Simulation model of Lake Como that can be combined with the Borg Multi-Objective Evolutionary Algorithm or another MOEA implemented in the MOEA Framework to design a set of Pareto optimal operating policies via Evolutionary Multi-Objective Direct Policy Search policy design.
Lake Como is a sub-alpine lake in the Italian lake district, northern Italy. It is the third-largest lake in Italy and reaches a maximum depth of 410 m, which makes it the fifth-deepest lake in Europe. The lake is shaped like an inverted 'Y' surrounded by mountains and primarily fed by the Adda River, which also serves as the only exit point of the lake. The lake catchment has an area of 4,733 km2, with approximately 90% in Italy and 10% in Switzerland. The hydrologic regime is snow-rainfall dominated, characterized by relatively dry winters and summers and high peaks of flow in spring and autumn, due to snowmelt and rainfall, respectively. The Lake Como system involves numerous economic activities and has been actively studied since the 1980s. The existing regulation of the lake is driven by two primary, competing objectives: water supply mainly for irrigation, and flood control in the city of Como, which sits at the lowest point on the lake shoreline. These two objectives (both to be minimized) are formulated as follows:
- Flood control: the average annual number of flooding days in the simulation horizon.
- Water supply deficit: the daily average quadratic water deficit between lake releases and downstream water demands, subject to a minimum environmental flow constraint to ensure adequate environmental conditions in the Adda River.
To compile and run:
- Run
makein the test folder to compile - Run
./LakeComoSim settings_lakeComoEMODPS.txt < ../data/u_test46.txtto perform a simulation with a random policy using forecast information - Run
./LakeComoSim settings_lakeComoHistory.txt < ../data/u_32h.txtto perform a simulation with policy approximating the historcal regulation of the lake
The simulation is performed using a 1 year trajectory of inflow data simulated by a hydrologic model; the mass balance equation of the lake storage is integrated over time with a hourly time step; the daily release decision is determined by a Gaussian Radial Basis Function with 3 or 4 inputs (i.e. sine and cosine of the day of the year, lake level, and possibly a forecast of the cumulated inflows over the next 51 days according to the results of Denaro et al. 2017), 1 output, and 4 or 5 Gaussian bases, for a total of 32 or 46 policy parameters (decision variables). See the settings files in the test folder for further details. The observed inflows used in the paper are from Consorzio dell'Adda.
References:
Giuliani, M., M. Zaniolo, A. Castelletti, G. Davoli, and P. Block (2019), Detecting the state of the climate system via artificial intelligence to improve seasonal forecasts and inform reservoir operations, Water Resources Research, 55 - The full set of results are available in the paper branch.
Giuliani, M., L. Crochemore, I. Pechlivanidis, and A. Castelletti (2020), From skill to value: isolating the influence of end-user behaviour on seasonal forecast assessment, Hydrology and Earth System Sciences, 24 - The full set of results are available in the paper branch.
Copyright:
Copyright 2021 Environmental Intelligence Lab - Politecnico di Milano.
Developers: Matteo Giuliani and Andrea Castelletti.
LakeComo is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
The code is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with LakeComo. If not, see http://www.gnu.org/licenses/licenses.en.html.