connectivity=connectivity,
A_i=A_i,
L_i=L_i,
A_h=A_h)
SSN5.dam_ids = dams
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]
## SCENARIO-4 #############################################################################
SSN5 = Watershed(Model=256)
SSN5.init_custom(links=l_id,
connectivity=connectivity,
A_i=A_i,
L_i=L_i,
A_h=A_h)
SSN5.dam_ids = dams4
H_spill, H_max, S_max, _alpha, diam, c1, c2, L_spill, L_crest = PrepareDamParams(
dams4)
diam = [1.25 for _ in range(len(dams2))] + diam[4:]
dam_params256 = SSN5.init_dam_params256(H_spill, H_max, S_max, _alpha,
diam, c1, c2, L_spill, L_crest)
dc_passive_Peak_S4 = pd.DataFrame(columns=SSN5.__columns__()[0])
out_passive_Peak_S4 = pd.DataFrame(columns=SSN5.__columns__()[1])
for dstorm in dstorms:
forcing, cum_forcing, forcing_hour = Generate_SyntheticStorm(
dstorm,
24,
rate=(0.01 * np.diff(rate10, prepend=0)).tolist(),
timescale=30)
te = len(forcing) - 1
for key in initial_conditions.keys():
initial_condition = initial_conditions[key]
q = initial_condition['q']
s_p = initial_condition['s_p']
s_t = initial_condition['s_t']
A_h=A_h)
SSN5_r.dam_ids = dams5
H_spill, H_max, S_max, _alpha, diam, c1, c2, L_spill, L_crest = PrepareDamParams(
dams5)
dam_params256 = SSN5_r.init_dam_params256(H_spill, H_max, S_max, _alpha,
diam, c1, c2, L_spill, L_crest)
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)
## SCENARIO-1 #############################################################################
SSN2 = Watershed(Model=256)
SSN2.init_custom(links=l_id,
connectivity=connectivity,
A_i=A_i,
L_i=L_i,
A_h=A_h)
SSN2.dam_ids = dams1
H_spill, H_max, S_max, _alpha, diam, c1, c2, L_spill, L_crest = PrepareDamParams(
dams1)
dam_params256 = SSN2.init_dam_params256(H_spill, H_max, S_max, _alpha,
diam, c1, c2, L_spill, L_crest)
dc_passive_Peak_S1 = pd.DataFrame(columns=SSN2.__columns__()[0])
out_passive_Peak_S1 = pd.DataFrame(columns=SSN2.__columns__()[1])
for dstorm in dstorms:
forcing, cum_forcing, forcing_hour = Generate_SyntheticStorm(
dstorm,
24,
rate=(0.01 * np.diff(rate10, prepend=0)).tolist(),
timescale=30)
te = len(forcing) - 1
for key in initial_conditions.keys():
initial_condition = initial_conditions[key]
q = initial_condition['q']
s_p = initial_condition['s_p']
s_t = initial_condition['s_t']
s_s = initial_condition['s_s']