River Flow Model Using Artificial Neural Networks

Aichouri, Imen; Hani, Azzedine; Bougherira, Nabil; Djabri, Larbi; Chaffai, Hicham; Lallahem, S.

doi:10.1016/j.egypro.2015.07.832

Cited by 97 publications

(55 citation statements)

References 8 publications

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“…This dataset included three-dimensional radar data of typhoon events and rain gauges from 1990 to 2004, including various typhoons. The results indicated that the ANN performed better.Aichouri, Hani, Bougherira, Djabri, Chaffai, and Lallahem[167] implemented an MLP model for flood prediction, and compared the results with the traditional MLR model. The rainfall-runoff daily data from 1986 to 2003 were used for model building.…”

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confidence: 99%

Flood Prediction Using Machine Learning Models: Literature Review

Mosavi

Öztürk

Chau

2018

Water

981

469

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Floods are among the most destructive natural disasters, which are highly complex to model. The research on the advancement of flood prediction models contributed to risk reduction, policy suggestion, minimization of the loss of human life, and reduction the property damage associated with floods. To mimic the complex mathematical expressions of physical processes of floods, during the past two decades, machine learning (ML) methods contributed highly in the advancement of prediction systems providing better performance and cost-effective solutions. Due to the vast benefits and potential of ML, its popularity dramatically increased among hydrologists.Researchers through introducing novel ML methods and hybridizing of the existing ones aim at discovering more accurate and efficient prediction models. The main contribution of this paper is to demonstrate the state of the art of ML models in flood prediction and to give insight into the most suitable models. In this paper, the literature where ML models were benchmarked through a qualitative analysis of robustness, accuracy, effectiveness, and speed are particularly investigated to provide an extensive overview on the various ML algorithms used in the field. The performance comparison of ML models presents an in-depth understanding of the different techniques within the framework of a comprehensive evaluation and discussion. As a result, this paper introduces the most promising prediction methods for both long-term and short-term floods. Furthermore, the major trends in improving the quality of the flood prediction models are investigated. Among them, hybridization, data decomposition, algorithm ensemble, and model optimization are reported as the most effective strategies for the improvement of ML methods. This survey can be used as a guideline for hydrologists as well as climate scientists in choosing the proper ML method according to the prediction task. Keywords: flood prediction; flood forecasting; flash-flood model, big flood management; hydrologic model; rainfall-runoff, hybrid & ensemble machine learning; artificial neural networks (ANNs); support vector machines (SVM); natural hazards & disasters; adaptive neuro-fuzzy inference system (ANFIS); decision trees (DT); internet of things (IoT); random forest (RF); survey; classification and regression trees (CART), data science; deep learning; big data; bagging, boosting, artificial intelligence (AI); soft computing; extreme event management; time series prediction; multilayer perceptron (MLP); simulated annealing (SA); multivariate adaptive regression splines (MARS), supervised learning IntroductionAmong the natural disasters, floods are the most destructive, causing massive damage to human life, infrastructure, agriculture, and the socioeconomic system.Governments, therefore, are under pressure to develop reliable and accurate maps of flood risk areas and further plan for sustainable flood risk management focusing on prevention, protection, and preparedness [1]. Flood prediction models are of significant impor...

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confidence: 99%

Flood Prediction Using Machine Learning Models: Literature Review

Mosavi

Öztürk

Chau

2018

Water

981

469

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“…As RNAs desenvolvidas para as seções de monitoramento de vazão da bacia do rio Piracicaba apresentaram desempenho adequado e semelhante aos obtidos por Aichouri et al (2015), Elsafi (2014), Mehr et al (2015), Okkan et al (2012), Setiono (2015), Tayyab et al (2016) e Tongal et al (2013), quando da utilização de RNAs com o mesmo objetivo, em bacias hidrográficas de diferentes países.…”

Section: Estação Carrapato (56640000)unclassified

“…Entre os modelos empíricos, as Redes Neurais Artificiais (RNAs) apresentam resultados promissores para a estimativa das vazões de cursos de água, como demonstrado por Aichouri et al (2015), Elsafi (2014), Meng et al (2015), Oliveira et al (2013), Sattari;Apaydin;Ozturk (2012) e Setiono (2015).…”

Section: Introductionunclassified

Desenvolvimento De Redes Neurais Artificiais Para Estimativa Das Vazões Diárias Na Bacia Do Rio Piracicaba5

Uliana

Silva²,

Moreira

et al. 2018

R_I

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Palavras-Chave: modelo empírico inteligência artificial, modelagem hidrológica. ULIANA, E. M.; SILVA, D. D.; MOREIRA, M. C.; PEREIRA, D. R.; PEREIRA, S. B.; ALMEIDA, F. T. ARTIFICIAL NEURAL NETWORKS FOR DAILY FLOW ESTIMATES IN THE PIRACICABA RIVER BASIN 2 ABSTRACTArtificial neural networks (ANNs) have been used alternatively in hydrologic modeling to estimate accurately watercourse flows based on hydrometeorological data. This study developed artificial neural networks to estimate daily flows in Piracicaba river basin, in Minas Gerais state (Brazil). For this, we used three runoff-monitoring sections of the Piracicaba river

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“…Il établit des relations entre des variables dites « paramètres d′entrée » et d′autres variables dites « paramètres de sortie ». Il se caractérise par sa topologie et son fonctionnement (Aichouri et al, 2015 ;Yao, 2011 ;Zhang et al, 2003). La capacité du PMC à modéliser n′importe quel phénomène complexe tel que les problèmes environnementaux n′est plus à démontrer (Shouliang et al, 2013 ;Ma et al, 2014).…”

Section: Introductionunclassified

Prédiction de l’évolution spatio-temporelle du fer à l’exutoire d’un fleuve tropical : cas du fleuve Sassandra à Gaoulou (Côte d’Ivoire)

Yao¹,

Agbri²,

Bamba³

et al. 2017

Int. J. Bio. Chem. Sci

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La modélisation de l′évolution spatio-temporelle du fer à l′exutoire du fleuve Sassandra (la station hydrométrique de Gaoulou) a fait l′objet de cette étude. Pour ce faire, il a été utilisé le modèle hybride « ACP-PMC ». Deux scénarios ont été étudiés : la modélisation statique du fer et la modélisation dynamique du fer. La première partie de cette étude a été consacré à la recherche des paramètres d′entrée du perceptron multicouche (PMC) par l′analyse en composante principal (ACP). Les résultats obtenus ont permis de choisir comme paramètres d′entrée pour la modélisation statique du fer : les ions sulfates (SO 4 2-), le pH, la conductivité, le dioxyde de silicium (SiO 2 ) et le débit mensuel du fleuve Sassandra. A ces paramètres, il a été ajouté le paramètre temps (mois) pour la modélisation dynamique du fer. Les différentes simulations réalisées par le PMC, ont permis de montrer que les modèles 5-1-1 et 6-12-1 prédisent à plus de 99% respectivement les évolutions statique et dynamique du fer à Gaoulou. Ainsi, l′utilisation de ce modèle hybride est-t-il jugée très satisfaisante. Ce modèle peut servir de socle pour d′éventuelles études visant la gestion rationnelle, la protection et le développement durable du fleuve Sassandra et de l′océan Atlantique au niveau de l′exutoire de ce fleuve. © 2016 International Formulae Group. All rights reserved.Mots clés : ACP, modèle « ACP-PMC », océan Atlantique, pollution métallique, PMC.Prediction of the spatial and temporal evolution of the iron at tropical river outfall: case of Sassandra River at Gaoulou (Côte d'Ivoire) ABSTRACTThe modeling of the spatial and temporal evolution of the iron at the Sassandra River outfall (hydrometric station of Gaoulou) is the subject of this study. To do this, it was used the hybrid model "PCA-MLP." Two scenarios were studied: static modeling of iron and dynamic modeling of iron. The first part of this study has been devoted to research of the input parameters of multilayer perceptron (MLP) by principal component analysis (PCA). The results obtained allowed to select as input parameters for the static modeling of the iron: sulphate ions (SO 4 2-), pH, conductivity, silicon dioxide (SiO 2 ) and the monthly rate of the Sassandra River. In these settings, it was added the time parameter (month) for the dynamic modeling of the M. K. YAO et al. / Int. J. Biol. Chem. Sci. 10(6): 2768-2780 iron. The various simulations performed by the PMC showed that the models 5-1-1 and 6-12-1 predict more than 99% static and dynamic evolutions of the iron at Gaoulou respectively. Thus, the use of this hybrid model is satisfactory. It can be used for any studies to rational management, protection and sustainable development of the Sassandra River and the Atlantic Ocean near to Sassandra outfall.

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River Flow Model Using Artificial Neural Networks

Cited by 97 publications

References 8 publications

Flood Prediction Using Machine Learning Models: Literature Review

Flood Prediction Using Machine Learning Models: Literature Review

Desenvolvimento De Redes Neurais Artificiais Para Estimativa Das Vazões Diárias Na Bacia Do Rio Piracicaba5

Prédiction de l’évolution spatio-temporelle du fer à l’exutoire d’un fleuve tropical : cas du fleuve Sassandra à Gaoulou (Côte d’Ivoire)

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