Rainfall-runoff model usingan artificial neural network approach

Riad, S.; Mania, Jacky; Bouchaou, Lhoussaine; Najjar, Yacoub M.

doi:10.1016/j.mcm.2004.10.012

Cited by 190 publications

(72 citation statements)

References 15 publications

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“…By normalization method, each number is converted to a number between 0 and 1 to be applicable to the neural network function (Riad et al 2004). For this purpose, the following equations are used:…”

Section: Study Areamentioning

confidence: 99%

A new approach to flow simulation using hybrid models

et al. 2017

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The necessity of flow prediction in rivers, for proper management of water resource, and the need for determining the inflow to the dam reservoir, designing efficient flood warning systems and so forth, have always led water researchers to think about models with highspeed response and low error. In the recent years, the development of Artificial Neural Networks and Wavelet theory and using the combination of models help researchers to estimate the river flow better and better. In this study, daily and monthly scales were used for simulating the flow of Gamasiyab River, Nahavand, Iran. The first simulation was done using two types of ANN and ANFIS models. Then, using wavelet theory and decomposing input signals of the used parameters, sub-signals were obtained and were fed into the ANN and ANFIS to obtain hybrid models of WANN and WANFIS. In this study, in addition to the parameters of precipitation and flow, parameters of temperature and evaporation were used to analyze their effects on the simulation. The results showed that using wavelet transform improved the performance of the models in both monthly and daily scale. However, it had a better effect on the monthly scale and the WANFIS was the best model.

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Section: Study Areamentioning

confidence: 99%

A new approach to flow simulation using hybrid models

et al. 2017

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“…Scaling the axes, or standardizing the variables is a common technique (e.g., Liu et al, 2001;Ouarda et al, 2001;Mo and Beven, 2004;Riad et al, 2004). Application of such The mean CV The mean stdev Table 3.…”

Section: The Total Variation Trend Of Sm Over the 48 Yearsmentioning

confidence: 99%

Temporal variation of soil moisture over the Wuding River basin assessed with an eco-hydrological model, in-situ observations and remote sensing

Liu

Zhao

et al. 2009

Hydrol. Earth Syst. Sci.

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Abstract. The change pattern and trend of soil moisture (SM) in the Wuding River basin, Loess Plateau, China is explored based on the simulated long-term SM data from 1956 to 2004 using an eco-hydrological process-based model, Vegetation Interface Processes model, VIP. In-situ SM observations together with a remotely sensed SM dataset retrieved by the Vienna University of Technology are used to validate the model. In the VIP model, climate-eco-hydrological (CEH) variables such as precipitation, air temperature and runoff observations and also simulated evapotranspiration (E T ), leaf area index (LAI), and vegetation production are used to analyze the soil moisture evolution mechanism. The results show that the model is able to capture seasonal SM variations. The seasonal pattern, multi-year variation, standard deviation and coefficient of variation (C V ) of SM at the daily, monthly and annual scale are well explained by CEH variables. The annual and inter-annual variability of SM is the lowest compared with that of other CEH variables. The trend analysis shows that SM is in decreasing tendency at α=0.01 level of significance, confirming the Northern Drying phenomenon. This trend can be well explained by the decreasing tendency of precipitation (α=0.1) and increasing tendency of temperature (α=0.01). The decreasing tendency of runoff has higher significance level (α=0.001). Because of SM's decreasing tendency, soil evaporation (E S ) is also Correspondence to: S. Liu (liusx@igsnrr.ac.cn) decreasing (α=0.05). The tendency of net radiation (R n ), evapotranspiration (E T ), transpiration (E C ), canopy intercept (E I ) is not obvious. Net primary productivity (NPP), of which the significance level is lower than α=0.1, and gross primary productivity (GPP) at α=0.01 are in increasing tendency.

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“…As MLPs possuem capacidade de modelar sistemas não-lineares sem a necessidade de realizar suposições sobre a interação das variáveis que compõem tais sistemas (RIAD et al, 2004).…”

Section: Introductionunclassified

“…Riad et al (2004) estudando a bacia hidrográfica de Ourika no Marrocos (503 km 2 ), obtiveram R 2 significativo de 0,917 ao comporem os vetores de entrada com as variáveis de precipitação e escoamento superficial dos 7 dias precedentes ao dia t, a partir de séries históricas de sete anos. Oliveira et al (2013) Estes estudos mostram que as RNAs são eficazes para simular eventos hidrológicos, especialmente para a previsão de vazões.…”

Section: Introductionunclassified

Eficácia da arquitetura MLP em modo closed-loop para simulação de um Sistema Hidrológico

Debastiani

Silva

Neto

2016

RBRH

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Este é um artigo publicado em acesso aberto (Open Access) sob a licença Creative Commons Attribution, que permite uso, distribuição e reprodução em qualquer meio, sem restrições desde que o trabalho original seja corretamente citado. Eficácia da arquitetura MLP em modo closed-loop para simulação de um Sistema HidrológicoEfficiency of MLP architecture on closed-loop mode for the simulation of a hydrological system ABSTRACTEstimatives of hydrological responses are needed for the watershed planning. The aim of this study was to evaluate the hydrological behavior simulation of the Upper Canoas basin using artificial neural networks Multi Layer Perceptron (MLP) method, as well as to analyze the contribution of the input variables for modeling. It were tested 12 treatments with combinations of variables such as precipitation, evapotranspiration (ET0) and discharge, as well as transformations and temporal displacements of these variables, in order to determine the variables that promoted the better performance on discharge modeling. The MLP was trained in open-loop mode using part of the observed discharges. The discharges for the whole series were simulated in closed-loop, using the discharge simulated on the previous time step as input. The learning algorithm used was the Levenberg-Marquardt. The treatment with the best performance (NS = 0.9119, RMS = 14.29 m 3 /s) employed the daily precipitation of the four rainfall stations (Urubici, Vila Canoas, Lomba Alta e Anitápolis), precipitation of the four stations with -2 days of response time, and simulated discharge from the previous day. Despite the low RMS, the modeled discharge using MLP was generally overestimated.

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Rainfall-runoff model usingan artificial neural network approach

Cited by 190 publications

References 15 publications

A new approach to flow simulation using hybrid models

A new approach to flow simulation using hybrid models

Temporal variation of soil moisture over the Wuding River basin assessed with an eco-hydrological model, in-situ observations and remote sensing

Eficácia da arquitetura MLP em modo closed-loop para simulação de um Sistema Hidrológico

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