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. 2023 Sep 1;23(17):7583.
doi: 10.3390/s23177583.

IoT and Ensemble Long-Short-Term-Memory-Based Evapotranspiration Forecasting for Riyadh

Muhammad Asif Nauman  1 , Mahlaqa Saeed  2 , Oumaima Saidani  3 , Tayyaba Javed  4 , Latifah Almuqren  3 , Rab Nawaz Bashir  5 , Rashid Jahangir  5
Affiliations

IoT and Ensemble Long-Short-Term-Memory-Based Evapotranspiration Forecasting for Riyadh

Muhammad Asif Nauman et al. Sensors (Basel). .

Abstract

Evapotranspiration (ET) is the fundamental component of efficient water resource management. Accurate forecasting of ET is essential for efficient water utilization in agriculture. ET forecasting is a complex process due to the requirements of large meteorological variables. The recommended approach is based on the Internet of Things (IoT) and an ensemble-learning-based approach for meteorological data collection and ET forecasting with limited meteorological conditions. IoT is part of the recommended approach to collect real-time data on meteorological variables. The daily maximum temperature (T), mean humidity (Hm), and maximum wind speed (Ws) are used to forecast evapotranspiration (ET). Long short-term memory (LSTM) and ensemble LSTM with bagged and boosted approaches are implemented and evaluated for their accuracy in forecasting ET values using meteorological data from 2001 to 2023. The results demonstrate that the bagged LSTM approach accurately forecasts ET with limited meteorological conditions in Riyadh, Saudi Arabia, with the coefficient of determination (R2) of 0.94 compared to the boosted LSTM and off-the-shelf LSTM with R2 of 0.91 and 0.77, respectively. The bagged LSTM model is also more efficient with small values of root mean squared error (RMSE) and mean squared error (MSE) of 0.42 and 0.53 compared to the boosted LSTM and off-the-shelf LSTM models.

Keywords: bagged LSTM; boosted LSTM; ensemble learning; evapotranspiration (ET); long short-term memory (LSTM).

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Factors affecting evapotranspiration (ET).
Figure 2
Figure 2
Flowchart of proposed ET forecasting.
Figure 3
Figure 3
Proposed LoRaWAN-enabled IoT architecture.
Figure 4
Figure 4
Location of Riyadh, Saudi Arabia on world map.
Figure 5
Figure 5
Correlation between climatic conditions and ET.
Figure 6
Figure 6
ET distributions in data set.
Figure 7
Figure 7
Distribution of daily maximum temperature (T) to months.
Figure 8
Figure 8
Distribution of daily mean humidity (Hm) to months.
Figure 9
Figure 9
Distribution of daily maximum wind speed (Ws) to months.
Figure 10
Figure 10
Preprocessing of data.
Figure 11
Figure 11
Architecture of LSTM.
Figure 12
Figure 12
Sequence diagram of bagged LSTM.
Figure 13
Figure 13
Sequence diagram of boosting LSTM.
Figure 14
Figure 14
ET forecasted with different ML models along with actual ET values calculated by PM method in the test data set.
Figure 15
Figure 15
Difference in ET forecasted with different ML models compared to actual ET calculated using PM method.
See this image and copyright information in PMC

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