forked from ryanmorrison/bayesian_network
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy path3-scenarios_processing.R
235 lines (207 loc) · 12.8 KB
/
3-scenarios_processing.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
#### CUFA 150 ####
# This scenario does not permit diversion withdrawals in the Gila River when natural flows are less than 150 cfs
# Read data for the scenario
CUFA_150 <- scenario.process("data/scenarios.csv", 3)
head(CUFA_150)
#### Add stage values (feet and cm) to data frame based on a regression equation ####
# The regression equation was developed using historical gage data
CUFA_150_stage <- stage(CUFA_150, head_conv)
CUFA_150 <- data.frame(CUFA_150, CUFA_150_stage)
#### Calculate recession rates ####
# The 1-, 2-, ... , 7-day recession rates were computed
recess_1d_s1 <- recession.rate.forward(CUFA_150, 1)
recess_2d_s1 <- recession.rate.forward(CUFA_150, 2)
recess_3d_s1 <- recession.rate.forward(CUFA_150, 3)
recess_4d_s1 <- recession.rate.forward(CUFA_150, 4)
recess_5d_s1 <- recession.rate.forward(CUFA_150, 5)
recess_6d_s1 <- recession.rate.forward(CUFA_150, 6)
recess_7d_s1 <- recession.rate.forward(CUFA_150, 7)
recess_14d_s1 <- recession.rate.forward(CUFA_150, 14)
recess_30d_s1 <- recession.rate.forward(CUFA_150, 30)
recess_45d_s1 <- recession.rate.forward(CUFA_150, 45)
# CUFA_150 <- cbind(CUFA_150, recess_1d_s1)
# recess_2d_s1 <- as.data.frame(SMA(CUFA_150[,5], 2))
# recess_3d_s1 <- as.data.frame(SMA(CUFA_150[,5], 3))
# recess_4d_s1 <- as.data.frame(SMA(CUFA_150[,5], 4))
# recess_5d_s1 <- as.data.frame(SMA(CUFA_150[,5], 5))
# recess_6d_s1 <- as.data.frame(SMA(CUFA_150[,5], 6))
# recess_7d_s1 <- as.data.frame(SMA(CUFA_150[,5], 7))
# recess_14d_s1 <- as.data.frame(SMA(CUFA_150[,5], 14))
# recess_45d_s1 <- as.data.frame(SMA(CUFA_150[,5], 45))
CUFA_150 <- cbind(CUFA_150, recess_1d_s1, recess_2d_s1, recess_3d_s1, recess_4d_s1, recess_5d_s1, recess_6d_s1, recess_7d_s1, recess_14d_s1, recess_30d_s1, recess_45d_s1)
colnames(CUFA_150)[5:14] <- c("recess_1d", "recess_2d", "recess_3d", "recess_4d", "recess_5d", "recess_6d", "recess_7d", "recess_14d", "recess_30d", "recess_45d")
#### Populate network states based on scenarios ####
# Discrete states (1, 2, 3) based on the timing
timing_state_s1 <- timing.states(CUFA_150)
# Discrete states (Y, N) based on inundation
q1_state_s1 <- q.states(CUFA_150, q_bin, 1)
q2_state_s1 <- q.states(CUFA_150, q_bin, 2)
q3_state_s1 <- q.states(CUFA_150, q_bin, 3)
q4_state_s1 <- q.states(CUFA_150, q_bin, 4)
q5_state_s1 <- q.states(CUFA_150, q_bin, 5)
q6_state_s1 <- q.states(CUFA_150, q_bin, 6)
q7_state_s1 <- q.states(CUFA_150, q_bin, 7)
# Discrete states based on recession rates
recess_state_s1 <- recess.states(CUFA_150, recess_rates, "recess_14d")
# Combine discrete states into single data frame
# q1_all_states_s1 <- cbind(timing_state_s1, q1_state_s1, recess_state_s1)
# q2_all_states_s1 <- cbind(timing_state_s1, q2_state_s1, recess_state_s1)
# q3_all_states_s1 <- cbind(timing_state_s1, q3_state_s1, recess_state_s1)
# q4_all_states_s1 <- cbind(timing_state_s1, q4_state_s1, recess_state_s1)
# q5_all_states_s1 <- cbind(timing_state_s1, q5_state_s1, recess_state_s1)
# q6_all_states_s1 <- cbind(timing_state_s1, q6_state_s1, recess_state_s1)
# # Remove evidence when FLOODING == N or RECESSION == 1 or RECESSION == 5
# q1_trim_states_s1 <- subset(q1_all_states_s1, TIMING != "NA" & FLOOD == "Y")
# q2_trim_states_s1 <- subset(q2_all_states_s1, TIMING != "NA" & FLOOD == "Y")
# q3_trim_states_s1 <- subset(q3_all_states_s1, TIMING != "NA" & FLOOD == "Y")
# q4_trim_states_s1 <- subset(q4_all_states_s1, TIMING != "NA" & FLOOD == "Y")
# q5_trim_states_s1 <- subset(q5_all_states_s1, TIMING != "NA" & FLOOD == "Y")
# q6_trim_states_s1 <- subset(q6_all_states_s1, TIMING != "NA" & FLOOD == "Y")
# q1_trim_states_s1 <- subset(q1_all_states_s1, TIMING != "NA" & FLOOD == "Y" & RECESSION != "1" & RECESSION != "5")
# q2_trim_states_s1 <- subset(q2_all_states_s1, TIMING != "NA" & FLOOD == "Y" & RECESSION != "1" & RECESSION != "5")
# q3_trim_states_s1 <- subset(q3_all_states_s1, TIMING != "NA" & FLOOD == "Y" & RECESSION != "1" & RECESSION != "5")
# q4_trim_states_s1 <- subset(q4_all_states_s1, TIMING != "NA" & FLOOD == "Y" & RECESSION != "1" & RECESSION != "5")
# q5_trim_states_s1 <- subset(q5_all_states_s1, TIMING != "NA" & FLOOD == "Y" & RECESSION != "1" & RECESSION != "5")
# q6_trim_states_s1 <- subset(q6_all_states_s1, TIMING != "NA" & FLOOD == "Y" & RECESSION != "1" & RECESSION != "5")
# all_trim_states_s1 <- list(q1_trim_states_s1, q2_trim_states_s1, q3_trim_states_s1, q4_trim_states_s1, q5_trim_states_s1, q6_trim_states_s1)
#### CUFA no minimum ####
# This scenario permits diversion withdrawals in the Gila River during all discharges
# Read data for the scenario
CUFA_nomin <- scenario.process("data/scenarios.csv", 4)
head(CUFA_nomin)
#### Add stage values (feet and cm) to data frame based on a regression equation ####
# The regression equation was developed using historical gage data
CUFA_nomin_stage <- stage(CUFA_nomin, head_conv)
CUFA_nomin <- data.frame(CUFA_nomin, CUFA_nomin_stage)
#### Calculate recession rates ####
# The 1-, 2-, ... , 7-day recession rates were computed
recess_1d_s2 <- recession.rate.forward(CUFA_nomin, 1)
recess_2d_s2 <- recession.rate.forward(CUFA_nomin, 2)
recess_3d_s2 <- recession.rate.forward(CUFA_nomin, 3)
recess_4d_s2 <- recession.rate.forward(CUFA_nomin, 4)
recess_5d_s2 <- recession.rate.forward(CUFA_nomin, 5)
recess_6d_s2 <- recession.rate.forward(CUFA_nomin, 6)
recess_7d_s2 <- recession.rate.forward(CUFA_nomin, 7)
recess_14d_s2 <- recession.rate.forward(CUFA_nomin, 14)
recess_30d_s2 <- recession.rate.forward(CUFA_nomin, 30)
recess_45d_s2 <- recession.rate.forward(CUFA_nomin, 45)
# CUFA_nomin <- cbind(CUFA_nomin, recess_1d_s2)
# recess_2d_s2 <- as.data.frame(SMA(CUFA_nomin[,5], 2))
# recess_3d_s2 <- as.data.frame(SMA(CUFA_nomin[,5], 3))
# recess_4d_s2 <- as.data.frame(SMA(CUFA_nomin[,5], 4))
# recess_5d_s2 <- as.data.frame(SMA(CUFA_nomin[,5], 5))
# recess_6d_s2 <- as.data.frame(SMA(CUFA_nomin[,5], 6))
# recess_7d_s2 <- as.data.frame(SMA(CUFA_nomin[,5], 7))
# recess_14d_s2 <- as.data.frame(SMA(CUFA_nomin[,5], 14))
# recess_45d_s2 <- as.data.frame(SMA(CUFA_nomin[,5], 45))
CUFA_nomin <- cbind(CUFA_nomin, recess_1d_s2, recess_2d_s2, recess_3d_s2, recess_4d_s2, recess_5d_s2, recess_6d_s2, recess_7d_s2, recess_14d_s2, recess_30d_s2, recess_45d_s2)
colnames(CUFA_nomin)[5:14] <- c("recess_1d", "recess_2d", "recess_3d", "recess_4d", "recess_5d", "recess_6d", "recess_7d", "recess_14d", "recess_30d", "recess_45d")
#### Populate network states based on scenarios ####
# Discrete states (1, 2, 3) based on the timing
timing_state_s2 <- timing.states(CUFA_nomin)
# Discrete states (Y, N) based on inundation
q1_state_s2 <- q.states(CUFA_nomin, q_bin, 1)
q2_state_s2 <- q.states(CUFA_nomin, q_bin, 2)
q3_state_s2 <- q.states(CUFA_nomin, q_bin, 3)
q4_state_s2 <- q.states(CUFA_nomin, q_bin, 4)
q5_state_s2 <- q.states(CUFA_nomin, q_bin, 5)
q6_state_s2 <- q.states(CUFA_nomin, q_bin, 6)
q7_state_s2 <- q.states(CUFA_nomin, q_bin, 7)
# Discrete states based on recession rates
recess_state_s2 <- recess.states(CUFA_nomin, recess_rates, "recess_14d")
# Combine discrete states into single data frame
# q1_all_states_s2 <- cbind(timing_state_s2, q1_state_s2, recess_state_s2)
# q2_all_states_s2 <- cbind(timing_state_s2, q2_state_s2, recess_state_s2)
# q3_all_states_s2 <- cbind(timing_state_s2, q3_state_s2, recess_state_s2)
# q4_all_states_s2 <- cbind(timing_state_s2, q4_state_s2, recess_state_s2)
# q5_all_states_s2 <- cbind(timing_state_s2, q5_state_s2, recess_state_s2)
# q6_all_states_s2 <- cbind(timing_state_s2, q6_state_s2, recess_state_s2)
# # Remove evidence when FLOODING == N or RECESSION == 1 or RECESSION == 5
# q1_trim_states_s2 <- subset(q1_all_states_s2, TIMING != "NA" & FLOOD == "Y")
# q2_trim_states_s2 <- subset(q2_all_states_s2, TIMING != "NA" & FLOOD == "Y")
# q3_trim_states_s2 <- subset(q3_all_states_s2, TIMING != "NA" & FLOOD == "Y")
# q4_trim_states_s2 <- subset(q4_all_states_s2, TIMING != "NA" & FLOOD == "Y")
# q5_trim_states_s2 <- subset(q5_all_states_s2, TIMING != "NA" & FLOOD == "Y")
# q6_trim_states_s2 <- subset(q6_all_states_s2, TIMING != "NA" & FLOOD == "Y")
# q1_trim_states_s2 <- subset(q1_all_states_s2, TIMING != "NA" & FLOOD == "Y" & RECESSION != "1" & RECESSION != "5")
# q2_trim_states_s2 <- subset(q2_all_states_s2, TIMING != "NA" & FLOOD == "Y" & RECESSION != "1" & RECESSION != "5")
# q3_trim_states_s2 <- subset(q3_all_states_s2, TIMING != "NA" & FLOOD == "Y" & RECESSION != "1" & RECESSION != "5")
# q4_trim_states_s2 <- subset(q4_all_states_s2, TIMING != "NA" & FLOOD == "Y" & RECESSION != "1" & RECESSION != "5")
# q5_trim_states_s2 <- subset(q5_all_states_s2, TIMING != "NA" & FLOOD == "Y" & RECESSION != "1" & RECESSION != "5")
# q6_trim_states_s2 <- subset(q6_all_states_s2, TIMING != "NA" & FLOOD == "Y" & RECESSION != "1" & RECESSION != "5")
# all_trim_states_s2 <- list(q1_trim_states_s2, q2_trim_states_s2, q3_trim_states_s2, q4_trim_states_s2, q5_trim_states_s2, q6_trim_states_s2)
# c(all_trim_states, all_trim_states_s1, all_trim_states_s2)
# #### San Juaquin Post-dam ####
#
# # Read data for the scenario
# postdam <- usgs.process("data/usgs_11251000_daily_postdam.txt")
# head(postdam)
#
# #### Add stage values (feet and cm) to data frame based on a regression equation ####
# # The regression equation was developed using historical gage data
# postdam_stage <- stage(postdam, head_conv)
# postdam <- data.frame(postdam, postdam_stage)
#
# #### Calculate recession rates ####
# # The 1-, 2-, ... , 7-day recession rates were computed
# recess_1d_pd <- recession.rate(postdam, 1)
# # recess_2d_pd <- recession.rate(postdam, 2)
# # recess_3d_pd <- recession.rate(postdam, 3)
# # recess_4d_pd <- recession.rate(postdam, 4)
# # recess_5d_pd <- recession.rate(postdam, 5)
# # recess_6d_pd <- recession.rate(postdam, 6)
# # recess_7d_pd <- recession.rate(postdam, 7)
# # recess_14d_pd <- recession.rate(postdam, 14)
#
# postdam <- cbind(postdam, recess_1d_pd)
# recess_2d_pd <- as.data.frame(SMA(postdam[,5], 2))
# recess_3d_pd <- as.data.frame(SMA(postdam[,5], 3))
# recess_4d_pd <- as.data.frame(SMA(postdam[,5], 4))
# recess_5d_pd <- as.data.frame(SMA(postdam[,5], 5))
# recess_6d_pd <- as.data.frame(SMA(postdam[,5], 6))
# recess_7d_pd <- as.data.frame(SMA(postdam[,5], 7))
# recess_14d_pd <- as.data.frame(SMA(postdam[,5], 14))
# recess_45d_pd <- as.data.frame(SMA(postdam[,5], 45))
#
# postdam <- cbind(postdam, recess_2d_pd, recess_3d_pd, recess_4d_pd, recess_5d_pd, recess_6d_pd, recess_7d_pd, recess_14d_pd, recess_45d_pd)
# colnames(postdam)[6:13] <- c("recess_2d", "recess_3d", "recess_4d", "recess_5d", "recess_6d", "recess_7d", "recess_14d", "recess_45d")
#
# #### Populate network states based on scenarios ####
# # Discrete states (1, 2, 3) based on the timing
# timing_state_pd <- timing.states(postdam)
#
# # Discrete states (Y, N) based on inundation
# q1_state_pd <- q.states(postdam, q_bin, 1)
# q2_state_pd <- q.states(postdam, q_bin, 2)
# q3_state_pd <- q.states(postdam, q_bin, 3)
# q4_state_pd <- q.states(postdam, q_bin, 4)
# q5_state_pd <- q.states(postdam, q_bin, 5)
# q6_state_pd <- q.states(postdam, q_bin, 6)
#
# # Discrete states based on recession rates
# recess_state_pd <- recess.states(postdam, recess_rates, "recess_7d")
#
# # Combine discrete states into single data frame
# # q1_all_states_pd <- cbind(timing_state_pd, q1_state_pd, recess_state_pd)
# # q2_all_states_pd <- cbind(timing_state_pd, q2_state_pd, recess_state_pd)
# # q3_all_states_pd <- cbind(timing_state_pd, q3_state_pd, recess_state_pd)
# # q4_all_states_pd <- cbind(timing_state_pd, q4_state_pd, recess_state_pd)
# # q5_all_states_pd <- cbind(timing_state_pd, q5_state_pd, recess_state_pd)
# # q6_all_states_pd <- cbind(timing_state_pd, q6_state_pd, recess_state_pd)
#
# # # Remove evidence when FLOODING == N or RECESSION == 1 or RECESSION == 5
# # q1_trim_states_pd <- subset(q1_all_states_pd, TIMING != "NA" & FLOOD == "Y")
# # q2_trim_states_pd <- subset(q2_all_states_pd, TIMING != "NA" & FLOOD == "Y")
# # q3_trim_states_pd <- subset(q3_all_states_pd, TIMING != "NA" & FLOOD == "Y")
# # q4_trim_states_pd <- subset(q4_all_states_pd, TIMING != "NA" & FLOOD == "Y")
# # q5_trim_states_pd <- subset(q5_all_states_pd, TIMING != "NA" & FLOOD == "Y")
# # q6_trim_states_pd <- subset(q6_all_states_pd, TIMING != "NA" & FLOOD == "Y")
#
# # q1_trim_states_pd <- subset(q1_all_states_pd, TIMING != "NA" & FLOOD == "Y" & RECESSION != "1" & RECESSION != "5")
# # q2_trim_states_pd <- subset(q2_all_states_pd, TIMING != "NA" & FLOOD == "Y" & RECESSION != "1" & RECESSION != "5")
# # q3_trim_states_pd <- subset(q3_all_states_pd, TIMING != "NA" & FLOOD == "Y" & RECESSION != "1" & RECESSION != "5")
# # q4_trim_states_pd <- subset(q4_all_states_pd, TIMING != "NA" & FLOOD == "Y" & RECESSION != "1" & RECESSION != "5")
# # q5_trim_states_pd <- subset(q5_all_states_pd, TIMING != "NA" & FLOOD == "Y" & RECESSION != "1" & RECESSION != "5")
# # q6_trim_states_pd <- subset(q6_all_states_pd, TIMING != "NA" & FLOOD == "Y" & RECESSION != "1" & RECESSION != "5")
#
# # all_trim_states_pd <- list(q1_trim_states_pd, q2_trim_states_pd, q3_trim_states_pd, q4_trim_states_pd, q5_trim_states_pd, q6_trim_states_pd)