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Krzysztof Drachal and Michal Pawlowski
This study firstly applied a Bayesian symbolic regression (BSR) to the forecasting of numerous commodities? prices (spot-based ones). Moreover, some features and an initial specification of the parameters of the BSR were analysed. The conventional approa...
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Yongqi Liu, Guibing Hou, Baohua Wang, Yang Xu, Rui Tian, Tao Wang and Hui Qin
Flood control operation of cascade reservoirs is an important technology to reduce flood disasters and increase economic benefits. Flood forecast information can help reservoir managers make better use of flood resources and reduce flood risks. In this p...
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Svetlana S. Uvarova, Svetlana V. Belyaeva, Alexandr K. Orlov and Vadim S. Kankhva
Most large construction projects face the problem of cost overruns and failures to meet deadlines mainly due to changes in the cost of building materials. A lot of studies proved the high importance of the cost of building materials for the project budge...
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Gonca Gürses-Tran and Antonello Monti
Forecast developers predominantly assess residuals and error statistics when tuning the targeted model?s quality. With that, eventual cost or rewards of the underlying business application are typically not considered in the model development phase. The ...
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Ályson Brayner Sousa Estácio,Samiria Maria Oliveira da Silva,Francisco de Assis Souza Filho
Droughts affect basic human activities, and food and industry production. An adequate drought forecasting is crucial to guarantee the survival of population and promote societal development. The Standard Precipitation Index (SPI) is recommended by the Wo...
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Mengning Wu, Christos Stefanakos and Zhen Gao
Short-term wave forecasts are essential for the execution of marine operations. In this paper, an efficient and reliable physics-based machine learning (PBML) model is proposed to realize the multi-step-ahead forecasting of wave conditions (e.g., signifi...
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Wanle Wang, Ming Zhong, Yiming Zhang, Yaqiu Li, Jing Ge, John Douglas Hunt, John Edward Abraham
Pág. 93 - 112
A precise and stable microsimulation space development module is fundamental for supporting various policy decision-making exercises related to land development. This paper studies the dynamics or uncertainty of outputs of the parcel-based space developm...
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Fi-John Chang and Shenglian Guo
The impacts of climate change on water resources management as well as the increasing severe natural disasters over the last decades have caught global attention. Reliable and accurate hydrological forecasts are essential for efficient water resources ma...
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Ashkan Zarnani, Soheila Karimi and Petr Musilek
Information about forecast uncertainty is vital for optimal decision making in many domains that use weather forecasts. However, it is not available in the immediate output of deterministic numerical weather prediction systems. In this paper, we investig...
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Vasilios Plakandaras, Periklis Gogas and Theophilos Papadimitriou
An important ingredient in economic policy planning both in the public or the private sector is risk management. In economics and finance, risk manifests through many forms and it is subject to the sector that it entails (financial, fiscal, international...
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Vasilios Plakandaras, Periklis Gogas and Theophilos Papadimitriou
An important ingredient in economic policy planning both in the public or the private sector is risk management. In economics and finance, risk manifests through many forms and it is subject to the sector that it entails (financial, fiscal, international...
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Amos O. Anele, Ezio Todini, Yskandar Hamam and Adnan M. Abu-Mahfouz
In a previous paper, a number of potential models for short-term water demand (STWD) prediction have been analysed to find the ones with the best fit. The results obtained in Anele et al. (2017) showed that hybrid models may be considered as the accurate...
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Ye Tian, Yue-Ping Xu, Zongliang Yang, Guoqing Wang and Qian Zhu
This study applied a GR4J model in the Xiangjiang and Qujiang River basins for rainfall-runoff simulation. Four recurrent neural networks (RNNs)?the Elman recurrent neural network (ERNN), echo state network (ESN), nonlinear autoregressive exogenous input...
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Mawuli Segnon, Stelios Bekiros and Bernd Wilfling
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Silvia Barbetta, Gabriele Coccia, Tommaso Moramarco, Ezio Todini
Pág. 1 - 25
This work presents the application of the multi-temporal approach of the Model Conditional Processor (MCP-MT) for predictive uncertainty (PU) estimation in the Godavari River basin, India. MCP-MT is developed for making probabilistic Bayesian decision. I...
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Silvia Barbetta, Gabriele Coccia, Tommaso Moramarco and Ezio Todini
This work presents the application of the multi-temporal approach of the Model Conditional Processor (MCP-MT) for predictive uncertainty (PU) estimation in the Godavari River basin, India. MCP-MT is developed for making probabilistic Bayesian decision. I...
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Erich J. Plate and Khurram M. Shahzad
This paper demonstrates, by means of a systematic uncertainty analysis, that the use of outputs from more than one model can significantly improve conditional forecasts of discharges or water stages, provided the models are structurally different. Discha...
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Hana Sevcikova, Mark Simonson, Michael Jensen
Uncertainty in land use and transportation modeling has received increasing attention in the past few years. However, methods for quantifying uncertainty in such models are usually developed in an academic environment and in most cases do not reach users...
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Rangley C. Mickey, Patricia S. Dalyander, Robert McCall and Davina L. Passeri
Antecedent topography is an important aspect of coastal morphology when studying and forecasting coastal change hazards. The uncertainty in morphologic response of storm-impact models and their use in short-term hazard forecasting and decadal forecasting...
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Nicolas Akil, Guillaume Artigue, Michaël Savary, Anne Johannet and Marc Vinches
Neural networks are used to forecast hydrogeological risks, such as droughts and floods. However, uncertainties generated by these models are difficult to assess, possibly leading to a low use of these solutions by water managers. These uncertainties are...
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