Implementation of a hybrid MLP-FFA model for water level prediction of Lake Egirdir, Turkey

Ghorbani, Mohammad Ali; Deo, Ravinesh C.; Karimi, Vahid; Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Zaher Mundher; Terzi, Özlem

doi:10.1007/s00477-017-1474-0

Cited by 107 publications

(48 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance, a combination between the adaptive neurofuzzy system (ANFIS) model with the firefly algorithm (FFA) was conducted for modeling the roller length of the hydraulic jump [46]. An integration of the classical multilayer perceptron with FFA for pan evaporation process is given in [47]. River flow forecasting for tropical environment was established using the ANFIS-FFA model [48].…”

Section: Complexitymentioning

confidence: 99%

Hybridized Extreme Learning Machine Model with Salp Swarm Algorithm: A Novel Predictive Model for Hydrological Application

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Faris

Al‐Ansari

2020

Complexity

Self Cite

View full text Add to dashboard Cite

e capability of the extreme learning machine (ELM) model in modeling stochastic, nonlinear, and complex hydrological engineering problems has been proven remarkably. e classical ELM training algorithm is based on a nontuned and random procedure that might not be efficient in convergence of excellent performance or possible entrapment in the local minima problem. is current study investigates the integration of a newly explored metaheuristic algorithm (i.e., Salp Swarm Algorithm (SSA)) with the ELM model to forecast monthly river flow. Twenty years of river flow data time series of the Tigris river at the Baghdad station, Iraq, is used as a case study. Different input combinations are applied for constructing the predictive models based on antecedent values. e results are evaluated based on several statistical measures and graphical presentations. e river flow forecast accuracy of SSA-ELM outperformed the classical ELM and other artificial intelligence (AI) models. Over the testing phase, the proposed SSA-ELM model yielded a satisfactory enhancement in the level accuracies (8.4 and 13.1 percentage of augmentation for RMSE and MAE, respectively) against the classical ELM model. In summary, the study ascertains that the SSA-ELM model is a qualified data-intelligent model for monthly river flow prediction at the Tigris river, Iraq.

show abstract

Section: Complexitymentioning

confidence: 99%

Hybridized Extreme Learning Machine Model with Salp Swarm Algorithm: A Novel Predictive Model for Hydrological Application

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Faris

Al‐Ansari

2020

Complexity

Self Cite

View full text Add to dashboard Cite

show abstract

“…Several versions of ML models have been developed for evaporation modeling, including evolutionary computing, classical neural networks, kernel models, fuzzy logic, decision trees, deep learning, complementary wavelet-machine learning, and hybrid machine learning, among others (Danandeh Mehr et al, 2018;Fahimi, Yaseen, & El-shafie, 2016;Jing et al, 2019;Yaseen, Sulaiman, Deo, & Chau, 2019). The performance of these models and their hybrid combinations has been impressive in terms of prediction accuracy (Ghorbani, Deo, Karimi, Yaseen, & Terzi, 2017;Yaseen et al, 2018). However, most of these studies primarily focus on investigating the generalized capabilities of ML models in different climates, owing to the fact that each climate has its own characteristics of stochasticity and non-stationarity.…”

Section: Introductionmentioning

confidence: 99%

Prediction of evaporation in arid and semi-arid regions: a comparative study using different machine learning models

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Al-Juboori

Beyaztaş

et al. 2019

Engineering Applications of Computational Fluid Mechanics

Self Cite

View full text Add to dashboard Cite

Evaporation, one of the fundamental components of the hydrology cycle, is differently influenced by various meteorological variables in different climatic regions. The accurate prediction of evaporation is essential for multiple water resources engineering applications, particularly in developing countries like Iraq where the meteorological stations are not sustained and operated appropriately for in situ estimations. This is where advanced methodologies such as machine learning (ML) models can make valuable contributions. In this research, evaporation is predicted at two different meteorological stations located in arid and semi-arid regions of Iraq. Four different ML models for the prediction of evaporation-the classification and regression tree (CART), the cascade correlation neural network (CCNNs), gene expression programming (GEP), and the support vector machine (SVM)were developed and constructed using various input combinations of meteorological variables. The results reveal that the best predictions are achieved by incorporating sunshine hours, wind speed, relative humidity, rainfall, and the minimum, mean, and maximum temperatures. The SVM was found to show the best performance with wind speed, rainfall, and relative humidity as inputs at Station I (R 2 = .92), and with all variables as inputs at Station II (R 2 = .97). All the ML models performed well in predicting evaporation at the investigated locations.

show abstract

“…Figure 2 demonstrates a schematic perspective of the process of obtaining the optimal hidden layer weights of MLP with the use of the firefly algorithm for the estimation of daily dew point temperature. The hybrid MLP-FFA algorithm has been successfully implemented to model pan evaporation [41], water quality parameters-the BOD and DO of Langat river [49], lake water level prediction [50], and wind speed prediction [51].…”

Section: Hybridized Mlp-ffa Modelsmentioning

confidence: 99%

Dew Point Temperature Estimation: Application of Artificial Intelligence Model Integrated with Nature-Inspired Optimization Algorithms

Naganna

Deka

Ghorbani

et al. 2019

Water

View full text Add to dashboard Cite

Dew point temperature (DPT) is known to fluctuate in space and time regardless of the climatic zone considered. The accurate estimation of the DPT is highly significant for various applications of hydro and agro–climatological researches. The current research investigated the hybridization of a multilayer perceptron (MLP) neural network with nature-inspired optimization algorithms (i.e., gravitational search (GSA) and firefly (FFA)) to model the DPT of two climatically contrasted (humid and semi-arid) regions in India. Daily time scale measured weather information, such as wet bulb temperature (WBT), vapor pressure (VP), relative humidity (RH), and dew point temperature, was used to build the proposed predictive models. The efficiencies of the proposed hybrid MLP networks (MLP–FFA and MLP–GSA) were authenticated against standard MLP tuned by a Levenberg–Marquardt back-propagation algorithm, extreme learning machine (ELM), and support vector machine (SVM) models. Statistical evaluation metrics such as Nash Sutcliffe efficiency (NSE), root mean square error (RMSE), and mean absolute error (MAE) were used to validate the model efficiency. The proposed hybrid MLP models exhibited excellent estimation accuracy. The hybridization of MLP with nature-inspired optimization algorithms boosted the estimation accuracy that is clearly owing to the tuning robustness. In general, the applied methodology showed very convincing results for both inspected climate zones.

show abstract

Implementation of a hybrid MLP-FFA model for water level prediction of Lake Egirdir, Turkey

Cited by 107 publications

References 65 publications

Hybridized Extreme Learning Machine Model with Salp Swarm Algorithm: A Novel Predictive Model for Hydrological Application

Hybridized Extreme Learning Machine Model with Salp Swarm Algorithm: A Novel Predictive Model for Hydrological Application

Prediction of evaporation in arid and semi-arid regions: a comparative study using different machine learning models

Dew Point Temperature Estimation: Application of Artificial Intelligence Model Integrated with Nature-Inspired Optimization Algorithms

Contact Info

Product

Resources

About