An empirical-based rainfall-runoff modelling using optimization technique

Roy, Bappadittya; Singh, M. P.

doi:10.1080/15715124.2019.1680557

Cited by 34 publications

(18 citation statements)

References 89 publications

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“…Where n is the data number, Qo,i is the observed runoff, Qp,i is predicted runoff, ˆo Q is the average value of the observed runoff and ˆp Q is the average value of the predicted runoff [6].…”

Section: ) Statistical Performance Indicatorsmentioning

confidence: 99%

“…Also, developing accurate models to simulate rainfall-runoff process can help to manage water scarcity problems. However, the rainfall to runoff conversion is mightily nonlinear, stochastic and strongly complex process as there are several meteorological parameters and other various subprocesses influence this complicated system [6], [7]. Therefore, hydrologists and researchers have developed various rainfall-runoff (R-R) models in order to capture and represent this intricate phenomenon, where the model selection has to be made according to its ability and levels of complexity [8].…”

Section: Introductionmentioning

confidence: 99%

“…In the last few years, several optimization tools have been used to enhance the potential of the backpropagation algorithm including the gradient descent (GD) which is commonly applied in backpropagation stage of the neural network training process [19] and it's formulated as reducing the error between measured and predicted output at every iteration. Nevertheless, the GD may suffer from convergence issues, training method deceleration, stocking within local minima and overfitting; if the model structure is intricate and the parameter set is large, this results in poor performance of ANN models [6], [9], [20]. Recently, several conventional heuristic tools have been created to beat the deficiency of gradient-based techniques and to facilitate the solution of difficult optimization problems and obtain the optimal ANN parameters in training; in order to enhancing its efficiency.…”

Section: Introductionmentioning

confidence: 99%

“…[34] suggested a PSO-based perceptron approach to forecast water stage in the Shing Mun River in Hong Kong. [6] proposed a new hybrid metaheuristic algorithm combining BBO, PSO and GWO integrated with ANN and ANFIS for modeling R-R process in the watershed Fal at Tregony. [35], proposed three optimization algorithms integrated with ANFIS, i.e.…”

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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Section: ) Statistical Performance Indicatorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2021

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“…Mean-square error (MSE) is used as the fitness function for BBO algorithm. A more detailed review of BBO algorithm can be found in reference [37,38]. The Algorithm 1 shows a rudimentary structure of BBO-ANN.…”

Section: Bb0-annmentioning

confidence: 99%

Developing a New Computational Intelligence Approach for Approximating the Blast-Induced Ground Vibration

Kumar

Samui

et al. 2020

Applied Sciences

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Ground vibration induced by blasting operations is an important undesirable effect in surface mines and has significant environmental impacts on surrounding areas. Therefore, the precise prediction of blast-induced ground vibration is a challenging task for engineers and for managers. This study explores and evaluates the use of two stochastic metaheuristic algorithms, namely biogeography-based optimization (BBO) and particle swarm optimization (PSO), as well as one deterministic optimization algorithm, namely the DIRECT method, to improve the performance of an artificial neural network (ANN) for predicting the ground vibration. It is worth mentioning this is the first time that BBO-ANN and DIRECT-ANN models have been applied to predict ground vibration. To demonstrate model reliability and effectiveness, a minimax probability machine regression (MPMR), extreme learning machine (ELM), and three well-known empirical methods were also tested. To collect the required datasets, two quarry mines in the Shur river dam region, located in the southwest of Iran, were monitored, and the values of input and output parameters were measured. Five statistical indicators, namely the percentage root mean square error (%RMSE), coefficient of determination (R2), Ratio of RMSE to the standard deviation of the observations (RSR), mean absolute error (MAE), and degree of agreement (d) were taken into account for the model assessment. According to the results, BBO-ANN provided a better generalization capability than the other predictive models. As a conclusion, BBO, as a robust evolutionary algorithm, can be successfully linked to the ANN for better performance.

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Wavelet analysis of rainfall and application of hydrological model in a semi‐arid river basin of Rajasthan, India

Sharma,

Panda

et al. 2024

CLEAN Soil Air Water

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Rainfall–runoff modeling requires a selection of a suitable hydrologic model for the determination of an accurate quantity. A better understanding of rainfall–runoff processes is important for stream flow generation within a river basin. This study investigate the long‐term spatial–temporal trend of rainfall and streamflow using the hydrologic engineering center‐hydrologic modeling system (HEC‐HMS) model to understand the rainfall–runoff dynamics in the Banas River basin. Rainfall variability was analyzed for two periods: 1971–1995 and 1996–2020. Continuous wavelet transform was used to analyze the periodicity and the inter‐seasonal relationship in rainfall. HEC‐GeoHMS was used to generate the various inputs for the hydrologic modeling inputs for the HEC‐HMS model. Initial constant loss and ModClark transform method were used to set up the HEC‐HMS model for the study area. Calibration and validation of the model were performed using 3 years of observed data 2010–2012 and 2013–2015, respectively. Statistical model efficiency was checked using the coefficient of determination (R2), Nash–Sutcliffe efficiency, and root mean squared error to evaluate the performance of the HEC‐HMS model. The finding indicated that the R2 values are 0.80 for calibration and 0.82 for validation periods, respectively, which are in good arrangement with the measured values. This study analyzes the relationship between rainfall and runoff, which will aid in proper and efficient water resource management.

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An empirical-based rainfall-runoff modelling using optimization technique

Cited by 34 publications

References 89 publications

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

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

Developing a New Computational Intelligence Approach for Approximating the Blast-Induced Ground Vibration

Wavelet analysis of rainfall and application of hydrological model in a semi‐arid river basin of Rajasthan, India

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