzzy inference model; fuzzy rules; irrigation indices; Mamdani model; Sarab Plain

PL

model Mamdaniego; model wnioskowania rozmytego; reguły rozmyte; równanie Saraba; wskaźniki irygacyjne

• Wydawca: Wydawnictwo ITP
• Czasopismo: Journal of Water and Land Development
• Rocznik: 2019
• Tom: no. 43
• Strony: 158--170
• Opis fizyczny: Bibliogr. 50 poz., rys., tab.

Twórcy

autor

Vadiati Meysam

• Kharazmi University, Department of Applied Geology, Faculty of Earth Sciences, Tehran, Iran; McGill University, Department of Bioresource Engineering, Faculty of Agricultural and Environmental Sciences, Quebec, Canada

autor

Nalley Deasy

• McGill University, Department of Bioresource Engineering, Faculty of Agricultural and Environmental Sciences, Quebec, Canada

autor

Adamowski Jan

• McGill University, Department of Bioresource Engineering, Faculty of Agricultural and Environmental Sciences, Quebec, Canada

autor

Nakhaei Mohammad

• Kharazmi University, Department of Applied Geology, Faculty of Earth Sciences, Tehran, Iran

autor

Asghari-Moghaddam Asghar

• University of Tabriz, Department of Earth Sciences, Faculty of Natural Science, Tabriz, Iran

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Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).