Rainfall forecasting is one of the most challenging factors of weather forecasting all over the planet. Due to climate change, Thailand has experienced extreme weather events, including prolonged lacks of and heavy rainfall. Accurate rainfall forecasting is crucial for Thailand’s agricultural sector. Agriculture depends on rainfall water, which is important for water resources, adversity management, and overall socio-economic development. Artificial intelligence techniques (AITs) have shown remarkable precision in rainfall forecasting in the past two decades. AITs may accurately forecast rainfall by identifying hidden patterns from past weather data features. This research investigates and reviews the most recent AITs focused on advanced machine learning (ML), artificial neural networks (ANNs), and deep learning (DL) utilized for rainfall forecasting. For this investigation, academic articles from credible online search libraries published between 2000 and 2022 are analyzed. The authors focus on Thailand and the worldwide applications of AITs for rainfall forecasting and determine the best methods for Thailand. This will assist academics in analyzing the most recent work on rainfall forecasting, with a particular emphasis on AITs, but it will also serve as a benchmark for future comparisons. The investigation concludes that hybrid models combining ANNs with wavelet transformation and bootstrapping can improve the current accuracy of rainfall forecasting in Thailand.

Handling missing values is a critical component of the data processing in hydrological modeling. The key objective of this research is to assess statistical techniques (STs) and artificial intelligence-based techniques (AITs) for imputing missing daily rainfall values and recommend a methodology applicable to the mountainous terrain of northern Thailand. In this study, 30 years of daily rainfall data was collected from 20 rainfall stations in northern Thailand and randomly 25–35 % of data was deleted from four target stations based on Spearman correlation coefficient between the target and neighboring stations. Imputation models were developed on training and testing datasets and statistically evaluated by mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R²), and correlation coefficient (r). This study used STs, including arithmetic averaging (AA), multiple linear regression (MLR), normal-ratio (NR), nonlinear iterative partial least squares (NIPALS) algorithm, and linear interpolation was used. • STs results were compared with AITs, including long-short-term-memory recurrent neural network (LSTM-RNN), M5 model tree (M5-MT), multilayer perceptron neural networks (MLPNN), support vector regression with polynomial and radial basis function SVR-poly and SVR-RBF. • The findings revealed that MLR imputation model achieved an average MAE of 0.98, RMSE of 4.52, and R² was about 79.6 % at all target stations. On the other hand, for the M5-MT model, the average MAE was 0.91, RMSE was about 4.52, and R² was around 79.8 % compared to other STs and AITs. M5-MT was most prominent among AITs. Notably, the MLR technique stood out as a recommended approach due to its ability to deliver good estimation results while offering a transparent mechanism and not necessitating prior knowledge for model creation.

The purpose of this research is to study the spatial and temporal groupings of 124 meteorological stations in Thailand under ENSO. The multivariate climate variables are rainfall, relative humidity, temperature, max temperature, min temperature, solar downwelling, and horizontal wind from the conformal cubic atmospheric model (CCAM) in years of El Niño (1987, 2004, and 2015) and La Niña (1999, 2000, and 2011). Euclidean distance timed and spaced with average linkage for clustering and silhouette width for cluster validation were employed. Five spatial clusters (SCs) and three temporal clusters (TCs) in each SC with different average precipitation were compared by El Niño and La Niña. The pattern of SCs and TCs was similar for both events except in the case when severe El Niño occurred. This method could be applied using variables forecasted in the future to be used for planning and managing crop cultivation with the climate change in each area.

To evaluate the psychometric properties of the Thai version of the Cancer Survivors’ Unmet needs (CaSUN-TH) scale among Thai cancer survivors after completion of primary treatment. Standardized translation procedures developed the Cancer Survivors’ Unmet Needs into a Thai version (CaSUN-TH). Face validity was evaluated by a group of experts, and a pilot test on 10 cancer patients was conducted to evaluate its readability. A total of 236 cancer survivors who were attending follow-up visits at a cancer hospital in Thailand completed the CaSUN-TH. The internal consistency of the instrument was examined using Cronbach’s α. The association of the CaSUN-TH and its subscales with physical symptoms, QoL, age, gender, and type of cancer were examined for criterion validity and known-group validity. Construct validity was evaluated using confirmatory factor analysis. The CaSUN-TH showed good readability and high content validity for use as an instrument to assess unmet needs among Thai cancer survivors. Cronbach’s $\alpha$ for the entire scale was 0.95. Confirmatory factor analysis indicated that the five-factor structure of the CaSUN-TH was good fit to the data (CFI ​= ​0.901, SRMR ​= ​0.074, RMSEA ​= ​0.076 [90% confidence interval, 0.066–0.085]). In terms of construct validity, CaSUN-TH scores significantly correlated to other variables hypothesized to influence the level of need, including higher physical symptoms prevalence was related to poor quality of life, and poorer QOL and younger age were associated with a higher level of unmet needs. In addition, the scale was able to differentiate scores between groups, including gender, age, and type of primary cancer, with theoretically hypothesized differences. The CaSUN-TH demonstrated appropriate psychometric properties for assessing unmet needs in different cancer survivor groups in Thailand. Using the CaSUN-TH can help health professionals in targeting individual survivor needs, bridging the gap between patients’ experiences and their expectations, and improving the quality of cancer survivorship care.