Comparative implementation between neuro-emotional genetic algorithm and novel ensemble computing techniques for modelling dissolved oxygen concentration DOI
Sani I. Abba, Rabiu Aliyu Abdulkadir,

Saad Sh. Sammen

et al.

Hydrological Sciences Journal, Journal Year: 2021, Volume and Issue: 66(10), P. 1584 - 1596

Published: June 3, 2021

Accurate prediction of dissolved oxygen (DO) concentration is important for managing healthy aquatic ecosystems. This study investigates the comparative potential emotional artificial neural network-genetic algorithm (EANN-GA), and three ensemble techniques, i.e. network (EANN), feedforward (FFNN), (NNE), to predict DO in Kinta River basin Malaysia. The performance EANN-GA, EANN, FFNN, NNE models predicting was evaluated using statistical metrics visual interpretation. Appraisal results revealed a promising NNE-M3 model (Nash-Sutcliffe efficiency (NSE) = 0.8743/0.8630, correlation coefficient (CC) 0.9351/0.9113, mean square error (MSE) 0.5757/0.6833 mg/L, root (RMSE) 0.7588/0.8266 absolute percentage (MAPE) 20.6581/14.1675) during calibration/validation period compared FFNN basin.

Language: Английский

Monthly evapotranspiration estimation using optimal climatic parameters: efficacy of hybrid support vector regression integrated with whale optimization algorithm DOI
Yazid Tikhamarine, Anurag Malik,

Kusum Pandey

et al.

Environmental Monitoring and Assessment, Journal Year: 2020, Volume and Issue: 192(11)

Published: Oct. 11, 2020

Language: Английский

Citations

62
A large-scale comparison of Artificial Intelligence and Data Mining (AI&DM) techniques in simulating reservoir releases over the Upper Colorado Region DOI Creative Commons
Tiantian Yang, Lujun Zhang, Taereem Kim

et al.

Journal of Hydrology, Journal Year: 2021, Volume and Issue: 602, P. 126723 - 126723

Published: July 26, 2021

Language: Английский

Citations

53
Prediction of daily water level using new hybridized GS-GMDH and ANFIS-FCM models DOI Creative Commons
Isa Ebtehaj,

Saad Sh. Sammen,

Lariyah Mohd Sidek

et al.

Engineering Applications of Computational Fluid Mechanics, Journal Year: 2021, Volume and Issue: 15(1), P. 1343 - 1361

Published: Jan. 1, 2021

Accurate prediction of water level (WL) is essential for the optimal management different resource projects. The development a reliable model WL remains challenging task in resources management. In this study, novel hybrid models, namely, Generalized Structure-Group Method Data Handling (GS-GMDH) and Adaptive Neuro-Fuzzy Inference System with Fuzzy C-Means (ANFIS-FCM) were proposed to predict daily at Telom Bertam stations located Cameron Highlands Malaysia. Different percentage ratio data division i.e. 50%–50% (scenario-1), 60%–40% (scenario-2), 70%–30% (scenario-3) adopted training testing these models. To show efficiency their results compared standalone models that include Gene Expression Programming (GEP) Group (GMDH). investigation revealed GS-GMDH ANFIS-FCM outperformed GEP GMDH both study sites. addition, indicate best performance was obtained scenario-3 (70%–30%). summary, highlight better suitability supremacy prediction, can, serve as robust predictive tools region.

Language: Английский

Citations

49
A simple machine learning approach to model real-time streamflow using satellite inputs: Demonstration in a data scarce catchment DOI
Ashish Kumar, RAAJ Ramsankaran, Luca Brocca

et al.

Journal of Hydrology, Journal Year: 2021, Volume and Issue: 595, P. 126046 - 126046

Published: Feb. 5, 2021

Language: Английский

Citations

45
Comparative implementation between neuro-emotional genetic algorithm and novel ensemble computing techniques for modelling dissolved oxygen concentration DOI
Sani I. Abba, Rabiu Aliyu Abdulkadir,

Saad Sh. Sammen

et al.

Hydrological Sciences Journal, Journal Year: 2021, Volume and Issue: 66(10), P. 1584 - 1596

Published: June 3, 2021

Accurate prediction of dissolved oxygen (DO) concentration is important for managing healthy aquatic ecosystems. This study investigates the comparative potential emotional artificial neural network-genetic algorithm (EANN-GA), and three ensemble techniques, i.e. network (EANN), feedforward (FFNN), (NNE), to predict DO in Kinta River basin Malaysia. The performance EANN-GA, EANN, FFNN, NNE models predicting was evaluated using statistical metrics visual interpretation. Appraisal results revealed a promising NNE-M3 model (Nash-Sutcliffe efficiency (NSE) = 0.8743/0.8630, correlation coefficient (CC) 0.9351/0.9113, mean square error (MSE) 0.5757/0.6833 mg/L, root (RMSE) 0.7588/0.8266 absolute percentage (MAPE) 20.6581/14.1675) during calibration/validation period compared FFNN basin.

Language: Английский

Citations

45