scholarly journals Identification of spatial and temporal contributions of rainfalls to flash floods using neural network modelling: case study on the Lez Basin (Southern France)

2015 ◽  
Vol 12 (4) ◽  
pp. 3681-3718
Author(s):  
T. Darras ◽  
V. Borrell Estupina ◽  
L. Kong-A-Siou ◽  
B. Vayssade ◽  
A. Johannet ◽  
...  
Keyword(s):  
Neural Network ◽  
Global Climate ◽  
Flash Floods ◽  
Rainfall Runoff ◽  
Crucial Importance ◽  
Network Modelling ◽  
Southern France ◽  
Flood Warning ◽  
Warning Services

Abstract. Flash floods pose significant hazards in urbanised zones and have important human and financial implications in both the present and future due to the likelihood that global climate change will exacerbate their consequences. It is thus of crucial importance to better model these phenomena especially when they occur in heterogeneous and karst basins where they are difficult to describe physically. Toward this goal, this paper applies a recent methodology (KnoX methodology) dedicated to extracting knowledge from a neural network model to better determine the contributions and time responses of several well-identified geographic zones of an aquifer. To assess the interest of this methodology, a case study was conducted in Southern France: the Lez hydrosystem whose river crosses the conurbation of Montpellier (400 000 inhabitants). Rainfall contributions and time transfers were estimated and analysed in four geologically-delimited zones to estimate the sensitivity of flash floods to water coming from the surface or karst. The Causse de Viol-le-Fort is shown to be the main contributor to flash floods and the delay between surface and underground flooding is estimated to be three hours. This study will thus help operational flood warning services to better characterise critical rainfall and develop measurements to design efficient flood forecasting models. This generic method can be applied to any basin with sufficient rainfall–runoff measurements.

scholarly journals Identification of spatial and temporal contributions of rainfalls to flash floods using neural network modelling: case study on the Lez basin (southern France)

2015 ◽  
Vol 19 (10) ◽  
pp. 4397-4410 ◽  
Author(s):  
T. Darras ◽  
V. Borrell Estupina ◽  
L. Kong-A-Siou ◽  
B. Vayssade ◽  
A. Johannet ◽  
...  
Keyword(s):  
Neural Network ◽  
Global Climate ◽  
Flash Floods ◽  
Crucial Importance ◽  
Network Modelling ◽  
Southern France ◽  
Flood Warning ◽  
Run Off ◽  
Warning Services

Abstract. Flash floods pose significant hazards in urbanised zones and have important implications financially and for humans alike in both the present and future due to the likelihood that global climate change will exacerbate their consequences. It is thus of crucial importance to improve the models of these phenomena especially when they occur in heterogeneous and karst basins where they are difficult to describe physically. Toward this goal, this paper applies a recent methodology (Knowledge eXtraction (KnoX) methodology) dedicated to extracting knowledge from a neural network model to better determine the contributions and time responses of several well-identified geographic zones of an aquifer. To assess the interest of this methodology, a case study was conducted in southern France: the Lez hydrosystem whose river crosses the conurbation of Montpellier (400 000 inhabitants). Rainfall contributions and time transfers were estimated and analysed in four geologically delimited zones to estimate the sensitivity of flash floods to water coming from the surface or karst. The Causse de Viols-le-Fort is shown to be the main contributor to flash floods and the delay between surface and underground flooding is estimated to be 3 h. This study will thus help operational flood warning services to better characterise critical rainfall and develop measurements to design efficient flood forecasting models. This generic method can be applied to any basin with sufficient rainfall–run-off measurements.

scholarly journals Hybrid ARIMA and Neural Network Modelling Applied to Telecommunications in Urban Environments in the Amazon Region

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Ramz L. Fraiha Lopes ◽  
Simone G. C. Fraiha ◽  
Vinicius D. Lima ◽  
Herminio S. Gomes ◽  
Gervásio P. S. Cavalcante
Keyword(s):  
Neural Network ◽  
Moving Average ◽  
Urban Environments ◽  
Digital Television ◽  
Network Modelling ◽  
Worst Case ◽  
Autoregressive Integrated Moving Average ◽  
Neural Network Modelling ◽  
Better Than

This study explores the use of a hybrid Autoregressive Integrated Moving Average (ARIMA) and Neural Network modelling for estimates of the electric field along vertical paths (buildings) close to Digital Television (DTV) transmitters. The work was carried out in Belém city, one of the most urbanized cities in the Brazilian Amazon and includes a case study of the application of this modelling within the subscenarios found in Belém. Its results were compared with the ITU recommendations P. 1546-5 and proved to be better in every subscenario analysed. In the worst case, the estimate of the model was approximately 65% better than that of the ITU. We also compared this modelling with a classic modelling technique: the Least Squares (LS) method. In most situations, the hybrid model achieved better results than the LS.

Artificial Neural Network Model for Rainfall-Runoff -A Case Study

2016 ◽  
Vol 9 (3) ◽  
pp. 263-272 ◽  
Author(s):  
P.Sundara Kumar ◽  
T. V. Praveen ◽  
M. Anjanaya Prasad
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Rainfall Runoff ◽  
Artificial Neural

Short-range visibility forecast by means of neural-network modelling: a case-study

1995 ◽  
Vol 18 (5) ◽  
pp. 505-516 ◽  
Author(s):  
A. Pasini ◽  
S. Potestà
Keyword(s):  
Neural Network ◽  
Short Range ◽  
Network Modelling ◽  
Neural Network Modelling

scholarly journals Investigation the Ability of Artificial Neural Network in Simulation of Rainfall-Runoff Process under the Climate Change Conditions (Case Study: Pashakola Babol Dam Basin)

2018 ◽  
Vol 8 (16) ◽  
pp. 53-64
Author(s):  
معصومه خادمی ◽  
رامین فضل‌ اولی ◽  
علیرضا عمادی ◽  
◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Climate Change ◽  
Artificial Neural Network ◽  
Rainfall Runoff ◽  
Artificial Neural

scholarly journals Warning! The use of meteorological information during a flash-flood warning process

2009 ◽  
Vol 3 (1) ◽  
pp. 99-103 ◽  
Author(s):  
L. Créton-Cazanave
Keyword(s):  
Emergency Services ◽  
Flash Flood ◽  
Local Population ◽  
Flash Floods ◽  
Decision Makers ◽  
Classical Approach ◽  
The Real ◽  
Flood Warning ◽  
Integrated Studies

Abstract. Warning is a key issue to reduce flash floods impacts. But, despite many studies, local and national authorities still struggle to issue good flash floods warnings. We will argue that this failure results from a classical approach of warnings, based on a strict separation between the assessment world and the action world. We will go further than the previous criticisms (Pielke and Carbone, 2002) and show that forecasters, decision makers, emergency services and local population have quite similar practices during a flash-flood warning. Focusing on the use of meteorological information in the warning process, our case study shows that more research about the real practices of stakeholders would be another step towards integrated studies.

scholarly journals Rainfall-Runoff Prediction based on Artificial Neural Network: A Case Study Priyadarshini Watershed

2019 ◽  
Vol 8 (05) ◽  
pp. 1328-1334
Author(s):  
S.K. Kothe ◽  
B.L. Ayare ◽  
H.N. Bhange ◽  
S.T. Patil
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Rainfall Runoff ◽  
Runoff Prediction ◽  
Artificial Neural

KnoX method, or Knowledge eXtraction from neural network model. Case study on the Lez karst aquifer (southern France)

2013 ◽  
Vol 507 ◽  
pp. 19-32 ◽  
Author(s):  
Line Kong-A-Siou ◽  
Kévin Cros ◽  
Anne Johannet ◽  
Valérie Borrell-Estupina ◽  
Séverin Pistre
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Karst Aquifer ◽  
Knowledge Extraction ◽  
Model Case ◽  
Southern France
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