Rainfall forecasting plays a critical role in climate adaptation, agriculture, and water resource management. This study develops long-term forecasts of monthly rainfall across 19 districts of West Bengal using a century-scale dataset spanning 1900-2019. Daily rainfall records are aggregated into monthly series, resulting in 120 years of observations for each district. The forecasting task involves predicting the next 108 months (9 years, 2011-2019) while accounting for temporal dependencies and spatial interactions among districts. To address the nonlinear and complex structure of rainfall dynamics, we propose a hierarchical modeling framework that combines regression-based forecasting of yearly features with multi-layer perceptrons (MLPs) for monthly prediction. Yearly features, such as annual totals, quarterly proportions, variability measures, skewness, and extremes, are first forecasted using regression models that incorporate both own lags and neighboring-district lags. These forecasts are then integrated as auxiliary inputs into an MLP model, which captures nonlinear temporal patterns and spatial dependencies in the monthly series. The results demonstrate that the hierarchical regression-MLP architecture provides robust long-term spatio-temporal forecasts, offering valuable insights for agriculture, irrigation planning, and water conservation strategies.

翻译：降雨预测在气候适应、农业和水资源管理中起着至关重要的作用。本研究利用横跨1900-2019年的百年尺度数据集，对西孟加拉邦19个地区的月降雨量进行长期预测。日降雨记录被汇总为月度序列，从而为每个地区提供了120年的观测数据。预测任务涉及对未来108个月（2011-2019年，共9年）的降雨量进行预测，同时考虑时间依赖性和地区间的空间相互作用。为应对降雨动态的非线性和复杂结构，我们提出了一种分层建模框架，该框架结合了基于回归的年特征预测与用于月度预测的多层感知机（MLPs）。首先，利用包含自身滞后项和邻近地区滞后项的回归模型，对年特征（如年总量、季度比例、变异性度量、偏度和极值）进行预测。随后，将这些预测结果作为辅助输入整合到一个MLP模型中，该模型能够捕捉月度序列中的非线性时间模式和空间依赖性。结果表明，分层回归-MLP架构能够提供稳健的长期时空预测，为农业、灌溉规划和水资源保护策略提供有价值的见解。