Development of real-time flood forecast model for vamsadhara river through hydrological approach

Abstract Multiple factors including rainfall and underlying surface conditions make river basin real-time flood forecasting very challenging. It is often necessary to use real-time correction techniques to modify the forecasting results so that they reach satisfactory accuracy. There are many such techniques in use today; however, they tend to have weak physical conceptual basis, relatively short forecast periods, unsatisfactory correction effects, and other problems. The mechanism that affects real-time flood forecasting error is very complicated. The strongest influencing factors corresponding to this mechanism affect the runoff yield of the forecast model. This paper proposes a feedback correction algorithm that traces back to the source of information, namely, modifies the watershed runoff. The runoff yield error is investigated using the principle of least squares estimation. A unit hydrograph is introduced into the real-time flood forecast correction; a feedback correction model that traces back to the source of information. The model is established and verified by comparison with an ideal model. The correction effects of the runoff yield errors are also compared in different ranges. The proposed method shows stronger correction effect and enhanced prediction accuracy than the traditional method. It is also simple in structure and has a clear physical concept without requiring added parameters or forecast period truncation. It is readily applicable in actual river basin flood forecasting scenarios.

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A rain-on-snow mixed flood forecast model and its application

Frontiers of Architecture and Civil Engineering in China ◽

10.1007/s11709-009-0051-0 ◽

2009 ◽

Vol 3 (4) ◽

pp. 440-444

Author(s):

Jian Wu ◽

Lan Li

Keyword(s):

Forecast Model ◽

Flood Forecast

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Test on Flood Prediction-Model Using Artificial Neural Network for ShiiLiAn Hydrologic Station on MinChiang,China

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.39.555 ◽

2010 ◽

Vol 39 ◽

pp. 555-561 ◽

Cited By ~ 1

Author(s):

Qing Hua Luan ◽

Yao Cheng ◽

Zha Xin Ima

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Flood Control ◽

Back Propagation ◽

Forecast Model ◽

Flood Forecast ◽

Back Propagation Algorithm ◽

Nonlinear Method ◽

Flood Prediction ◽

Artificial Neural

The establishing of a precise simulation model for runoff prediction in river with several tributaries is the difficulty of flood forecast, which is also one of the difficulties in hydrologic research. Due to the theory of Artificial Neural Network, using Back Propagation algorithm, the flood forecast model for ShiLiAn hydrologic station in Minjiang River is constructed and validated in this study. Through test, the result shows that the forecast accuracy is satisfied for all check standards of flood forecast and then proves the feasibility of using nonlinear method for flood forecast. This study provides a new method and reference for flood control and water resources management in the local region.

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Coupled Hydraulic and Kalman Filter Model for Real-Time Correction of Flood Forecast in the Three Gorges Interzone of Yangtze River, China

Journal of Hydrologic Engineering ◽

10.1061/(asce)he.1943-5584.0000473 ◽

2013 ◽

Vol 18 (11) ◽

pp. 1416-1425 ◽

Cited By ~ 14

Author(s):

Xiao-Ling Wu ◽

Xiao-Hua Xiang ◽

Chuan-Hai Wang ◽

Xi Chen ◽

Chong-Yu Xu ◽

...

Keyword(s):

Kalman Filter ◽

Real Time ◽

Yangtze River ◽

Flood Forecast ◽

Three Gorges ◽

Filter Model ◽

The Three Gorges ◽

Time Correction

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Improving probabilistic flood forecasting through a data assimilation scheme based on genetic programming

Natural Hazards and Earth System Science ◽

10.5194/nhess-12-3719-2012 ◽

2012 ◽

Vol 12 (12) ◽

pp. 3719-3732 ◽

Cited By ~ 4

Author(s):

L. Mediero ◽

L. Garrote ◽

A. Chavez-Jimenez

Keyword(s):

Data Assimilation ◽

Genetic Programming ◽

Real Time ◽

High Performance ◽

Forecast Model ◽

Flood Forecasting ◽

Distribution Functions ◽

Model Parameters ◽

Hydrological Process ◽

Model Based

Abstract. Opportunities offered by high performance computing provide a significant degree of promise in the enhancement of the performance of real-time flood forecasting systems. In this paper, a real-time framework for probabilistic flood forecasting through data assimilation is presented. The distributed rainfall-runoff real-time interactive basin simulator (RIBS) model is selected to simulate the hydrological process in the basin. Although the RIBS model is deterministic, it is run in a probabilistic way through the results of calibration developed in a previous work performed by the authors that identifies the probability distribution functions that best characterise the most relevant model parameters. Adaptive techniques improve the result of flood forecasts because the model can be adapted to observations in real time as new information is available. The new adaptive forecast model based on genetic programming as a data assimilation technique is compared with the previously developed flood forecast model based on the calibration results. Both models are probabilistic as they generate an ensemble of hydrographs, taking the different uncertainties inherent in any forecast process into account. The Manzanares River basin was selected as a case study, with the process being computationally intensive as it requires simulation of many replicas of the ensemble in real time.

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New Approaches to the Interpretation of Balance in the Digital Economy

Statistics and Economics ◽

10.21686/2500-3925-2020-3-25-36 ◽

2020 ◽

Vol 17 (3) ◽

pp. 25-36

Author(s):

M. I. Lugachev ◽

N. V. Ulianova ◽

K. G. Skripkin

Keyword(s):

Real Time ◽

Balance Sheet ◽

Forecast Model ◽

Financial Capital ◽

Selling Price ◽

The Past ◽

Financial Condition ◽

Operating Profit ◽

The Impact ◽

Operating Activities

The purpose of the article is to theoretically prove the possibility of generating forecast information in the balance-sheet regarding profit indicators, net inflow of operating money and financial capital. According to the authors, the system of these indicators is revealed in dynamics, thus reflecting the impact of profit on the financial condition of the organization. A logical and accounting balance-sheet relationship is established between actual and forecast indicators that characterize the financial condition in the past and future. By analyzing the processes in the operating cycle, the economic and financial feasibility of operating profit as a net cash flow from operating activities is theoretically proved. Based on the process approach and the induction method, the indicator of operating profit is included in the valuation of the asset and liability side of the balance-sheet, thereby developing the valuation method and forming a new forecast model of balance-sheet generalizations. The content of the forecast model of balance is described in the form of a balance equation. The obtained theoretical conclusions are verified experimentally.As a result, the asset of the balance-sheet reflects the process of transforming the value of operational resources into their selling price, and the forecast operating profit is generated in the liability side of the balance-sheet, which relates to assets and liabilities recognized in accounting at the current time. Cost parameter and value index are introduced, which characterize the indicators of income and expenses as the transformation of operational resources. Any change in the cost of resources used and the possible price (value) of their sale is reflected in the balance-sheet and affects the change in the estimate of forecast operating profit in real time. At the same time, due to the simultaneous recognition in the balance-sheet of actual and forecast estimates of assets and liabilities and the indicator of forecast operating profit, the indicator of financial capital receives a new interpretation. If we compare the value of assets and accounts payable, then financial capital characterizes the security of operating activities with own sources of financing in the past. If we compare the selling price of assets and account payable, then financial capital shows the forecast for repayment of account payable at the expense of own funds in the future. Consequently, the transition from actual to forecast estimates in the balance-sheet reveals the process of the circulation of operating capital and shows how much profit is provided by investments in working stocks made in the past. Due to the double recording method, any forecast estimates can be verified by the user, which increases the reliability of the forecast information in the balance-sheet.In fact, the balance-sheet is interpreted as a new method of analysis and forecasting of financial and economic indicators characterizing the activities of the organization. At the same time, it is not necessary to perform additional analytical calculations, forecast operating profit and analysis of its impact on financial capital can be carried out in real time as often as accounting entries are made that affect the change in working capital.

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First month of the epidemic caused by COVID-19 in Italy: current status and real-time outbreak development forecast

10.21203/rs.3.rs-20456/v2 ◽

2020 ◽

Author(s):

Rosario Megna

Keyword(s):

Real Time ◽

Forecast Model ◽

Current Status ◽

Logistic Distribution ◽

Infected People ◽

The North ◽

Heavy Burden ◽

Novel Coronavirus ◽

Health Organization ◽

Collaborative Efforts

Abstract Background : The first outbreaks of COVID-19 in Italy occurred during the second half of February 2020 in some areas in the North of the country. Due to the high contagiousness of the infection, further spread by asymptomatic people, Italy has become in a few weeks the country with the greatest number of infected people in the World. The large number of severe cases among infected people in Italy led to the hospitalization of thousands of patients, with a heavy burden on the National Health Service. Methods: We analyzed data provided daily by Italian Authorities for the period from 24 February 2020 to 30 March 2020. Considering such information, we developed a forecast model in real-time, based on the cumulative logistic distribution. We then produced an estimate of the overall number of potentially infected individuals and epidemic duration at a national and Regional level, for the most affected Regions. Results: A total of 101,739 infected individuals was confirmed until 30 March 2020, of which 75,528 active cases, 14,620 recovered or discharged, and 11,591 deaths. Until the same date patients quarantined at home were 43,752, whereas hospitalized patients were 31,776, of which 3,981 in intensive care. The forecast model estimated a number of infected persons for Italy of 130,000 about, and a duration of the epidemic greater than 2 months. Conclusions : Once month after the first outbreaks there seems to be the first signs of a decrease in the number of infections, showing that we could be now facing the descending phase of the epidemic. The forecast obtained thanks to our model could be used by decision-makers to implement coordinative and collaborative efforts in order to control the epidemic. The pandemic due to novel Coronavirus must be a warning for all countries worldwide, regarding a rapid and complete dissemination of information, surveillance, health organization, and cooperation among the states.

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