#Forecast future runoff based on meteorological data and historical runoff
Streamflow=pd.read_csv('###.csv', delimiter=',')
x = Streamflow.drop('Q',axis=1)
Y = Streamflow['Q']
X = np.array(x)
y = np.array(Y)
test_size = int(len(X) * 0.15)
valid_size = int(len(X) * 0.15)
train_size= len(X) - (valid_size+test_size)
y_train, y_valid, y_test = y[0:train_size], y[train_size:train_size+valid_size], y[-test_size:]
X_train, X_valid, X_test = X[0:train_size], X[train_size:train_size+valid_size], X[train_size+valid_size:]
X_train = np.reshape(X_train, (X_train.shape[0], 1, X_train.shape[1]))
X_valid = np.reshape(X_valid, (X_valid.shape[0], 1, X_valid.shape[1]))
X_test = np.reshape(X_test, (X_test.shape[0], 1, X_test.shape[1]))
input=X_train[1:]
input_shape=X_train.shape[1:]
print (y_train.shape, y_valid.shape, y_test.shape)
model = Sequential()
model.add(LSTM(150, input_shape=X_train.shape[1:], activation='relu',return_sequences=True))
model.add(Dropout(0.3))
model.add(LSTM(300, activation='relu'))
model.add(Dropout(0.3))
model.add(Dense(200, activation='relu'))
model.add(Dropout(0.6))
model.add(Dense(1, activation='relu'))
optimizer = tf.keras.optimizers.SGD(learning_rate=e_LR)
model.compile(optimizer=optimizer,loss='MeanAbsoluteError')
history = model.fit(X_train, y_train, epochs=e_epoch, batch_size=e_batch_size, verbose=0, validation_data=(X_valid, y_valid), shuffle=True)
enter image description here This is model loss and the result. The results of the training period and the verification period are acceptable, but the results of the testing period are too poor. How should I modify the model? (The data is not normalized because the normalized prediction is a straight line. )
