Rainfall Pattern Forecasting Using Novel Hybrid Intelligent Model Based ANFIS-FFA

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Zaher Mundher; Ghareb, Mazen Ismaeel; Ebtehaj, Isa; Bonakdari, Hossein; Siddique, Ridwan; Heddam, Salim; Yusif, Ali A.; Deo, Ravinesh C.

doi:10.1007/s11269-017-1797-0

Cited by 108 publications

(24 citation statements)

References 45 publications

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“…The conceptual model of the ensemble system for scenario 1 involving ANN, ANFIS and LSSVM single models is shown by Figure 4. The argumentation of using P(t−1) and P(t−12) as inputs for prediction of P(t) is supported by the following: a) As shown by some previous studies [3,6,27] in modeling precipitation, as a Markovian (auto-regression) process, P(t) is more correlated with precipitation values at prior time steps as P(t−1) and so on. For this reason, it is feasible to select previous time steps values as inputs for the AI models.…”

Section: First Scenariomentioning

confidence: 86%

“…Some previous investigations indicated that ANFIS can be used as an efficient tool for precipitation modeling (e.g., see, [23][24][25][26]). Yaseen et al [27] employed a hybrid ANFIS-FFA model to forecast one month ahead precipitation value and compared the results with the classic ANFIS model. The results showed that, the proposed ANFIS-FFA method could perform more accurate than the classic ANFIS method, so that the efficiency of the ANFIS-FFA and ANFIS methods were strongly governed by size of the inputs set.…”

Section: Introductionmentioning

confidence: 99%

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Artificial Intelligence Based Ensemble Modeling for Multi-Station Prediction of Precipitation

et al. 2019

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The aim of ensemble precipitation prediction in this paper was to achieve the best performance via artificial intelligence (AI) based modeling. In this way, ensemble AI based modeling was proposed for prediction of monthly precipitation with three different AI models (feed forward neural network-FFNN, adaptive neural fuzzy inference system-ANFIS and least square support vector machine-LSSVM) for the seven stations located in the Turkish Republic of Northern Cyprus (TRNC). Two scenarios were examined each having specific inputs set. The scenario 1 was developed for predicting each station’s precipitation through its own data at previous time steps while in scenario 2, the central station’s data were imposed into the models, in addition to each station’s data, as exogenous input. Afterwards, the ensemble modeling was generated to improve the performance of the precipitation predictions. To end this aim, two linear and one non-linear ensemble techniques were used and then the obtained outcomes were compared. In terms of efficiency measures, the averaging methods employing scenario 2 and non-linear ensemble method revealed higher prediction efficiency. Also, in terms of Skill score, non-linear neural ensemble method could enhance predicting efficiency up to 44% in the verification step.

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Section: First Scenariomentioning

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Based Ensemble Modeling for Multi-Station Prediction of Precipitation

et al. 2019

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“…where O i : observed DO, O m : average of observed DO, X i : predicated value of DO, X m : average predicated value of DO, N: number of datapoints, MAE: mean absolute error, RMSE: root-mean-square error, and R: determination of coefficient [76,77]. Table 3 presents the correlation coefficient between DO and other water quality parameters.…”

Section: Case Studymentioning

confidence: 99%

The Integration of Nature-Inspired Algorithms with Least Square Support Vector Regression Models: Application to Modeling River Dissolved Oxygen Concentration

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Ehteram

Sharafati

et al. 2018

Water

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Abstract:The current study investigates an improved version of Least Square Support Vector Machines integrated with a Bat Algorithm (LSSVM-BA) for modeling the dissolved oxygen (DO) concentration in rivers. The LSSVM-BA model results are compared with those obtained using M5 Tree and Multivariate Adaptive Regression Spline (MARS) models to show the efficacy of this novel integrated model. The river water quality data at three monitoring stations located in the USA are considered for the simulation of DO concentration. Eight input combinations of four water quality parameters, namely, water temperature, discharge, pH, and specific conductance, are used to simulate the DO concentration. The results revealed the superiority of the LSSVM-BA model over the M5 Tree and MARS models in the prediction of river DO. The accuracy of the LSSVM-BA model compared with those of the M5 Tree and MARS models is found to increase by 20% and 42%, respectively, in terms of the root-mean-square error. All the predictive models are found to perform best when all the four water quality variables are used as input, which indicates that it is possible to supply more information to the predictive model by way of incorporation of all the water quality variables.

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“…A robust alternative tool for PAR estimation is the Adaptive Neuro-Fuzzy Inference System (ANFIS). This combines fuzzy set and ANN theory, offering advantages over a standalone ANN (Ali, Deo, Downs, & Maraseni, 2018;Yaseen et al, 2018). First utilized by Jang (1995), ANFIS consists of a hybrid learning system that processes data features via a set of rules and membership functions (MF).…”

Section: Background Reviewmentioning

confidence: 99%

Adaptive Neuro‐Fuzzy Inference System integrated with solar zenith angle for forecasting sub‐tropical Photosynthetically Active Radiation

Deo

Downs

Adamowski

et al. 2018

Food and Energy Security

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Advocacy for climate mitigation aims to minimize the use of fossil fuel and to support clean energy adaptation. While alternative energies (e.g., biofuels) extracted from feedstock (e.g., micro‐algae) represent a promising role, their production requires reliably modeled photosynthetically active radiation (PAR). PAR models predict energy parameters (e.g., algal carbon fixation) to aid in decision‐making at PAR sites. Here, we model very short‐term (5‐min scale), sub‐tropical region's PAR with an Adaptive Neuro‐Fuzzy Inference System model with a Centroid‐Mean (ANFIS‐CM) trained with a non‐climate input (i.e., only the solar angle, θZ). Accuracy is benchmarked against genetic programming (GP), M5Tree, Random Forest (RF), and multiple linear regression (MLR). ANFIS‐CM integrates fuzzy and neural network algorithms, whereas GP adopts an evolutionary approach, M5Tree employs binary decision, RF employs a bootstrapped ensemble, and MLR uses statistical tools to link PAR with θZ. To design the ANFIS‐CM model, 5‐min θZ (01–31 December 2012; 0500H–1900H) for sub‐tropical, Toowoomba are utilized to extract predictive features, and the testing accuracy (i.e., differences between measurements and forecasts) is evaluated with correlation (r), root‐mean‐square error (RMSE), mean absolute error (MAE), Willmott (WI), Nash–Sutcliffe (ENS), and Legates & McCabes (ELM) Index. ANFIS‐CM and GP are equivalent for 5‐min forecasts, yielding the lowest RMSE (233.45 and 233.01μ mol m−2s−1) and MAE (186.59 and 186.23 μmol m−2s−1). In contrast, MLR, M5Tree, and RF yields higher RMSE and MAE [(RMSE = 322.25 μmol m−2s−1, MAE = 275.32 μmol m−2s−1), (RMSE = 287.70 μmol m−2s−1, MAE = 234.78 μmol m−2s−1), and (RMSE = 359.91 μmol m−2s−1, MAE = 324.52 μmol m−2s−1)]. Based on normalized error, ANFIS‐CM is considerably superior (MAE = 17.18% versus 19.78%, 34.37%, 26.39%, and 30.60% for GP, MLR, M5Tree, and RF models, respectively). For hourly forecasts, ANFIS‐CM outperforms all other methods (WI = 0.964 vs. 0.942, 0.955, 0.933 & 0.893, and ELM = 0.741 versus 0.701, 0.728, 0.619 & 0.490 for GP, MLR, M5Tree, and RF, respectively). Descriptive errors support the versatile predictive skills of the ANFIS‐CM model and its role in real‐time prediction of the photosynthetic‐active energy to explore biofuel generation from micro‐algae, studying food chains, and supporting agricultural precision.

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Rainfall Pattern Forecasting Using Novel Hybrid Intelligent Model Based ANFIS-FFA

Cited by 108 publications

References 45 publications

Artificial Intelligence Based Ensemble Modeling for Multi-Station Prediction of Precipitation

Artificial Intelligence Based Ensemble Modeling for Multi-Station Prediction of Precipitation

The Integration of Nature-Inspired Algorithms with Least Square Support Vector Regression Models: Application to Modeling River Dissolved Oxygen Concentration

Adaptive Neuro‐Fuzzy Inference System integrated with solar zenith angle for forecasting sub‐tropical Photosynthetically Active Radiation

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