Complexity to Forecast Flood: Problem Definition and Spatiotemporal Attention LSTM Solution
Algorithm 1
Pseudocode of STA-LSTM for implementation.
Input: Input dataset X with n samples, each sample refers to a hydrological feature set collected from total τ states, input initial super-parameters θ.
Output: Run-off value Prediction Y.
(1)
Initialize STA-LSTM model with initial super-parameters θ and random network weights W1.
(2)
Set sample index i = 1;
(3)
for i ≤ n do
(4)
Set current state t = 2, hidden output in the first state , weighted hidden output in the first state ;
(5)
for t ≤ τ do
(6)
Input into t − 1th spatial attention module to calculate spatial attention weight by ;
(7)
Refine current input with spatial attention weight αt by ;
(8)
Input and into tth LSTM cell to compute current hidden output by ;
(9)
Input and into t − 1th temporal attention module to compute temporal attention weight by ;
(10)
Refine current hidden output with spatial attention weight βt by .
(11)
t ++;
(12)
end for
(13)
Increase or Decrease value in Run-off Yi = Outlayer(, …, )
(14)
Calculate RMSE with Yi and ground-truth value.
(15)
if RMSE decreases then
(16)
Update model weights with: ;
(17)
else
(18)
Continue;
(19)
end if
(20)
i ++;
(21)
end for
(22)
End training process and Save model parameters.
(23)
Input testing samples to output corresponding prediction value Y.
