# Qom--A New Hydroologic Prediction Model Enhanced with Multi-Objective Optimization
## 1 Introduction  
The modeling over time of the volumes of rainfall collected in a watershed in terms of losses and excesses, quantifying the accumulation on the surface and the movement by gravity superficially to topographically lower areas is currently an indispensable task in hydrology, since it allows experts to efficiently project water management infrastructures and design ecological strategies. Examples of these applications include the generation of hydroelectric power facilities, water reservoirs for human consumption, agricultural and livestock exploitation, water quality management, and so forth. It can also be used as a base and support for other studies, such as the dissolution of pollutants in a river, the erosion and sedimentation in riverbeds and basins, the impact of urbanization on the increase of the waterproof surface, as well as the formulation of contingency plans for emergencies due to deficiencies (droughs) and excesses of water (floods). However, realistic hydrologic models often require many parameters to be properly tuned to reduce prediction errors with regards to observed scenarios. Finding the most appropriate settings of these parameters can be formulated as a complex optimization problem [1]. This was outlined in early studies [2, 3] and more recently in Reference [4], where the importance of using bio-inspiredmulti-objective approaches was emphasized due to their ability to explore the search space generated for each problem river basin scenario and for several objectives at the same time. The main contribution of this work is the combination of a new hydrologic model--called Qom--with the application of a multi-objective approach to find efficient calibrations of the model when applied to real-world scenarios. The motivation driving us is to provide an efficient tool, able to provide accurate predictions when applied to heterogeneous river basin real scenarios, considering different time periods and climatological conditions. Compared to other models, Qom is featured by requiring a small number of parameter to be tuned--maximum storage water volume on surface, maximum storage volume in the soil and volumetric conductivity coefficient. Qom allows the efficient separation and quantification of the volume of losses and excesses of the rainwater falling in a hydrographic basin. Qom considers antecedent moisture and it is applicable to urban and rural watersheds, for permeable and impermeable soils. In addition, it determines the runoff in three layers (two superficial ones and a groundwater one) and it considers simple and continuous rainfall events for long periods of time of several years, including dry and wet periods with very short time records from several minutes to one day. A [PERSON]'s Clark (1988) model is then used to obtain predicted output hydrograms that include the transit of excesses with the effects of delays and storage. The calibration of Qom is carried out by formulating it as a bi-objective optimization problem, which is solved with the combination of a number of bio-inspired algorithms belonging to metaheuristics, a family of non-exact optimization techniques ([PERSON], 1996). We have taken seven algorithms that are representative of the state-of-the-art, namely NSGAII ([PERSON], 1996), OMOPSO (1996), SMSEMOA (1996), MOEAD (1996), AbYSS (1996), SMPSO (1996) and MOCell (1997). These algorithms are provided by the jMetal framework for multi-objective optimization with metaheuristics ([PERSON], 1997), an open-source project, developed in the Java programming language, and released under an MIT (Massachusetts Institute of Technology) licence. Qom has also been implemented in Java to be integrated into jMetal as a black-box optimization problem, with vectors of decision variables as inputs and objective function values as outputs. The version of jMetal including Qom as well as all the utilities and scripts (most of them implemented in R) we have used in this work, are freely available to the community of interested users.