# Qom--A New Hydroologic Prediction Model Enhanced with Multi-Objective Optimization
## 7 Conclusions  
In this work, a novel hydrologic model called Qom is proposed to efficiently separate and quantify the volumes of losses and excesses of the rainwater falling in a hydrographic basin. In combination with it, an evolutionary multi-objective approach is used for parameter calibration focusing on indicators to qualify the adjustments between the observed and estimated hydrograms. A number of multi-objective algorithms in the scope of the jMetal framework are used to deal with the hydrologic model as a black-box optimization problem, with a vector of decision variables as inputs and objective function values as outputs. The Qom model, featuring a reduced set of parameters, together with the possibility of using existing multi-objective optimizers without the need to tune each of them, just combining the solutions they produce, make this proposal of special interest for both academy and industry communities. For testing purposes, six realistic hydrographic scenarios, comprising river basins located in Spain, USA, Brazil and Argentina, have been modeled and evaluated with different size conditions, climates, topographies, heterogeneous soils, and with series of very short time periods of 10, 15, 30 and 60 min and 24 h. The results indicate that the calibration of Qom of each problem allow to predict future storm flows as it is possible of accurately reproduce the phenomenon rain-runoff (cause-effect), thus fulfilling the goal of our work. In general, the NSE and PBIAS obtained by Qom involved highly successful values of roughly 75% and 10% (respectively) in calibration, as well as in validation. This could be explained in terms of low errors of the predicted series with regards to observed ones, which can be classified as very satisfactory according to Reference [39]. Moreover, these results could even be improved if larger time periods of registration are considered, as we measure changing conditions from 10 min to 24 h, whereas references in the literature worked within monthly periods [39]. As lines of future work, an open issue to explore is the analysis of the Qom model when compared with other hydrological models, such as SWAT, VIC or SAC-SMA. Our proposal to combine different multi-objective metaheuristics can be time consuming, as a large number of independent runs of configurations of the algorithm/problem must be carried out. It would be more efficient to explore the use of automatic parameter tuning tools to determine if a single algorithm could be good enough. In this sense, a further step would be to self-generate an ad-hoc metaheuristic suited for the calibration of Qom. In both cases, the idea would always be to use automatic tools to avoid the expert to deal with algorithmic issues. The performance of the resulting method would be validated by comparing it with other parameter estimation techniques. [PERSON] has contributed to the conceptualization, methodology, software development and validation; [PERSON] has contributed to methodology, formal analysis, writing--original draft preparation, writing--review, and editing and visualization; [PERSON] has contributed to supervision, software development, writing--review, and editing. All authors have read and agreed to the published version of the manuscript. This work has been partially funded by Grants TIN2017-86049-R (Spanish Ministry of Education and Science). [PERSON] is the recipient of a Post-Doctoral fellowship of \"Captacion de Talento para la Investigacion\" Plan Propio at University of Malaga. The authors declare no conflict of interest. A description of the main features of the studied river basins is included next:  
Asua river basin, Bizkaia, Spain. It is located in the north of the Iberian Peninsula. The climate is temperate, oceanic, with frequent and abundant rainfall, especially in autumn and winter, with an annual average of 1200 mm. It is a rural basin comprising a drainage area equal to 50.8 km\({}^{2}\), corresponding to the Dario (meteorological) and San Groniz (capacity) stations, with slopes not exceeding 1%.