Prediction of Water Levels on Peatland using Deep Learning

Prediction of Water Levels on Peatland using Deep Learning

water level, peatlands, prediction, deep learning, LSTM, CRISP-DM

The water level on peatlands is one of the causes of peatland fires, so water levels must be maintained at a safe standard value.
Government Regulation No. 71/2014 stipulates water level standard value is 0.4 meters. The forest and land fires in 2015
caused huge losses of 220 trillion Rupiah. However, fires still occur frequently. BRGM (Peatland and Mangrove Restoration
Agency) installed sensors measuring peatland water levels to obtain real-time water level data. These data can be used to
predict water levels. Several previous studies used drought indices, regression models, and artificial neural networks to predict
water levels. In this study, it is proposed to use deep learning Long Short-Term Memory (LSTM), and apply the CRISP-DM
methodology. The dataset in this study contains water level data from 15 measurement stations in Central Kalimantan from
2018 through 2021. It was concluded that the LSTM model was able to predict water level well, as indicated by the average
RMSE of 0.07 m, the average R2
of 0.85, and the average MAE of 0.04 m. The optimal LSTM model parameters are 50 epochs,
a 70%:30% ratio of training data to testing data, and 2 hidden layers

Namora1
, Jan Everhard Riwurohi2

Universitas Budi Luhur

20-04-2022

Fajar bagus W

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Indonesia

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