Climate change is a global issue posing threats to the human population and water systems. As Malaysia experiences a tropical climate with intense rainfall occurring throughout the year, accurate rainfall simulations are particularly important to provide information for climate change assessment and hydrological modelling. An artificial intelligence-based hybrid model, the bootstrap aggregated classification tree–artificial neural network (BACT-ANN) model, was proposed for simulating rainfall occurrences and amounts over the Langat River Basin, Malaysia. The performance of this proposed BACT-ANN model was evaluated and compared with the stochastic non-homogeneous hidden Markov model (NHMM). The observed daily rainfall series for the years 1975–2012 at four rainfall stations have been selected. It was found that the BACT-ANN model performed better however, with slight underproductions of the wet spell lengths. The BACT-ANN model scored better for the probability of detection (POD), false alarm rate (FAR) and the Heidke skill score (HSS). The NHMM model tended to overpredict the rainfall occurrence while being less capable with the statistical measures such as distribution, equality, variance and statistical correlations of rainfall amount. Overall, the BACT-ANN model was considered the more effective tool for the purpose of simulating the rainfall characteristics in Langat River Basin.

气候变化是一个全球性问题，对人口和水系统构成威胁。由于马来西亚是热带气候，全年降雨频繁，因此准确的降雨模拟对于为气候变化评估和水文建模提供信息尤为重要。提出了一种基于人工智能的混合模型，即自举聚合分类树-人工神经网络（BACT-ANN）模型，用于模拟马来西亚兰加特河流域的降雨发生和雨量。评估了该提出的BACT-ANN模型的性能，并将其与随机非均匀隐马尔可夫模型（NHMM）进行了比较。选择了四个降雨站在1975-2012年期间观测到的每日降雨序列。结果发现，BACT-ANN模型表现更好，但湿法术长度略有不足。BACT-ANN模型在检测概率（POD），误报率（FAR）和海德克技能评分（HSS）上得分更高。NHMM模型趋向于高估降雨发生，而缺乏统计量（如降雨量的分布，均等，方差和统计相关性）的能力。总体而言，BACT-ANN模型被认为是更有效的工具，用于模拟Langat流域的降雨特征。NHMM模型趋向于高估降雨发生，而缺乏统计量（如降雨量的分布，均等，方差和统计相关性）的能力。总体而言，BACT-ANN模型被认为是更有效的工具，用于模拟Langat流域的降雨特征。NHMM模型趋向于高估降雨发生，而缺乏统计量（如降雨量的分布，均等，方差和统计相关性）的能力。总体而言，BACT-ANN模型被认为是更有效的工具，用于模拟Langat流域的降雨特征。