The four-parameter kappa distribution (K4D) is a generalized form of some commonly used distributions such as generalized logistic, generalized Pareto, generalized Gumbel, and generalized extreme value (GEV) distributions. Owing to its flexibility, the K4D is widely applied in modeling in several fields such as hydrology and climatic change. For the estimation of the four parameters, the maximum likelihood approach and the method of L-moments are usually employed. The L-moment estimator (LME) method works well for some parameter spaces, with up to a moderate sample size, but it is sometimes not feasible in terms of computing the appropriate estimates. Meanwhile, using the maximum likelihood estimator (MLE) with small sample sizes shows substantially poor performance in terms of a large variance of the estimator. We therefore propose a maximum penalized likelihood estimation (MPLE) of K4D by adjusting the existing penalty functions that restrict the parameter space. Eighteen combinations of penalties for two shape parameters are considered and compared. The MPLE retains modeling flexibility and large sample optimality while also improving on small sample properties. The properties of the proposed estimator are verified through a Monte Carlo simulation, and an application case is demonstrated taking Thailand’s annual maximum temperature data.

Facing the increasing complexity and dynamic fluctuations of the global agricultural trade market, accurate forecasting plays a key role in supporting agricultural policy formulation, stabilising the market and optimising resource allocation. In order to increase the precision and stability of agricultural trade predictions, this research suggests a hybrid model built on a temporal convolution network (TCN) and a lightweight gradient boosting tree (LightGBM). The TCN module effectively captures the long-term dependence characteristics of time series data through dilated convolution, which improves the model’s ability to identify seasonal and periodic trends. The LightGBM module, on the other hand, makes use of the characteristics of gradient boosting decision trees and excels at efficiently handling nonlinear relationships and avoiding overfitting. Experimental results show that the TCN-LightGBM model outperforms traditional models in terms of mean square error (MSE), mean absolute error (MAE) and prediction accuracy. Specifically, compared with ARIMA, LSTM, TCN alone or LightGBM alone, TCN-LightGBM achieves a prediction accuracy of 91.3% on the test data, with MSE and MAE of 0.021 and 0.115 respectively, significantly improving prediction accuracy and stability. In addition, parameter sensitivity analysis shows that the TCN-LightGBM model maintains a highly consistent prediction trend under different parameter configurations, which verifies the robustness of the model and its practical application value. This study provides a data-driven decision support tool with high accuracy and strong stability, providing a new solution for agricultural trade forecasting and other complex time series prediction tasks.

Non‐stationarity in heavy rainfall time series is often apparent in the form of trends because of long‐term climate changes. We have built non‐stationary (NS) models for annual maximum daily (AMP1) and 2‐day precipitation (AMP2) data observed between 1984 and 2020 years by 71 stations and between 1960 and 2020 by eight stations over Thailand. The generalized extreme value (GEV) models are used. Totally, 16 time‐dependent functions of the location and scale parameters of the GEV model are considered. On each station, a model is selected by using Bayesian and Akaike information criteria among these candidates. The return levels corresponding to some years are calculated and predicted for the future. The stations with the highest return levels are Trad, Samui, and Narathiwat, for both AMP1 and AMP2 data. We found some evidence of increasing (decreasing) trends in maximum precipitation for 22 (10) stations in Thailand, based on NS GEV models.

The objective of the present study was to examine and predict the annual maximum temperature in the northeast of Thailand by using data from 25 stations and employing spatial extreme modeling which is based on max-stable process (MSP) using schlatter’s method. We analyzed extreme temperature data using the MSP using latitude, longitude, and altitude variables. Our result showed that the maximum temperature has an increasing trend. The return level estimates of the study areas from both the local generalized extreme value (GEV) model and MSP models show that the Nong Khai, Maha Sarakham, and Khon Kaen stations had higher return levels than the other stations for every return period, whereas Pak Chong Agromet had the lowest return levels. Furthermore, the results showed that MSP modeling is more suitable than point-wise GEV distribution. We realize that the spatial extreme modeling based on MSP provides more precise and robust return levels as well as some indices of the maximum temperatures for both the observation stations and the locations with no observed data. The results of this study are consistent with those of some previous studies. The increasing trend in return levels could affect agriculture and the surrounding environment in northeast Thailand. Spatial extreme modeling can be beneficial in the impact management and vulnerability assessment under extreme event scenarios caused by climate change.