dam_params256 = SSN5.init_dam_params256(H_spill, H_max, S_max, _alpha,
diam, c1, c2, L_spill, L_crest)
q, s_p, s_t, s_s = Set_InitialConditions(0.5, A_i[0], A_i)
S = [100000 for _ in range(n_dams)]
# continuous simulation
update = 60 #mins
lead_time = 360 # the time window checked if there will be any flooding
lead_time_opt = 60 # the time window used in optimization procedure
t0 = 0
states_all = [(-60, [1 for _ in range(len(dams))])]
columns = SSN5.__columns__()
dc_ga = pd.DataFrame(columns=columns[0])
st_ga = pd.DataFrame(columns=columns[1])
while t0 < te - lead_time:
if t0 != 0:
q, S, s_p, s_t, s_s = SSN5.Get_Snapshot()
#define initial conditions
SSN5.initialize(q=q, S=S, s_p=s_p, s_t=s_t, s_s=s_s)
#check if flooding occurs
data = RunSimulation([
SSN5, [1 for _ in range(n_dams)], t0, forcing, dam_params256,
lead_time
])
flow = data[0]
if flow['8'] > 31.80 / 2 or flow['26'] > 30.40 / 2 or flow[
'62'] > 27.4 / 2 or flow['89'] > 16.33 / 2 or flow[
'170'] > 16.33 / 2:
population = InitialPopulation2(16, n_dams)
q = initial_condition['q']
s_p = initial_condition['s_p']
s_t = initial_condition['s_t']
s_s = initial_condition['s_s']
fill_percent = np.repeat([0.0001], n_dams5)
S = (S_max * fill_percent).tolist()
t0 = 0
update = 30 #mins
columns = SSN5_r.__columns__()
dc_active_S5r = pd.DataFrame(columns=columns[0])
st_active_S5r = pd.DataFrame(columns=columns[1])
out_active_S5r = pd.DataFrame(columns=columns[1])
while t0 < te:
if t0 != 0:
q, S, s_p, s_t, s_s = SSN5_r.Get_Snapshot()
SSN5_r.initialize(q=q, S=S, s_t=s_t, s_p=s_p, s_s=s_s)
SSN5_r.set_dam_state(
states=np.random.choice([0, 0.25, 0.50, 0.75, 1], size=(n_dams5)))
try:
dc_S5, st_S5 = SSN5_r.Run_256([t0, t0 + update], forcing,
dam_params256)
out_S5 = SSN5_r.CalculateOutflow(dam_params256, st_S5)
except IndexError:
pass
dc_active_S5r = dc_active_S5r.append(dc_S5)
st_active_S5r = st_active_S5r.append(st_S5)