A survey of water level fluctuation predicting in Urmia Lake using support vector machine with firefly algorithm

Kisi, Özgür; Shiri, Jalal; Karimi, Shahram; Shamshirband, Shahaboddin; Motamedi, Shervin; Petković, Dalibor; Hashim, Roslan

doi:10.1016/j.amc.2015.08.085

Cited by 84 publications

(37 citation statements)

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“…The soft computing technique is one of the most relevant and modern techniques used in the civil engineering problems (Sihag et al 2018b, c;Nain et al 2018bKisi et al 2017;Parsaie et al 2017c;Kisi et al 2015;Parsaie and Haghiabi 2014;Shiri and Kisi 2012). In this investigation, GP, SVM and M5P tree models were used.…”

Section: Soft Computing Techniquesmentioning

confidence: 99%

Modelling of the impact of water quality on the infiltration rate of the soil

2019

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The concept behind of this paper is to check the potential of the three regression-based techniques, i.e. M5P tree, support vector machine (SVM) and Gaussian process (GP), to estimate the infiltration rate of the soil and to compare with two empirical models, i.e. Kostiakov model and multi-linear regression (MLR). Totally, 132 observations were obtained from the laboratory experiments, out of which 92 observations were used for training and residual 40 for testing the models. A double-ring infiltrometer was used for experimentation with different concentrations (1%, 5%, 10% and 15%) of impurities and different types of water quality (ash and organic manure). Cumulative time (T f ), type of impurities (I t ), concentration of impurities (C i ) and moisture content (W c ) were the input variables, whereas infiltration rate was considered as target. For SVM and GP regression, two kernel functions (radial based kernel and Pearson VII kernel function) were used. The results from this investigation suggest that M5P tree technique is more precise as compared to the GP, SVR, MLR approach and Kostiakov model. Among GP, SVR, MLR approach and Kostiakov model, MLR is more accurate for estimating the infiltration rate of the soil. Thus, M5P tree is a technique which could be used for modelling the infiltration rate for the given data set. Sensitivity analyses suggest that the cumulative time (T f ) is the major influencing parameter on which infiltration rate of the soil depends.

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Section: Soft Computing Techniquesmentioning

confidence: 99%

Modelling of the impact of water quality on the infiltration rate of the soil

2019

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“…Among different kernel functions of the SVM, the RBF has shown superiority over the others, as reported previously (e.g. Rajasekaran et al, 2008;Yang, 2009;Kisi et al, 2015). It works with the solution of a set of linear equations in its training phase (Shamshirband et al, 2014).…”

Section: Support Vector Machines (Svm and Svm-fa)mentioning

confidence: 99%

Comprehensive assessment of 12 soft computing approaches for modelling reference evapotranspiration in humid locations

Shiri

Zounemat‐Kermani

Kisi

et al. 2019

Meteorological Applications

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Owing to the lack of lysimetric data in many regions, the standard Penman–Monteith equation‐adopted by Food and Agriculture Organization of the United Nations (FAO56‐PM model) is usually used to calculate the reference evapotranspiration (ETo). However, as this model needs many meteorological parameters that cannot be easily obtained in many regions, other simple models along with the soft computing models are used to obtain ETo values. The paper presents a comprehensive comparison of 12 soft computing models: gene expression programming (GEP), neuro‐fuzzy with grid partitioning (NF‐GP), neuro‐fuzzy with sub‐clustering (NF‐SC), multivariate adaptive regression spline (MARS), boosted regression tree (BT), random forest (RF), model tree (MT), support vector machine (SVM), SVM‐firefly algorithm (SVM‐FA), extreme learning machine (ELM), neural network‐particle swarm optimization (NN‐PSO) and neural network‐differential evolution (NN‐DE), to estimate daily ETo values in humid regions. Therefore, daily meteorological data from two weather stations (during a 12 year period) were used to assess the models. The obtained results revealed that very good efficiency was obtained from all the applied methods. The temperature‐based SVM‐FA (root mean square error (RMSE) = 0.324 mm, Nash–Sutcliffe co‐efficient (NS) = 0.960) and NF‐GP (RMSE = 0.272 mm, NS = 0.974) models generally provided the best accuracy when estimating the ETo of Sari and Bablosar, two humid stations in northern Iran, respectively. The accuracy ranks of the other models (from best to worst) were found to be: NN‐PSO, NF‐SC, ELM, NN‐DE, MARS, GEP, RF, SVM, BT and MT. Among the radiation‐based models, the NF‐GP provided the best accuracy at estimating the ETo of both stations. The other models were ranked as: ELM, SVM‐FA, NN‐DE, NN‐PSO, MARS, RF, BT, NF‐SC, SVM and MT, respectively.

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“…For example, principal component analysis (PCA) has been used to determine the main variables that affect eutrophication processes from a wide number of water quality parameters including TN, TP, oxygen, Chl-a, Secchi depth, phosphate, nitrate, nitrite, and ammonia (Lundberg et al, 2005;Primpas et al, 2010); Cluster analysis (CA) has been used to classify waters into the three eutrophication statuses including the oligotrophic, mesotrophic, and eutrophic state using several variables (Chl-a, phosphate, nitrate, nitrite, and ammonia) (Stefanou et al, 2000;Primpas et al, 2008); Discriminant factor analysis (DFA) has been used to identify different variables (nitrate, phosphate, Chl-a, DO, turbidity and temperature) that can differentiate sampling sites and to group them according to their eutrophication conditions (Tsirtsis and Karydis, 1999;Pinto et al, 2012); Artificial neural network (ANN) mode has been used for prediction of eutrophication conditions with reasonable accuracy by a wide range of variables (TP, TN, COD, the Secchi disk depth, DO and Chl-a) (Jiang et al, 2006;Kuo et al, 2007). Support vector machine (SVM) (Vapnik, 1995) is a promising power approach used to reflect the nonlinearity between responsive indicator and input variables using stochastic error minimization approaches (Zhou et al, 2016a) and is an effective tool to predict values from a wide variety of environmental fields (Ribeiro and Torgo, 2008;Farfani et al, 2015;Kisi et al, 2015). The grid search (GS) algorithm is straight forward to determine the optimized parameter values for the SVM (Sajan et al, 2015;Gao and Hou, 2016).…”

Section: Introductionmentioning

confidence: 99%