Application of swarm-based deep neural networks and ensemble models for reconstruction of specific conductance data

Monitoring the specific conductance (SC) in coastal zones is vital for environmental management and sustainable development. Due to unpredictable reasons such as atmospheric conditions, mechanical problems, power outages, sensors limits, etc., recording systems may fail which causes gaps in data recording. In this study, original artificial intelligence (AI) models are developed for the modeling and reconstruction of missing SC data. Two novel swarm-based deep neural networks (DNNs)-the nonlinear group method of data handling (NGMDH) and a long short-term memory (LSTM) model integrated with the turbulent flow of water-based optimization (TFWO) algorithm were developed and applied to model SC records. The results were also compared with six conventional and two ensemble machine learning (ML) models. The efficacies of the models were evaluated in five hypothetical scenarios. Then, in the derivation phase, the best models were applied to the SC datasets comprising 5% gaps. The results highlighted the extraordinary role of AI-based models in improving knowledge on SC distribution in coastal waters. The new LSTM-TFWO and NGMDH-TFWO models, with average normalized root mean square error (NRMSE) of 0.11 and 0.11, and R² of 0.742 and 0.71, are approximately 11% and 6.36% more accurate than LSTM and NGMDH models, respectively. However, the tree-based models, with an average NRMSE of 0.05, demonstrate substantially higher accuracy than these complex DNN architectures. Among all the ML methods evaluated, ensemble models showed superior performance in reconstructing gaps in SC datasets. XGBoost achieved the highest accuracy, as indicated by an NRMSE of 0.031. Consequently, ensemble models are recommended for application in simulating various types of engineering problems.