Feasibility study on operational use of neural networks in a flash flood early warning system

Lima, Glauston Roberto Teixeira de; Scofield, Graziela Balda

doi:10.1590/2318-0331.262120200152

Cited by 6 publications

(2 citation statements)

References 21 publications

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“…Previously, researchers employed techniques processing the statistics accumulated using various methods (regression, approximative, and others [27][28][29][30][31]). Widespread adoption of neural networks and machine learning contributed to their use in many works to predict runoff (for example, [32][33][34][35][36][37][38][39][40]). This paper presents one of the approaches to calculating predictive estimates of effective water inflow into a lake and flow rates of rivers with the aid of the GeoGIPSAR system and neural network.…”

Section: Initial Data Organization Of Monitoring Of the Atmospheric State Datamentioning

confidence: 99%

Monitoring and Predictive Estimations of Atmospheric Parameters in the Catchment Area of Lake Baikal

et al. 2021

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The paper is concerned with a methodological approach to monitoring the state of atmospheric parameters in the catchment area of Lake Baikal, including real-time analysis of actual distributed data with the determination of analog years according to the preset proximity of comparative indicators and the most probable long-term predictive distributions of surface temperatures, precipitation, pressure, and geopotential with a lead time of up to 9–12 months. We have developed the information-analytical system GeoGIPSAR to conduct the real-time analysis of spatial and point data by various processing methods and obtain long-term prognostic estimates of water inflow into the lake.

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Section: Initial Data Organization Of Monitoring Of the Atmospheric State Datamentioning

confidence: 99%

Monitoring and Predictive Estimations of Atmospheric Parameters in the Catchment Area of Lake Baikal

et al. 2021

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“…Reis (2011) used ANNs to predict silo pressures; this study served as a basis for the present study Lima (2021). used ANNs to develop an early warning system to predict flash floods Araújo, Dantas Neto and Souza Filho (2015).…”

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confidence: 99%

Use of artificial neural networks in the prediction of horizontal and friction pressures in a slender silo

Mancini¹,

Tavares²,

Gomes³

et al. 2023

Braz. J. Develop.

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Structural reliability studies in silo pressure predictions are a subject of great interest because the number of variables in silo pressure calculations is large, which can generate numerous failures and occurrences of collapse. The objective of this study was to propose a pressure prediction model using artificial neural networks (ANNs) in comparison with experimental data. For data collection, a pilot silo model, proposed by Pieper and Schutz (1980), adopted by the German standard DIN 1055 (1987), was used. The pressure measurements were performed during loading and unloading of the silo. Smooth walls with flat bottom were used in the silo, varying the height/diameter ratio between 4, 6 and 8. The pressures observed on the silo walls were the horizontal pressures and the friction pressure of the product with the wall. The results of the pressures obtained experimentally were fed into an algorithm using multilayer Perceptron ANNs. The pressures measured on the silo walls were compared with the values predicted by ANNs, linear and polynomial regressions, and also with the values calculated by means of the European Standard EN 1991-4: 2006 and Australian Standard AS 3774 (1996). Among the models used, the best performance was obtained by ANNs, which were able to predict silo wall pressures with smaller errors.

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Evaluation of synergy ability and reconstruction of synergy organization for marine disaster monitoring and early warning in coastal cities, China

et al. 2023

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In order to improve the synergy level of marine disaster monitoring and early warning in coastal cities, an evaluation index system of 21 factors with 5 dimensions was constructed based on the synergy theory. The weights of all levels of indicators were determined by coefficient of variation method, and 24 representative coastal cities in China were evaluated by multi-level grey relational method. The evaluation results show that: the level of synergy in each dimension of 24 coastal cities is uneven, and the distribution level is not high. The level of synergy of marine disaster monitoring and early warning in coastal cities is positively correlated with the level of economic development. The level of synergy of monitoring and early warning of marine disasters in 24 coastal cities is not high, which is generally distributed in medium and low levels. Furthermore, according to the evaluation results and the current situation of marine disaster monitoring and early warning in coastal cities, a network organization structure is proposed to enhance the synergy ability of coastal city monitoring and early warning, so as to provide practical basis for the construction of China's coastal cities in the global marine disaster monitoring and early warning synergy.

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Feasibility study on operational use of neural networks in a flash flood early warning system

Cited by 6 publications

References 21 publications

Monitoring and Predictive Estimations of Atmospheric Parameters in the Catchment Area of Lake Baikal

Monitoring and Predictive Estimations of Atmospheric Parameters in the Catchment Area of Lake Baikal

Use of artificial neural networks in the prediction of horizontal and friction pressures in a slender silo

Evaluation of synergy ability and reconstruction of synergy organization for marine disaster monitoring and early warning in coastal cities, China

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