scholarly journals A mathematical model for QPF for flood Forecasting purposes

MAUSAM ◽  
2021 ◽  
Vol 42 (2) ◽  
pp. 201-204
Author(s):  
P. N. SEN
Keyword(s):  
Mathematical Model ◽  
Time Integration ◽  
Flood Forecasting ◽  
Rainfall Forecasting ◽  
Precipitation Forecasting ◽  
Meteorological Observations

A mathematical, model for Quantitative Precipitation Forecasting (QPF) has been developed on the basis of physical and dynamical laws. The surface and upper air meteorological observations have been used as inputs in the model. The output is the rate of precipitation from which the amount of precipitation can be computed time integration. The model can be used operationally for rainfall forecasting.

scholarly journals Flash flood forecasting in poorly gauged basins using neural networks: case study of the Gardon de Mialet basin (southern France)

2012 ◽  
Vol 12 (11) ◽  
pp. 3307-3324 ◽  
Author(s):  
G. Artigue ◽  
A. Johannet ◽  
V. Borrell ◽  
S. Pistre
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Flash Flood ◽  
Network Models ◽  
Weather Conditions ◽  
Flood Forecasting ◽  
Target Area ◽  
Rainfall Forecasting ◽  
Neural Network Models ◽  
Southern France

Abstract. In southern France, flash flood episodes frequently cause fatalities and severe damage. In order to inform and warn populations, the French flood forecasting service (SCHAPI, Service Central d'Hydrométéorologie et d'Appui à la Prévision des Inondations) initiated the BVNE (Bassin Versant Numérique Expérimental, or Experimental Digital Basin) project in an effort to enhance flash flood predictability. The target area for this study is the Gardon d'Anduze basin, located in the heart of the Cévennes range. In this Mediterranean mountainous setting, rainfall intensity can be very high, resulting in flash flooding. Discharge and rainfall gauges are often exposed to extreme weather conditions, which undermines measurement accuracy and continuity. Moreover, the processes governing rainfall-discharge relations are not well understood for these steeply-sloped and heterogeneous basins. In this context of inadequate information on both the forcing variables and process knowledge, neural networks are investigated due to their universal approximation and parsimony properties. We demonstrate herein that thanks to a rigorous variable and complexity selection, efficient forecasting of up to two-hour durations, without requiring rainfall forecasting as input, can be derived using the measured discharges available from a feedforward model. In the case of discharge gauge malfunction, in degraded mode, forecasting may result using a recurrent neural network model. We also observe that neural network models exhibit low sensitivity to uncertainty in rainfall measurements since producing ensemble forecasting does not significantly affect forecasting quality. In providing good results, this study suggests close consideration of our main purpose: generating forecasting on ungauged basins.

scholarly journals NUMERICAL ANALYSIS OF LIQUID-SOLID ADSORPTION MODEL

2019 ◽  
Vol 24 (4) ◽  
pp. 598-616
Author(s):  
Teresė Leonavičienė ◽  
Raimondas Čiegis ◽  
Edita Baltrėnaitė ◽  
Valeriia Chemerys
Keyword(s):  
Mathematical Model ◽  
Time Integration ◽  
Numerical Algorithms ◽  
Control Analysis ◽  
Adsorption Model ◽  
Nonlinear Mathematical Model ◽  
Integration Algorithms ◽  
Volume Method ◽  
The Mathematical Model ◽  
Adsorption Systems

In this paper, the numerical algorithms for solution of pore volume and surface diffusion model of adsorption systems are constructed and investigated. The approximation of PDEs is done by using the finite volume method for space derivatives and ODE15s solvers for numerical integration in time. The analysis of adaptive in time integration algorithms is presented. The main aim of this work is to analyze the sensitivity of the solution with respect to the main parameters of the mathematical model. Such a control analysis is done for a linearized and normalized mathematical model. The obtained results are compared with simulations done for a full nonlinear mathematical model.

MODELS FOR SPATIALIZATION AND FORECASTING OF FLOODED AREAS IN THE SÃO SEBASTIÃO DO CAÍ URBAN ZONE, RIO GRANDE DO SUL STATE, BRAZIL

2011 ◽  
Vol 38 (2) ◽  
pp. 132
Author(s):  
Guilherme Garcia De OLIVEIRA ◽  
Dejanira Luderitz SALDANHA ◽  
Laurindo Antonio GUASSELLI
Keyword(s):  
Mathematical Model ◽  
Urban Area ◽  
Satellite Image ◽  
Digital Terrain Model ◽  
Flood Forecasting ◽  
Average Error ◽  
Terrain Model ◽  
Historical Series ◽  
Elevation Data ◽  
Flooded Area

The study aims at developing models for the spatialization and forecasting of floods in the urban area of São Sebastião do Caí, RS, Brazil. For the calculation of return period (RP), and in order to analyze the seasonality of floods, streamflow data from the station located in the city were used. However, for the development of a mathematical model for flood forecasting, the time series of a station upstream was also used in order to perform a regression with the quotas recorded in both seasons. For the identification of flood plains, a digital terrain model was produced based on elevation data in scales between 1:2,000 and 1:10,000. The QuickBird satellite image (spatial resolution of 0.61 m) was used only for the spatialization of the land use and land cover reached by each flood scenario. Mapping and 3D simulation of the areas affected by flooding were obtained for RP of 2, 5, 10 and 30 years. The following results are most significant: i) the river water level rises between 9.28 m and 11.98 m for RP of 2 to 30 years; ii) along the historical series, 75% of floods have occurred between June and October; iii) the mathematical model for flood forecasting showed an average error of 0.72 m, and the accuracy varies between 0.62 m and 1.84 m, according to the expected magnitude; iv) it was observed that 93 hectares of urban area in São Sebastião do Caí are hit by floods with a RP of 30 years (23% of the urban area); v) modelling of a recent flood event dated of 24/09/2007 has resulted in similar values for the simulated and observed flooded area.

scholarly journals Evaluation of RadVil, a Radar-Based Very Short-Term Rainfall Forecasting Model

2006 ◽  
Vol 7 (1) ◽  
pp. 178-189 ◽  
Author(s):  
Brice Boudevillain ◽  
Hervé Andrieu ◽  
Nadine Chaumerliac
Keyword(s):  
Spatial Scales ◽  
Temporal Structure ◽  
Forecast Model ◽  
Radar Data ◽  
Rainfall Forecasting ◽  
Short Term ◽  
Precipitation Forecasting ◽  
Short Lead Time ◽  
Rain Events ◽  
Measurement Strategies

Abstract A very short-term rainfall forecast model is tested on actual radar data. This model, called RadVil, takes advantages of voluminal radar data through vertically integrated liquid (VIL) water content measurements. The model is tested on a dataset collected during the intensive observation period of the Mesoscale Alpine Program (MAP). Five rain events have been studied during this experiment. The results confirm the interest of VIL for quantitative precipitation forecasting at very short lead time. The evaluation is carried out in qualitative and quantitative ways according to Nash and correlation criteria on forecasting times ranging from 10 to 90 min and spatial scales from 4 to 169 km2. It attempts to be consistent with the hydrological requirements concerning the rainfall forecasting, for instance, by taking account of the relation between the catchments' size, their response time, and the required forecasting time. Several versions of RadVil corresponding to several VIL measurement strategies have been tested. Improvements offered by RadVil depend on meteorological situations. They are related to the spatial and temporal evolution of the VIL field structure and the validity of the models assumptions. Finally, a relationship between the temporal structure of VIL fields and forecast quality is established.

scholarly journals Ensemble Numerical Modeling Approach with Social Network Information to Optimize Flood Forecasting

10.29007/7crq ◽  
2018 ◽  
Author(s):  
Pin-Hao Liao ◽  
Dong-Sin Shih
Keyword(s):  
Numerical Modeling ◽  
Social Network ◽  
Heavy Rain ◽  
Flood Forecasting ◽  
Learning System ◽  
Physical Parameters ◽  
Peak Time ◽  
Rainfall Forecasting ◽  
Network Information ◽  
Section Data

The rivers in Taiwan are steep, the surface runoff is rushed into ocean quickly with high speeds and large discharges. When the typhoons hit Taiwan with heavy rain, how to predict correct peak time and peak stage of rivers is the most important aim in this research. Taiwan Typhoon and Flood Research Institute will produce a rainfall forecasting every six hours for disaster warning, according to different physical parameters setting. The research site, Xiuguluan River is steepest one of Taiwan central rivers. By cross section data、land use、slope、soils and the rainfall forecasting, we can get results of each member by integrating the physically based on model HEC-HMS and WASH123D.The research reveals that ensemble numerical modeling can predict precise peak stage of the river by analysis and correction by machine learning system TensorFlow. As for peak time forecasting, it becomes accurate by making use of the open social network information such as facebook、network news、PTT discussion to improve. Moreover, no matter peak time or peak stage, it has highly variation in members. In other words, no member is always the best of typhoons. But we can use the probability flood forecasting to predict and get the best results.

scholarly journals Supervised Multilevel Clustering for Rainfall forecasting using Meteorological data

2019 ◽  
Vol 8 (12) ◽  
pp. 1591-1596
Keyword(s):  
Clustering Algorithm ◽  
Weather Forecasting ◽  
Meteorological Data ◽  
Clustering Algorithms ◽  
Model Performance ◽  
Rainfall Forecasting ◽  
Weather Patterns ◽  
Improve Model ◽  
Meteorological Observations ◽  
Em Clustering

Atmospheric science focuses on weather processes and forecasting. Numerical and statistical analysis plays an important role in meteorological research. Meteorological data will be used to predict the changes in climatic patterns by using forecasting models and weather forecasting instruments. Data mining techniques have more scope to discover future weather patterns by analyzing past weather dimensions. In our study two techniques Multiple Linear Regression (MLR) and Expectation Maximization (EM) clustering algorithms are combined for rainfall forecasting. MLR interprets most important parameters of rainfall for clustering algorithm. EM clustering algorithm will find correctly and incorrectly clustered instances when applied on selected partitioned attributes. The model was able to forecast less rainfall, medium rainfall and high rainfall by analyzing past meteorological observations. Standard deviation is used as a measure of error correction to improve the cluster results. Data normalization helps to improve model performance. These findings are useful to determine future climate expectation.

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