<![CDATA[Flooding frequently affects the Tangerang region and causes substantial social and economic losses. Conventional forecasting methods may not adequately capture the nonlinear relationships between dynamic urban weather conditions and flood events. This study applies a one-dimensional convolutional neural network (1D-CNN) to classify flood potential using multivariate weather parameters and historical flood-event records. Temperature, relative humidity, and rainfall observations from three Automatic Weather Stations were combined with flood-event data from 2022 to 2024. Preprocessing included quality-control checks, outlier treatment, Multiple Imputation by Chained Equations (MICE), moving-average and lag features, sliding-window sequence construction, Min-Max normalization, and an 80:20 training-testing split. The model used 40 convolutional filters, max pooling, a 40-neuron hidden layer, and a three-class Softmax output representing low, medium, and high flood potential. The 1D-CNN achieved 83.59% accuracy, 83.52% precision, 83.59% recall, and an F1-score of 83.52%. The confusion matrix showed strong identification of low- and high-risk cases, although the medium class and some high-risk events remained difficult to distinguish. These results indicate that integrating weather observations with historical disaster records can support the development of a data-driven flood early warning framework for the Tangerang region.]]>
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