Taiao Case 4



We load the required libraries used to train the LSTM

import tensorflow as tf import tensorflow.keras as keras import tensorflow.keras.backend as K from tensorflow.keras.layers import LSTM from tensorflow.keras.layers import Dense from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator import pandas as pd import numpy as np import matplotlib.pyplot as pyplot

The flood prediction dataset has been encapsulated in the packages below and can be loaded using the code below

import taiao.dataset.river as data import taiao.visualization.river as visualiser import taiao.model.river as models

We set the hyper-parameters and load the dataset and create the keras data generator

forecast=12 lookback=288 xTrain = data.x('train',forecast) yTrain = data.y('train',forecast) xTest = data.x('test',forecast) yTest = data.y('test',forecast) trainGen=TimeseriesGenerator(xTrain,yTrain,length=lookback,batch_size=3) testGen=TimeseriesGenerator(xTest,yTest,length=lookback,batch_size=1) featureCount=xTrain.shape[1] depth=2

Here we define the model and train the output

model = models.LSTM(depth,featureCount,lookback, optimizer="adam") history =,validation_data=testGen, epochs=1).history'3DLookBack_3hr_forecast_rmse')
134315/134322 [============================>.] - ETA: 0s - loss: 0.0027

We now evaluate the model

model.evaluate(testGen) trainPredict = model.predict(trainGen) testPredict = model.predict(testGen)
172522/172522 [==============================] - 469s 3ms/step - loss: 9.9911e-04

and plot the output for the flood event of interest

pyplot.plot(yTest[lookback:]) pyplot.plot(testPredict) pyplot.xlim((87800,88500))

We do a dump of the csv file comparing the predicted river level to the actual river level

CsvTemp=np.concatenate([yTest[lookback:].reshape(-1,1),testPredict],axis=1) np.savetxt('3D_Prediction_3hr_rmse.csv',CsvTemp,delimiter=',')