River flow simulation using a multilayer perceptron-firefly algorithm model

Darbandi, S; Pourhosseini, Fatemeh Akhoni

doi:10.1007/s13201-018-0713-y

Cited by 20 publications

(7 citation statements)

References 20 publications

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“…The research reveals that the ANN is an authentic way to detect the flood danger in the Nile. Darbandi and Pourhosseini (2018) worked on a Hybrid multi-layer perceptron to estimate monthly river flow, and multilayer perceptron ANN verified the subsequent results. A hybrid multilayer perceptron gives satisfactory results for the water flow forecast.…”

Section: Introductionmentioning

confidence: 73%

Artificial Intelligence Based Flood Forecasting for River Hunza at Danyor Station in Pakistan

Yaseen

Awais

Riaz

et al. 2022

Archives of Hydro-Engineering and Environmental Mechanics

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Floods can cause significant problems for humans and can damage the economy. Implementing a reliable flood monitoring warning system in risk areas can help to reduce the negative impacts of these natural disasters. Artificial intelligence algorithms and statistical approaches are employed by researchers to enhance flood forecasting. In this study, a dataset was created using unique features measured by sensors along the Hunza River in Pakistan over the past 31 years. The dataset was used for classification and regression problems. Two types of machine learning algorithms were tested for classification: classical algorithms (Random Forest, RF and Support Vector Classifier, SVC) and deep learning algorithms (Multi-Layer Perceptron, MLP). For the regression problem, the result of MLP and Support Vector Regression (SVR) algorithms were compared based on their mean square, root mean square and mean absolute errors. The results obtained show that the accuracy of the RF classifier is 0.99, while the accuracies of the SVC and MLP methods are 0.98; moreover, in the case of flood prediction, the SVR algorithm outperforms the MLP approach.

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Section: Introductionmentioning

confidence: 73%

Artificial Intelligence Based Flood Forecasting for River Hunza at Danyor Station in Pakistan

Yaseen

Awais

Riaz

et al. 2022

Archives of Hydro-Engineering and Environmental Mechanics

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“…Such models can be used as a module in general hydrological analysis models [9], [85], [86]. [87] used MLP-ANN and hybrid multilayer perceptron (MLP-FFA) to forecast monthly river flow for a set of time intervals using observed data. Their results show that MLP-FFA model is satisfactory for monthly river flow simulation in Ajichay watershed.…”

Section: Results Analysis and Discussionmentioning

confidence: 99%

“…In this work, the use of a single hidden layer was found to be enough to have simulation results of the model with good convergence and performance [78], [79]. The ideal number of nodes in the intermediate layer has been defined following a trial and error method (forward approach) by changing the intermediate-layer neurons number [45], [80]- [83], in this case, we start from an architecture with 2 nodes in the intermediate layer, after that, train and test the ANN, then constantly increase the hidden neurons number. We repeated the above procedure until training and testing improved, then we retain the architecture which gives the minimum of the error on the test base [75].…”

Section: ) Model Developmentmentioning

confidence: 99%

“…[44] developed an integrated adaptive neuro fuzzy inference system with firefly algorithm (ANFIS-FFA) to forecast monthly rainfall in Pahang River catchment, Malaysia. [45] adopted a novel simulation approach called MLP-FFA for monthly streamflow forecasting at Ajichay watershed, East Azerbaijani. [46] adopted a novel approach based on the integration of support vector regression (SVR) and FFA for rainfall predicting at two stations situated in a semiarid area, Iran.…”

Section: Introductionmentioning

confidence: 99%

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Highly Accurate Prediction Model for Daily Runoff in Semi-Arid Basin Exploiting Metaheuristic Learning Algorithms

et al. 2021

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Developing trustworthy rainfall-runoff (R-R) models can offer serviceable information for planning and managing water resources. Use of artificial neural network (ANN) in adopting such models and predicting changes in runoff has become popular among many hydrologists from a long time. However, since the optimization is the most significant phase in ANN training, researchers' attentiveness has been attracted to the ANN's biggest problem, i.e. its susceptibility of being blocked in local minima. Consequently, use of genetic algorithms (GA), particle swarm optimization (PSO), firefly algorithm (FFA) and improved particle swarm optimization (IPSO) approaches to increase the performance of ANN, have gained remarkable interest among distinct modern heuristic optimization approaches. In this paper, the capability of four improved ANN methods, hybrid GA-based ANN, PSO-based ANN, FFA-based ANN and IPSO-based ANN in modeling rainfall-runoff (R-R) is investigated. IPSO has been used in order to increase the ability of PSO, where the new positions of particles are dynamically adjusted using two procedures which is given form the velocity obtained by PSO and proposed velocity in IPSO. The random normal grated number with a dynamical scale factor is used to compute the new position of the best particles in proposed velocity. Daily R-R data from six stations distributed in the Seybouse watershed located in semi-arid region in Algeria were used in models' development. The selection of the input data sets was carried out using the autocorrelation, partial autocorrelation and cross correlation functions. The results of the four hybrid models were compared via performance metrics, viz., Root Mean Square Error (RMSE), Pearson's correlation coefficient (R), Nash Sutcliffe Efficiency coefficient (NSE), and via graphical analysis (scatter plots, time series and Taylor diagram). Outcomes of the analysis at all study stations disclosed that all the ANN models enhanced with IPSO overachieved the GA-based ANN, PSObased ANN and FFA-based ANN models in estimating runoff for both training and testing periods. The outcomes of the study indicate that the IPSO hybrid metaheuristic algorithm is the best technique in improving ANN capability in modeling daily R-R.

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“…Moreover, Taylor diagram is used to prepare a visual comprehension with the help of polar plot for the evaluation of modeling results. The Taylor diagram represents the normalization statndard deviation between simulated and observed values with normalized origin and R 2 are represented as directional angles (Darbandi & Pourhosseini, 2018). The interpretation of Taylor diagram is that an observed point is shown on graph and the closer the simulated performance measures to the observe point, the better the model performance (Al-Sudani, Salih & Yaseen, 2019).…”

Section: Proposed Methodologymentioning

confidence: 99%

Improving the prediction accuracy of river inflow using two data pre-processing techniques coupled with data-driven model

Nazir

Hussain

Faisal

et al. 2019

PeerJ

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River inflow prediction plays an important role in water resources management and power-generating systems. But the noises and multi-scale nature of river inflow data adds an extra layer of complexity towards accurate predictive model. To overcome this issue, we proposed a hybrid model, Variational Mode Decomposition (VMD), based on a singular spectrum analysis (SSA) denoising technique. First, SSA his applied to denoise the river inflow data. Second, VMD, a signal processing technique, is employed to decompose the denoised river inflow data into multiple intrinsic mode functions (IMFs), each with a relative frequency scale. Third, Empirical Bayes Threshold (EBT) is applied on non-linear IMF to smooth out. Fourth, predicted models of denoised and decomposed IMFs are established by learning the feature values of the Support Vector Machine (SVM). Finally, the ensemble predicted results are formulated by adding the predicted IMFs. The proposed model is demonstrated using daily river inflow data from four river stations of the Indus River Basin (IRB) system, which is the largest water system in Pakistan. To fully illustrate the superiority of our proposed approach, the SSA-VMD-EBT-SVM hybrid model was compared with SSA-VMD-SVM, VMD-SVM, Empirical Mode Decomposition (EMD) based i.e., EMD-SVM, SSA-EMD-SVM, Ensemble EMD (EEMD) based i.e., EEMD-SVM and SSA-EEMD-SVM. We found that our proposed hybrid SSA-EBT-VMD-SVM model outperformed than others based on following performance measures: the Nash-Sutcliffe Efficiency (NSE), Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). Therefore, SSA-VMD-EBT-SVM model can be used for water resources management and power-generating systems using non-linear time series data.

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River flow simulation using a multilayer perceptron-firefly algorithm model

Cited by 20 publications

References 20 publications

Artificial Intelligence Based Flood Forecasting for River Hunza at Danyor Station in Pakistan

Artificial Intelligence Based Flood Forecasting for River Hunza at Danyor Station in Pakistan

Highly Accurate Prediction Model for Daily Runoff in Semi-Arid Basin Exploiting Metaheuristic Learning Algorithms

Improving the prediction accuracy of river inflow using two data pre-processing techniques coupled with data-driven model

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