Soft Computing Techniques for Rainfall-Runoff Simulation: Local Non–Parametric Paradigm vs. Model Classification Methods

Rezaie-Balf, Mohammad; Zahmatkesh, Zahra; Kim, Sungwon

doi:10.1007/s11269-017-1711-9

Cited by 46 publications

(16 citation statements)

References 59 publications

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“…The test equation which is an adaptive amount is used to approximate high dimensional BFs for decreasing perfect fitness probability. N observations are used to calculate GCV of the training data model [40].…”

Section: Multivariate Adaptive Regression Splinesmentioning

confidence: 99%

Compressive Strength Prediction of Self-Compacting Concrete Incorporating Silica Fume Using Artificial Intelligence Methods

Azizifar

Babajanzadeh

2018

CivileJournal

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This paper investigates the capability of utilizing Multivariate Adaptive Regression Splines (MARS) and Gene Expression Programing (GEP) methods to estimate the compressive strength of self-compacting concrete (SCC) incorporating Silica Fume (SF) as a supplementary cementitious materials. In this regards, a large experimental test database was assembled from several published literature, and it was applied to train and test the two models proposed in this paper using the mentioned artificial intelligence techniques. The data used in the proposed models are arranged in a format of seven input parameters including water, cement, fine aggregate, specimen age, coarse aggregate, silica fume, super-plasticizer and one output. To indicate the usefulness of the proposed techniques statistical criteria are checked out. The results testing datasets are compared to experimental results and their comparisons demonstrate that the MARS (R2=0.98 and RMSE= 3.659) and GEP (R2=0.83 and RMSE= 10.362) approaches have a strong potential to predict compressive strength of SCC incorporating silica fume with great precision. Performed sensitivity analysis to assign effective parameters on compressive strength indicates that age of specimen is the most effective variable in the mixture.

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Section: Multivariate Adaptive Regression Splinesmentioning

confidence: 99%

Compressive Strength Prediction of Self-Compacting Concrete Incorporating Silica Fume Using Artificial Intelligence Methods

Azizifar

Babajanzadeh

2018

CivileJournal

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“…Also, their results showed that Q with lag time enhanced the models' predictive power. Rezaie-Balf et al (2017) employed ANNs, a model tree algorithm, and multivariate adaptive regression splines to predict streamflow in northern Iran's Tajan catchment using rainfall, discharge, evaporation, morning relative humidity, noon relative humidity, evening relative humidity, wind velocity, maximum temperature, and minimum temperature, with and without lag time as input. The study focused on the effects of input size, the number of effective input variables for the streamflow prediction as well as the length of data time-series on the quality of streamflow simulations by the applied algorithms.…”

Section: Comparison With the Literaturementioning

confidence: 99%

Improving daily stochastic streamflow prediction: comparison of novel hybrid data-mining algorithms

Khosravi

Booij

Barzegar

et al. 2021

Hydrological Sciences Journal

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In the current paper, the efficiency of three new standalone data-mining algorithms [M5 Prime (M5P), Random Forest (RF), M5Rule (M5R)] and six novel hybrid algorithms of bagging (BA-M5P, BA-RF and BA-M5R) and Attribute Selected Classifier (ASC-M5P, ASC-RF and ASC-M5R) for streamflow prediction were assessed and compared with an autoregressive integrated moving average (ARIMA) model as a benchmark. The models used precipitation (P) and streamflow (Q) data from the period 1979-2012 for training and validation (70% and 30% of data, respectively). Different input combinations were prepared using both P and Q with different lag times. The best input combination proved to be that in which all of the the data were used (i.e. R and Q -with lag times). Overall, employing Q with different lag times proved to be more effective than using only P as input for streamflow prediction. Although all models showed very good predictive power, BA-M5P outperformed the other models.

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“…Runoff simulation plays a vital role in the management of reservoirs, appropriate planning for climatic extremes such as drought and flood hazards (Zahmatkesh et al, 2015). At any given spatial and temporal variations in explicit and implicit variables of watershed and precipitation characteristics, the relationship between rainfall and runoff is nonlinear and extremely complex (Kumar et al, 2019a(Kumar et al, , 2019bRezaie-Balf et al, 2017;Wu & Chau, 2011). The complex process of transformation of rainfall to runoff can be simulated through hydrologic models (Hadiani, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…These expressions are assessed based on the analysis of concurrent input and outputs of the hydrological time series data and not on physical relationships between hydrological parameters. In comparison with the theory-driven models that deliberate all potential parameters affecting the catchment output, fewer parameters are required in developing the data-driven models (Rezaie-Balf et al, 2017). Data-driven models are often applied in the absence of sufficient data, efficient in time and cost responsive (Mengistu et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Model coupling approach for daily runoff simulation in Hamp Pandariya catchment of Chhattisgarh state in India

Singh

Kumar

Jaiswal

et al. 2021

Environ Dev Sustain

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Runoff estimation is of immense importance in hydrological analysis for water resource planning and management. The developing countries cannot afford to establish a large number of gauging sites due to huge initial and operating expenditures. Hydrological modelling is an alternative solution to simulate the catchment response to extreme events under climate change for taking preventive measures. The hydrological models have their own leads and constraints, so because of limited hydrological data availability of the catchment, wavelet neural network (WNN), artificial neural network, adaptive neuro-fuzzy inference system, and Mike-11 Nedbor Afstromnings models were used in this study. These models were calibrated and validated using daily rainfall and runoff observations taken at Hamp Pandariya gauging station on Hamp river in the Chhattisgarh state of India. A comparative study of these models was carried out to investigate their performance, efficiency, and suitability for daily runoff simulation in Hamp Pandariya catchment and found suitable in simulating the hydrological response of the catchment and predicting runoff with a high degree of accuracy. The performance of these models was evaluated and compared with the aid of multiple goodness of fit criteria including coefficient of determinations (r 2 ), Nash-Sutcliffe model efficiency index (NS), root mean square error, and water balance for model calibration and validation. These parameters indicated good agreement between observed and simulated runoff in terms of time to peak, discharge rate, daily and accumulated runoff volume, and shape of the hydrograph. The WNN was found the most appropriate model for future application due to Nash-Sutcliffe efficiency (NS) of 97% and 98% in calibration and validation, respectively, and the coefficient of determination as 99% both in calibration and validation.

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Soft Computing Techniques for Rainfall-Runoff Simulation: Local Non–Parametric Paradigm vs. Model Classification Methods

Cited by 46 publications

References 59 publications

Compressive Strength Prediction of Self-Compacting Concrete Incorporating Silica Fume Using Artificial Intelligence Methods

Compressive Strength Prediction of Self-Compacting Concrete Incorporating Silica Fume Using Artificial Intelligence Methods

Improving daily stochastic streamflow prediction: comparison of novel hybrid data-mining algorithms

Model coupling approach for daily runoff simulation in Hamp Pandariya catchment of Chhattisgarh state in India

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