Environmental Prediction Using a Spatiotemporal WSN: A New Method for Integrating BKA Optimization and CNN-BiLSTM

Accurate environmental prediction is crucial for ecological monitoring and disaster early warnings, but it remains challenging due to the spatiotemporal complexity of dynamic wireless sensor networks (WSNs). To this end, we propose a novel hybrid model that integrates a convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM), and a black-winged kite algorithm (BKA). The CNN first extracts spatial features from multi-node sensor data to capture local environmental patterns. Subsequently, the BKA optimizes key CNN hyperparameters (learning rate, hidden layers, and regularization coefficients) to enhance the robustness of feature representation to noise and missing data. Subsequently, the BiLSTM processes the optimization features to model bidirectional long-term time dependencies (e.g., circadian rhythms, seasonal trends) to achieve accurate environmental predictions. Evaluation of the BKA-optimized CNN-BiLSTM model shows that our framework reduces prediction error by 19.3% to 32.7% compared to other models, achieving 89.4% accuracy in predicting extreme weather events. The synergy between BKA-driven CNN optimization and BiLSTM temporal dynamics modeling significantly improves the reliability of environmental prediction in resource-constrained sensor networks.