Comparison of Different Data-Driven Approaches for Modeling Lake Level Fluctuations: The Case of Manyas and Tuz Lakes (Turkey)

Hadi, Syamsul; Kisi, Özgür; Kiafar, Hamed; Ghavidel, Sarvin Zaman Zad

doi:10.1007/s11269-014-0894-6

Cited by 26 publications

(20 citation statements)

References 30 publications

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“…Datadriven modeling (DDM) is based on analyzing the data characterizing the system under study; in particular, a model can be defined on the basis of finding connections between the system state variables (input, internal and output variables) without explicit knowledge of the physical behavior [13]. DDM includes different categories generally divided into statistical and artificial-intelligent models which include neural networks, fuzzy systems and evolutionary computing as well as other areas within artificial intelligence and machine learning [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Data-driven modeling for water quality prediction case study: The drains system associated with Manzala Lake, Egypt

Khadr

Elshemy

2017

Ain Shams Engineering Journal

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Manzala Lake, the largest of the Egyptian lakes, is affected qualitatively and quantitatively by drainage water that flows into the lake. This study investigated the capabilities of adaptive neuro-fuzzy inference system (ANFIS) to predict water quality parameters of drains associated with Manzala Lake, with emphasis on total phosphorus and total nitrogen. A combination of data sets was considered as input data for ANFIS models, including discharge, pH, total suspended solids, electrical conductivity, total dissolved solids, water temperature, dissolved oxygen and turbidity. The models were calibrated and validated against the measured data for the period from year 2001 to 2010. The performance of the models was measured using various prediction skill criteria. Results show that ANFIS models are capable of simulating the water quality parameters and provided reliable prediction of total phosphorus and total nitrogen, thus suggesting the suitability of the proposed model as a tool for onsite water quality evaluation. Ó 2016 Ain Shams University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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Section: Introductionmentioning

confidence: 99%

Data-driven modeling for water quality prediction case study: The drains system associated with Manzala Lake, Egypt

Khadr

Elshemy

2017

Ain Shams Engineering Journal

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“…An appropriate structure has to be established in order to begin training of the fuzzy inference system (FIS). In this study, the FIS structure was generated by means of subtractive clustering [10,27], which is used to determine regions in the feature space with high densities of data points. The point with the maximum number of neighbours is selected as the cluster centre.…”

Section: Resultsmentioning

confidence: 99%

“…The final approximate function then becomes (10) For linear SVR, the kernel is , while different kernel functions, e.g. polynomial, sigmoid or RBF, can be used in nonlinear case.…”

Section: Support Vector Machines For Regressionmentioning

confidence: 99%

“…Hence, it can potentially benefit from both the ANN and fuzzy logic within a single framework. Sanikhani et al (2015) modelled two different ANFIS models -the ANFIS with grid partition (ANFIS-GP) and the ANFIS with subtractive clustering (ANFIS-SC) with gene expression programming (GEP) -in order to forecast one, two, and three months in advance the lake level fluctuations at Manyas and Tuz lakes in Turkey [10]. Rezaeianzadeh et al (2014) applied the ANN, ANFIS and regression models for forecasting the maximum daily flow at the outlet of the Khosrow Shirin watershed, located at the Fars Province in Iran [11].…”

Section: Introductionmentioning

confidence: 99%

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Application of artificial neural networks for hydrological modelling in Karst

Lakušić¹

2018

JCE

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Application of artificial neural networks for hydrological modelling in karstThe possibility of short-term water flow forecasting in a karst region is presented in this paper. Four state-of-the-art machine learning algorithms are used for the one day ahead forecasting: multi-layer perceptron neural network, radial basis function neural network, support vector machines for regression (SVR), and adaptive neuro fuzzy inference system (ANFIS). The results show that the ANFIS model outperforms other algorithms when the root mean square error and mean absolute error are used as quality indicators. Primjena neuronskih mreža za hidrološko modeliranje u krškom području U ovom radu je razmatrana mogućnost kratkoročnog predviđanja protoka vode u krškom području. Četiri suvremena algoritma strojnog učenja su korištena za predviđanje protoka jedan dan unaprijed i to: neuronska mreža s višeslojnim perceptronom, neuronska mreža s radijalnom aktivacijskom funkcijom, metoda potpornih vektora za regresiju (SVR), adaptivni neuroneizraziti (fuzzy) sustav zaključivanja (ANFIS). Rezultati pokazuju da ANFIS model nadmašuje ostale algoritme kada se korijen srednje kvadratne popogreške i srednja apsolutna pogreška upotrijebe kao mjere kvalitete.

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“…Previous studies have demonstrated that artificial intelligence (e.g. ANNs, ANFISs) may provide results with good accuracy in hydrology and water resources engineering (Martí and Gasque, 2010;Kişi, 2011a, 2011b;Kim et al, 2012;Landeras et al, 2012;Sanikhani et al, 2015). By assessing the performance of six commonly used ET 0 estimation methods with different data requirements in a semi-arid highland environment in Turkey, it was concluded that the FAO56-PM and Hargreaves (H) equations were the most appropriate to estimate ET 0 in a semi-arid region (Benli et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of several soft computing methods in monthly evapotranspiration modelling

Gavili

Hadi

Kişi

et al. 2017

Meteorological Applications

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Evapotranspiration assessment is one of the most substantial issues in hydrology. The methods used in modelling reference evapotranspiration (ET 0 ) consist of empirical equations or complex methods based on physical processes. In arid and semi-arid climates, determining the amount of evapotranspiration has a major role in the design of irrigation systems, irrigation network management, planning and management of water resources and water management issues in the agricultural sector. This paper presents a case study of five meteorological stations located in Kurdistan province in the west of Iran. The ability of three different soft computing methods, an artificial neural network (ANN), an adaptive neuro-fuzzy inference system (ANFIS) and gene expression programming (GEP), were compared for modelling ET 0 in this study. The FAO56 Penman−Monteith model was considered as a reference model and soft computing models were compared using the Priestley−Taylor, Hargreaves, Hargreaves−Samani, Makkink and Makkink−Hansen empirical methods, with respect to the determination co-efficient, the root mean square error, the mean absolute error and the Nash-Sutcliffe model efficiency co-efficient. Soft computing models were superior to the empirical methods in modelling ET 0 . Among the soft computing methods, the ANN was found to be better than the ANFIS and GEP.

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Comparison of Different Data-Driven Approaches for Modeling Lake Level Fluctuations: The Case of Manyas and Tuz Lakes (Turkey)

Cited by 26 publications

References 30 publications

Data-driven modeling for water quality prediction case study: The drains system associated with Manzala Lake, Egypt

Data-driven modeling for water quality prediction case study: The drains system associated with Manzala Lake, Egypt

Application of artificial neural networks for hydrological modelling in Karst

Evaluation of several soft computing methods in monthly evapotranspiration modelling

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