Combining Long Short-Term Memory and Genetic Programming for Monthly Rainfall Downscaling in Southern Thailand's Thale Sap Songkhla River Basin

Sirimon Pinthong1

Pakorn Ditthakit1,Email

Nureehan Salaeh1

Warit Wipulanusat2

Uruya Weesakul3

Ismail Elkhrachy4

Krishna Kumar Yadav5,6

Nand Lal Kushwaha7

1Center of Excellence in Sustainable Disaster Management, School of Engineering and Technology,  Walailak University, Nakhon Si Thammarat 80161, Thailand.
2Department of Civil Engineering, Thammasat University Research Unit in Data Science and Digital Transformation, Thammasat School of Engineering, Thammasat University, Pathumthani, Thailand.

3Thammasat University Research Unit in Climate Change and Sustainability, Department of Civil Engineering, Faculty of Engineering, Thammasat School of Engineering, Thammasat University, Pathumthani 12120, Thailand.
4College of Engineering, Civil Engineering Department, Najran University, Najran 66291, Saudi Arabia.
5Faculty of Science and Technology, Madhyanchal Professional University, Ratibad, Bhopal, 462044, India.
6Environmental and Atmospheric Sciences Research Group, Scientific Research Center, Al-Ayen University, Thi-Qar, Nasiriyah, 64001, Iraq.
7Division of Agricultural Engineering, ICAR-Indian Agricultural Research Institute, New Delhi 110012, India.


This paper proposed a novel methodology, combining Long Short-Term Memory (LSTM) and Genetic Programming (GP), for downscaling monthly rainfall into watershed regions. The exploration of suitable downscaling models and the trend of monthly rainfall in the 2030s, 2060s, and 2080s in the Thale Sap Songkhla river basin (TSS) was investigated. The TSS is one of four major areas in Thailand’s southern basin and has a tropical monsoon climate. The monthly rainfall observed by the Royal Irrigation Department (RID) from January 1993 to December 2018 (312 months) was available at three rainfall stations. Six machine learning techniques (i.e., M5, RF, SVR, MLP, GP, and LSTM) were employed to downscale the monthly rainfall data from the General Circulation Models (GCMs) of CMIP5 (HadGEM2-ES and ACCESS1-3) and CMIP6 (HadGEM2-CGM31-LL and ACCESS-CM2) under the RCP4.5 (SSP245) and RCP8.5 (SSP585) scenarios. Since the TSS experiences significant differences in low and high rainfall for January–September and October–December, respectively, those data were analyzed separately in addition to using the whole-year data sets. This study considered six common climate variables: precipitation (pr), maximum near-surface air temperature (tasmax), minimum near-surface air temperature (tasmin), relative humidity (hur), sea level pressure (psl), and near-surface wind speed (sfcWind). These variables were chosen based on the correlation between them and the observed rainfall data. The findings of this research indicate that when LSTM and GP models are merged, they are the most efficient for downscaling monthly rainfall. The OI and r-value illustrate a highly robust relationship between the average values within the TSS watershed. These results offer valuable understandings regarding the clear strengths and limitations of every model category, which are influenced by factors such as the size of the data and the characteristics used in the model training process. Climate change is likely to have only a minor impact on rainfall patterns in TSS in the near future, both in moderate and extreme emission scenarios. However, significant changes are expected in the later stages of this century (2060s and 2080s), particularly during the monsoon season, which experiences drastic shifts.

Combining Long Short-Term Memory and Genetic Programming for Monthly Rainfall Downscaling in Southern Thailand's Thale Sap Songkhla River Basin