FORECASTING OF CHANGES IN SALINITYINTRUSION IN THE VIETNAMESEMEKONG DELTA BY THE COMBINEDMODEL OF LSTM (Long Short-Term Memory)AND SRM (Sinusoidal Regression Model)
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Abstract
The salinity intrusion in the Vietnamese Mekong Delta (VMD) has become
more complex and temporally heterogeneous. This could seriously threaten the
livelihoods of local residents and agricultural activities. Therefore, the research
was conducted by using a combined model of LSTM (Long Short-Term Mem-
ory) and SRM (Sinusoidal Regression Model) to assess the trends and anomalies
of salinity intrusion, with a series of data collected from main stations in the VMD
in the year of 2021. The findings showed that the combined model exhibited high
predictive (
R
2
= 0
.
9299
, MSE
= 2
.
0861
, and
MAP E
= 0
.
1276
) in fore-
casting the increasing and decreasing trends of salinity intrusion and effectively
detecting anomalous variations. Consequently, these results could be helpful to
policymakers in predicting and responding to future salinity intrusion and to likely
widespread implications for other regions impacted by saline intrusion
more complex and temporally heterogeneous. This could seriously threaten the
livelihoods of local residents and agricultural activities. Therefore, the research
was conducted by using a combined model of LSTM (Long Short-Term Mem-
ory) and SRM (Sinusoidal Regression Model) to assess the trends and anomalies
of salinity intrusion, with a series of data collected from main stations in the VMD
in the year of 2021. The findings showed that the combined model exhibited high
predictive (
R
2
= 0
.
9299
, MSE
= 2
.
0861
, and
MAP E
= 0
.
1276
) in fore-
casting the increasing and decreasing trends of salinity intrusion and effectively
detecting anomalous variations. Consequently, these results could be helpful to
policymakers in predicting and responding to future salinity intrusion and to likely
widespread implications for other regions impacted by saline intrusion
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