Long-Short Term Memory Technique for Monthly Rainfall Prediction in Thale Sap Songkhla River Basin, Thailand
Salaeh, Nureehan ; Ditthakit, Pakorn ; Pinthong, Sirimon ; Abul Hasan, Mohd ; Islam, Saiful ; Mohammadi, Babak LU
and Thuy Linh, Nguyen
(2022)
In Symmetry
14(8).
- Abstract
- Rainfall is a primary factor for agricultural production, especially in a rainfed agricultural region. Its accurate prediction is therefore vital for planning and managing farmers’ plantations. Rainfall plays an important role in the symmetry of the water cycle, and many hydrological models use rainfall as one of their components. This paper aimed to investigate the applicability of six machine learning (ML) techniques (i.e., M5 model tree: (M5), random forest: (RF), support vector regression with polynomial (SVR-poly) and RBF kernels (SVR- RBF), multilayer perceptron (MLP), and long-short-term memory (LSTM) in predicting for multiple-month ahead of monthly rainfall. The experiment was set up for two weather gauged stations located in the... (More)
- Rainfall is a primary factor for agricultural production, especially in a rainfed agricultural region. Its accurate prediction is therefore vital for planning and managing farmers’ plantations. Rainfall plays an important role in the symmetry of the water cycle, and many hydrological models use rainfall as one of their components. This paper aimed to investigate the applicability of six machine learning (ML) techniques (i.e., M5 model tree: (M5), random forest: (RF), support vector regression with polynomial (SVR-poly) and RBF kernels (SVR- RBF), multilayer perceptron (MLP), and long-short-term memory (LSTM) in predicting for multiple-month ahead of monthly rainfall. The experiment was set up for two weather gauged stations located in the Thale Sap Songkhla basin. The model development was carried out by (1) selecting input variables, (2) tuning hyperparameters, (3) investigating the influence of climate variables on monthly rainfall prediction, and (4) predicting monthly rainfall with multi-step-ahead prediction. Four statistical indicators including correlation coefficient (r), mean absolute error (MAE), root mean square error (RMSE), and overall index (OI) were used to assess the model’s effectiveness. The results revealed that large-scale climate variables, particularly sea surface temperature, were significant influence variables for rainfall prediction in the tropical climate region. For projections of the Thale Sap Songkhla basin as a whole, the LSTM model provided the highest performance for both gauged stations. The developed predictive rainfall model for two rain gauged stations provided an acceptable performance: r (0.74), MAE (86.31 mm), RMSE (129.11 mm), and OI (0.70) for 1 month ahead, r (0.72), MAE (91.39 mm), RMSE (133.66 mm), and OI (0.68) for 2 months ahead, and r (0.70), MAE (94.17 mm), RMSE (137.22 mm), and OI (0.66) for 3 months ahead. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/9d4f08eb-4517-4d2c-a3a2-26b9273878cf
- author
- Salaeh, Nureehan
; Ditthakit, Pakorn
; Pinthong, Sirimon
; Abul Hasan, Mohd
; Islam, Saiful
; Mohammadi, Babak
LU
and Thuy Linh, Nguyen
- organization
- publishing date
- 2022
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Machine Learning, Deep Learning, Rainfall prediction, multi-step-ahead prediction
- in
- Symmetry
- volume
- 14
- issue
- 8
- article number
- 1599
- publisher
- MDPI AG
- external identifiers
-
- scopus:85137359364
- ISSN
- 2073-8994
- DOI
- 10.3390/sym14081599
- language
- English
- LU publication?
- yes
- id
- 9d4f08eb-4517-4d2c-a3a2-26b9273878cf
- date added to LUP
- 2022-09-18 08:37:07
- date last changed
- 2024-01-24 11:01:37
@article{9d4f08eb-4517-4d2c-a3a2-26b9273878cf,
abstract = {{Rainfall is a primary factor for agricultural production, especially in a rainfed agricultural region. Its accurate prediction is therefore vital for planning and managing farmers’ plantations. Rainfall plays an important role in the symmetry of the water cycle, and many hydrological models use rainfall as one of their components. This paper aimed to investigate the applicability of six machine learning (ML) techniques (i.e., M5 model tree: (M5), random forest: (RF), support vector regression with polynomial (SVR-poly) and RBF kernels (SVR- RBF), multilayer perceptron (MLP), and long-short-term memory (LSTM) in predicting for multiple-month ahead of monthly rainfall. The experiment was set up for two weather gauged stations located in the Thale Sap Songkhla basin. The model development was carried out by (1) selecting input variables, (2) tuning hyperparameters, (3) investigating the influence of climate variables on monthly rainfall prediction, and (4) predicting monthly rainfall with multi-step-ahead prediction. Four statistical indicators including correlation coefficient (r), mean absolute error (MAE), root mean square error (RMSE), and overall index (OI) were used to assess the model’s effectiveness. The results revealed that large-scale climate variables, particularly sea surface temperature, were significant influence variables for rainfall prediction in the tropical climate region. For projections of the Thale Sap Songkhla basin as a whole, the LSTM model provided the highest performance for both gauged stations. The developed predictive rainfall model for two rain gauged stations provided an acceptable performance: r (0.74), MAE (86.31 mm), RMSE (129.11 mm), and OI (0.70) for 1 month ahead, r (0.72), MAE (91.39 mm), RMSE (133.66 mm), and OI (0.68) for 2 months ahead, and r (0.70), MAE (94.17 mm), RMSE (137.22 mm), and OI (0.66) for 3 months ahead.}},
author = {{Salaeh, Nureehan and Ditthakit, Pakorn and Pinthong, Sirimon and Abul Hasan, Mohd and Islam, Saiful and Mohammadi, Babak and Thuy Linh, Nguyen}},
issn = {{2073-8994}},
keywords = {{Machine Learning; Deep Learning; Rainfall prediction; multi-step-ahead prediction}},
language = {{eng}},
number = {{8}},
publisher = {{MDPI AG}},
series = {{Symmetry}},
title = {{Long-Short Term Memory Technique for Monthly Rainfall Prediction in Thale Sap Songkhla River Basin, Thailand}},
url = {{http://dx.doi.org/10.3390/sym14081599}},
doi = {{10.3390/sym14081599}},
volume = {{14}},
year = {{2022}},
}