Performance evaluation of artificial intelligence paradigms—artificial neural networks, fuzzy logic, and adaptive neuro-fuzzy inference system for flood prediction

Flood prediction has gained prominence world over due to the calamitous socio-economic impacts this hazard has and the anticipated increase of its incidence in the near future. Artificial intelligence (AI) models have contributed significantly over the last few decades by providing improved accuracy...

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Veröffentlicht in:	Environmental science and pollution research international Jg. 28; H. 20; S. 25265 - 25282
Hauptverfasser:	Tabbussum, Ruhhee, Dar, Abdul Qayoom
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2021 Springer Nature B.V
Schlagworte:	Accuracy Adaptive systems Algorithms Aquatic Pollution Artificial intelligence Artificial neural networks Atmospheric Protection/Air Quality Control/Air Pollution Back propagation Back propagation networks Bayesian analysis Bayesian theory Computer simulation Conjugates Earth and Environmental Science Economic impact Ecotoxicology Environment Environmental Chemistry Environmental Health Environmental science Flood control Flood forecasting Flood predictions Floods Fuzzy logic Fuzzy systems Inference Mathematical models Mean square errors Neural networks Performance evaluation Performance measurement prediction Prediction models prognosis Regularization Research Article Root-mean-square errors socioeconomics Statistical analysis Stream discharge Stream flow Waste Water Technology Water Management Water Pollution Control Gaussian membership function Takagi-Sugeno fuzzy inference system Levenberg Marquardt neural network Flood forecasting Subtractive clustering Adaptive neuro-fuzzy inference system
ISSN:	0944-1344, 1614-7499, 1614-7499
Online-Zugang:	Volltext
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