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A Brief review of flood forecasting techniques and their applications

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TLDR
In this paper, the authors discuss the challenges of forecasting flood forecasting in hydrology and its critical contribution in reducing the cost of reducing the reduction in reduc... and the importance of hydrology due to its critical contributions in reducing costs.
Abstract
Flood forecasting (FF) is one the most challenging and difficult problems in hydrology. However, it is also one of the most important problems in hydrology due to its critical contribution in reduc...

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The Ensemble Kalman Filter: Theoretical formulation and practical implementation

TL;DR: The EnKF has a large user group, and numerous publications have discussed applications and theoretical aspects of it as mentioned in this paper, and also presents new ideas and alternative interpretations which further explain the success of the EnkF.
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Is this a practical approach

TL;DR: The health care system must treat illness, alleviate suffering and disability, and promote health, but the whole system needs to work to improve the health of populations.

Ensemble flood forecasting: A review

TL;DR: The scientific drivers of this shift towards ‘ensemble flood forecasting’ and the literature evidence of the ‘added value’ of flood forecasts based on EPS are reviewed.
Journal Article

Environmental Health: From Global to Local

TL;DR: The book’s 36 chapters contain highly pertinent insights and information on environmental issues that go beyond the usual boundaries of classic environmental health, but the index is very poorly done.
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An ANN-based emulation modelling framework for flood inundation modelling: Application, challenges and future directions

TL;DR: The results show that ANNs can model the time series behaviour of flood inundation and significantly reduce the simulation times required, which facilitates their use in applications requiring fast model response or a large number of model runs.
References
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Book

Fuzzy sets

TL;DR: A separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.
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Fitting Linear Mixed-Effects Models Using lme4

TL;DR: In this article, a model is described in an lmer call by a formula, in this case including both fixed-and random-effects terms, and the formula and data together determine a numerical representation of the model from which the profiled deviance or the profeatured REML criterion can be evaluated as a function of some of model parameters.
Journal ArticleDOI

Time series analysis, forecasting and control

TL;DR: Time series analysis san francisco state university, 6 4 introduction to time series analysis, box and jenkins time seriesAnalysis forecasting and, th15 weeks citation classic eugene garfield, proc arima references 9 3 sas support, time series Analysis forecasting and control pambudi, timeseries analysis forecasting and Control george e.