Assessing the predictive capability of randomized tree-based ensembles in streamflow modelling

Abstract. Combining randomization methods with ensemble prediction is emerging as an effective option to balance accuracy and computational efficiency in data-driven modelling. In this paper, we investigate the prediction capability of extremely randomized trees (Extra-Trees), in terms of accuracy, explanation ability and computational efficiency, in a streamflow modelling exercise. Extra-Trees are a totally randomized tree-based ensemble method that (i) alleviates the poor generalisation property and tendency to overfitting of traditional standalone decision trees (e.g. CART); (ii) is computationally efficient; and, (iii) allows to infer the relative importance of the input variables, which might help in the ex-post physical interpretation of the model. The Extra-Trees potential is analysed on two real-world case studies – Marina catchment (Singapore) and Canning River (Western Australia) – representing two different morphoclimatic contexts. The evaluation is performed against other tree-based methods (CART and M5) and parametric data-driven approaches (ANNs and multiple linear regression). Results show that Extra-Trees perform comparatively well to the best of the benchmarks (i.e. M5) in both the watersheds, while outperforming the other approaches in terms of computational requirement when adopted on large datasets. In addition, the ranking of the input variable provided can be given a physically meaningful interpretation

Automatic design of basin-specific drought indexes for highly regulated water systems

[EN] Socio-economic costs of drought are progressively increasing worldwide due to undergoing alterations of hydro-meteorological regimes induced by climate change. Although drought management is largely studied in the literature, traditional drought indexes often fail at detecting critical events in highly regulated systems, where natural water availability is conditioned by the operation of water infrastructures such as dams, diversions, and pumping wells. Here, ad hoc index formulations are usually adopted based on empirical combinations of several, supposed-to-be significant, hydro-meteorological variables. These customized formulations, however, while effective in the design basin, can hardly be generalized and transferred to different contexts. In this study, we contribute FRIDA (FRamework for Index-based Drought Analysis), a novel framework for the automatic design of basin-customized drought indexes. In contrast to ad hoc empirical approaches, FRIDA is fully automated, generalizable, and portable across different basins. FRIDA builds an index representing a surrogate of the drought conditions of the basin, computed by combining all the relevant available information about the water circulating in the system identified by means of a feature extraction algorithm. We used the Wrapper for Quasi-Equally Informative Subset Selection (W-QEISS), which features a multi-objective evolutionary algorithm to find Pareto-efficient subsets of variables by maximizing the wrapper accuracy, minimizing the number of selected variables, and optimizing relevance and redundancy of the subset. The preferred variable subset is selected among the efficient solutions and used to formulate the final index according to alternative model structures. We apply FRIDA to the case study of the Jucar river basin (Spain), a drought-prone and highly regulated Mediterranean water resource system, where an advanced drought management plan relying on the formulation of an ad hoc "state index" is used for triggering drought management measures. The state index was constructed empirically with a trial-and-error process begun in the 1980s and finalized in 2007, guided by the experts from the Confederacion Hidrografica del Jucar (CHJ). Our results show that the automated variable selection outcomes align with CHJ's 25-year-long empirical refinement. In addition, the resultant FRIDA index outperforms the official State Index in terms of accuracy in reproducing the target variable and cardinality of the selected inputs set.The work has been partially funded by the European Commission under the IMPREX project belonging to Horizon 2020 framework programme (grant no. 641811). The authors would like to thank the planning office of the Confederacion Hidrografica del Jucar (CHJ) for providing the data used in this study.Zaniolo, M.; Giuliani, M.; Castelletti, A.; Pulido-Velazquez, M. (2018). Automatic design of basin-specific drought indexes for highly regulated water systems. HYDROLOGY AND EARTH SYSTEM SCIENCES. 22(4):2409-2424. https://doi.org/10.5194/hess-22-2409-2018S24092424224AghaKouchak, A.: Recognize anthropogenic drought, Nature, 524, p. 409, 2015a. aAghaKouchak, A.: A multivariate approach for persistence-based drought prediction: Application to the 2010–2011 East Africa drought, J. Hydrol., 526, 127–135, 2015b. aAlcamo, J., Flörke, M., and Märker, M.: Future long-term changes in global water resources driven by socio-economic and climatic changes, Hydrolog. Sci. J., 52, 247–275, 2007. aAndreu, J., Capilla, J., and Sanchís, E.: AQUATOOL, a generalized decision-support system for water-resources planning and operational management, J. 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Soft Body Impact Against Aeronautical Structures

Statistics show that impacts of soft body against aeronautical structure are not so rare events. The damage caused by the impact of hailstones or of birds can sometimes be so heavy to compromise the service life of the vehicle. Companies, research centers and universities are interested in the evaluation of the effects of this kind of events and lots of researching works have been recently developed in this field. In this paper, an overview of the last studies performed at the Laboratory for the Safety in Transports (LaST – Crash Lab.) of Politecnico of Milan are presented throughout experimental tests and numerical finite element models. The validity of the correlation results method to prevent possible heavy consequence caused by these events is shown

A coupled human-natural system to assess the operational value of weather and climate services for agriculture

Recent advances in weather and climate (W&C) services are showing increasing forecast skills over seasonal and longer timescales, potentially providing valuable support in informing decisions in a variety of economic sectors. Quantifying this value, however, might not be straightforward as better forecast quality does not necessarily imply better decisions by the end users, especially when forecasts do not reach their final users, when providers are not trusted, or when forecasts are not appropriately understood. In this study, we contribute an assessment framework to evaluate the operational value of W&C services for informing agricultural practices by complementing traditional forecast quality assessments with a coupled human–natural system behavioural model which reproduces farmers' decisions. This allows a more critical assessment of the forecast value mediated by the end users' perspective, including farmers' risk attitudes and behavioural factors. The application to an agricultural area in northern Italy shows that the quality of state-of-the-art W&C services is still limited in predicting the weather and the crop yield of the incoming agricultural season, with ECMWF annual products simulated by the IFS/HOPE model resulting in the most skillful product in the study area. However, we also show that the accuracy of estimating crop yield and the probability of making optimal decisions are not necessarily linearly correlated, with the overall assessment procedure being strongly impacted by the behavioural attitudes of farmers, which can produce rank reversals in the quantification of the W&C services operational value depending on the different perceptions of risk and uncertainty.ISSN:1027-5606ISSN:1607-793

Contrasting farmers' perception of climate change and climatic data: How (in)consistency supports risk reduction and resilience?

Farmers’ adaptation to climate change is a two-step process that involves perceiving stressors and identifying impacts to respond to variability and changes through specific actions and strategies. Hence, successful adaptation depends on how well changing climate is perceived, either from a ‘bottom-up’ pathway –where farmers observe and identify changes through past experience–, or by using a ‘top-down’ pathway –where changes are identified through climate records. A gap between both pathways tends to be related to farmers’ misperception. For example, as life experiences influence perception, farmers who have been directly affected by extreme climatic events tend to report that the probability of such event happening again is relatively high. Furthermore, as perception is in part a subjective phenomenon, therefore, different farmers in the same locality might construct different perceptions of climate change impacts even though they experience the same weather patterns. Consequently, increased attention has been put on combining the ‘civic science’ of farmers’ perceptions with the ‘formal science’ from meteorological reports to identify the (in)consistency between perceived and observed data and how this affect farmers’ resilience when facing climate change impacts. This contribution provides a review comparing farmers’ perception and climate observations to address a twofold research question: 1) Which extreme events and compound risks are perceived by farmers in contrast with observed data? And 2) How do past experiences and social-learning influence farmers’ resilience and their adaptive capacity? We analyze a portfolio of 147 articles collected from Scopus library catalogue since 2000. The bibliometrics analysis was coupled with the systematic review to 103 articles selected from the original portfolio. Comparison between perceived and observed changes were focus on what was changing (onset, duration or cessation regarding temperature and rainfall patterns) and how it was changing (amount, frequency, intensity or inter-annual variability). Results will be useful for managers, developers, and policymakers of climate adaptation strategies to be more in tune with farmers’ understandings of when and how weather is changing. Furthermore, the review could generate recommendations for the design, formulation, and implementation of adaptation policies that are better tailored to farmers’ perception at local conditions, being more efficient and conducive to risk analysis when facing climate change