Application of ANN and fuzzy logic algorithms for streamflow modelling of Savitri catchment

Kothari, Mahesh; Gharde, K. D.

doi:10.1007/s12040-015-0592-7

Cited by 21 publications

(7 citation statements)

References 27 publications

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“…Thus, ANN has been widely applied to solve several water-resource problems, and it was found to be a powerful tool for streamflow simulation (Aichouri et al, 2015). Kothari and Gharde (2015) applied ANN for the streamflow modeling of Savitri catchment, India. Zhou et al (2018) forecasted the monthly streamflow of the Jinsha River by using three (ANN) architectures: extreme learning machine, radial basis function network, and Elman network.…”

Section: Introductionmentioning

confidence: 99%

Hydrological modeling in a basin of the Brazilian Cerrado biome

Rodrigues¹,

Andrade²,

Viola³

et al. 2021

Rev. ambiente água

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The Brazilian Cerrado biome (BCB) is among 25 biodiversity hotspots identified worldwide, and covers the recharge area of important aquifers and rivers in South America. The increase in deforestation has been threatening water availability in this region. In order to assist in the water-resource management of the BCB, this study models the daily streamflow in a basin of the Cerrado, using two approaches: a process-based model (Soil and Water Assessment Tool - SWAT) and the data-driven model (Artificial Neural Network - ANN). The performance of the models was evaluated by the Nash-Sutcliffe coefficient (NSE), coefficient of determination (R2) and flow-duration-curves (FDC). The results indicate that SWAT (NSE > 0.61; R2 > 0.68) and ANN (NSE > 0.91; R2 > 0.79) models are suitable tools in daily streamflow modeling of the studied basin, with the ANN model being the most accurate. Based on FDC, the ANN model was also better than the SWAT model for all frequencies evaluated. Thus, the ANN model is a promising new approach for daily streamflow modelling in this region. Moreover, the results of this study can help water-resource managers in planning and implementing appropriate water allocation and conservation measures in the Brazilian Cerrado biome.

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Section: Introductionmentioning

confidence: 99%

Hydrological modeling in a basin of the Brazilian Cerrado biome

Rodrigues¹,

Andrade²,

Viola³

et al. 2021

Rev. ambiente água

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“…The ANN is the most popular SC-based method employed for streamflow forecasting due its capability to solve diverse complex problems [15]- [17]. The assignment of weights to the neurons for optimum performance of ANN is the major challenge in ANN-based forecasting model.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Univariate Paradigm for Streamflow Simulation Using Hybrid Data-Driven Model: Case Study in Tropical Region

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Mohtar

Ameen

et al. 2019

IEEE Access

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The performance of the bio-inspired adaptive neuro-fuzzy inference system (ANFIS) models are proposed for forecasting highly non-linear streamflow of Pahang River, located in a tropical climatic region of Peninsular Malaysia. Three different bio-inspired optimization algorithms namely particle swarm optimization (PSO), genetic algorithm (GA), and differential evolution (DE) were individually used to tune the membership function of ANFIS model in order to improve the capability of streamflow forecasting. Different combination of antecedent streamflow was used to develop the forecasting models. The performance of the models was evaluated using a number of metrics including mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R 2), and Willmott's Index (WI) statistics. The results revealed that increasing number of inputs has a positive impact on the forecasting ability of both ANFIS and hybrid ANFIS models. The comparison of the performance of three optimization methods indicated PSO improved the capability of ANFIS model (RMSE = 7.96; MAE = 2.34; R 2 = 0.998 and WI = 0.994) more compared to GA and DE in forecasting streamflow. The uncertainty band of ANFIS-PSO forecast was also found the lowest (±0.217), which indicates that ANFIS-PSO model can be used for reliable forecasting of highly stochastic river flow in tropical environment. INDEX TERMS Streamflow forecasting, fuzzy logic, evolutionary algorithm, uncertainty analysis, tropical environment.

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“…There are two main groups: statistical models and machine learning models. Statistical models include logistic regression (Long et al, 2022), fuzzy logic (Kothari and Gharde, 2015), autoregressive integrated moving average model (Ab Razak et al, 2018;Singh et al, 2020), and autoregressive model (Özgür, 2005;Terzi and Ergin, 2014). These models assume a linear relationship between input and output data, so they cannot explain the nonlinear relationship of hydrological processes.…”

Section: Introductionmentioning

confidence: 99%

Daily Streamflow Forecasting Using Extreme Learning Machine and Optimization Algorithm. Case Study: A River in Vietnam

NGUYEN¹

2022

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Accurate prediction of streamflow plays an important role in water resource management and sustainability. Recent years have seen increased interest in data-based models, compared to the more established physics-based models, due to the accuracy of their predictions. Better results mean greater support for those who are tasked with formulating strategies and writing policy around water resource management. The objective of this study is the development of a state-of-the-art streamflow prediction method based on extreme learning machine (ELM), optimized by both hunger games search (HGS) and social spider optimization (SSO) to make accurate predictions for the Tra Khuc River in Vietnam. Rainfall and flow from 2000 to 2020 at Son Giang station on the Tra Khuc River were used to build the streamflow prediction model. The statistical indices root-mean-square error, mean absolute error, and the coefficient of determination (R²) were applied to assess the predictive ability of the proposed models. The results showed that both optimization algorithms successfully improved the ELM model to predict the streamflow for one day and six days ahead by using data from one day and three days before the day in question. Of the proposed models, the ELM-SSO model scored highest, with R²=0.891 for the one-day-ahead prediction and R²=0.701 for six days ahead. Second was ELM-HGS (R²=0.889 and R²=0.699 for one day and six days respectively), and third was ELM (R²=0.883, R²=0.696). The results demonstrate ELM to be a robust data-driven method for simulating time series regimes that is appropriate for various hydrological applications. The models proposed in this study can be generalized to predict streamflow in rivers around the world.

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Application of ANN and fuzzy logic algorithms for streamflow modelling of Savitri catchment

Cited by 21 publications

References 27 publications

Hydrological modeling in a basin of the Brazilian Cerrado biome

Hydrological modeling in a basin of the Brazilian Cerrado biome

Implementation of Univariate Paradigm for Streamflow Simulation Using Hybrid Data-Driven Model: Case Study in Tropical Region

Daily Streamflow Forecasting Using Extreme Learning Machine and Optimization Algorithm. Case Study: A River in Vietnam

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