Abstract
Management and analyses of water resources is of paramount importance in the implementation of water related sustainable development goals. Hydraulic models are key in flood forecasting and simulation applied to a river flood analysis and risk prediction and an accurate estimation of the roughness is one of the main factors in predicting the discharge in a stream. In practical implementation roughness can be represented by the prediction of the well known Manning’s coefficient necessary for discharge calculation. In this paper we design an objective function that measures the quality of a given configuration of the Manning’s coefficient. Such an objective function is optimised through several evolutionary approaches, namely: (1+1)-ES, CMA-ES, Differential Evolution, Particle Swarm Optimization and Bayesian Optimization. As case of study, a river in the central Italy was considered. The results indicate that the model, consistent with the classical techniques adopted in the hydraulic engineering field, is applicable to natural rivers and is able to provide an estimation of the roughness coefficients with a satisfactory accuracy. A comparison of the performances of the five evolutionary algorithms is also proposed.
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Acknowledgement
This work was partially supported by the following research grants: (i) Università per Stranieri di Perugia – Progetto di ricerca Artificial Intelligence for Education, Social and Human Sciences; (ii) Università per Stranieri di Perugia – Finanziamento per Progetti di Ricerca di Ateneo—PRA 2020; (iii) PRIN project PHRAME: Phraseological Complexity Measures in learner Italian.
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Agresta, A., Baioletti, M., Biscarini, C., Milani, A., Santucci, V. (2021). Evolutionary Algorithms for Roughness Coefficient Estimation in River Flow Analyses. In: Castillo, P.A., Jiménez Laredo, J.L. (eds) Applications of Evolutionary Computation. EvoApplications 2021. Lecture Notes in Computer Science(), vol 12694. Springer, Cham. https://doi.org/10.1007/978-3-030-72699-7_50
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