## 1 Introduction
Finally, the observation of various medium qualities (underground substances and open-air medium) was not attained through optimal ML and DL approaches. These are noted as crucial research problems for feasible data communication. On the prospect, the proposed DMCAP creates the suitable signaling models, nonlinear channel assessment models, multi-channel interference models, and channel distortion analysis models against uncertain WUGSN channels. Particularly, the proposed research work is motivated to design and implement a reactive nonlinear channel learning models and IDS engines for supporting feasible channel quality estimation. This research work backs higher throughput attainment through the proposed DMCAP model with EMLP, ENLSVM, VGAN-IDS, and nonlinear entropy analysis procedures. The proposed DMCAP model is significant against existing techniques in terms of differentiated channel assessment procedures. Notably, most of the existing techniques were implemented for analyzing the channel qualities using linear models for WUGSN. The existing techniques were not implemented accurately for evaluating multi-channel quality assessment factors such as nonlinear noise quantity, entropy rate, signal loss rate, and multi-channel interference rate (underground, underground to surface, and surface to surface). According to the motivation, this proposed model classifies the channel under three categories such as underground wireless channels, underground-surface channels and surface wireless channels. The proposed channel models and signal models are created by analyzing wireless channel attributes and signal attributes, respectively. Particularly, the proposed DMCAP model analyzes the channel quality metrics and nonlinearity issues using EMLP and ENLSVM. In this case, EMLP is used to determine the classified entropy levels for various wireless channels. The development of EMLP (back propagation procedures) is initialized over the configured model of interference and energy optimization. EMLP is a useful procedure for classifying the channel interference on multiple attribute (multi-modal) validation schemes. In this manner, ensemble MLP units are created to learn and classify multi-channel entropy attributes in WUGSN environment. In the next case, ENLSVM ensures multi-level classifier units for extracting SNIR from multiple channel quality metrics. ENLSVM consists of multi-channel SNIR distribution procedures, SNIR classification, and likelihood analysis procedures under uncertain network conditions. In the final case, VGAN-IDS analyzes the channel distortion rate initiated by malicious sensor nodes. On this basis, the proposed model observes and classifies the channel quality metrics of WUGSN through reactive channel learning and adaptation procedures. Thus, the proposed DMCAP model achieves optimal data transmission pattern and energy saving solution. The proposed DMCAP model has the motivation to optimize overall WUGSN communication quality depends upon real-time multi-channel uncertainties. The technical contributions of proposed model are listed below. * Development of multi-channel signaling and channel models;
* Development of multi-channel ensemble model and channel attribute classification model;
* Implementing channel entropy classification procedures (EMLP);
* Configuring SNIR distribution and nonlinear regression procedures (ENLSVM);
* Channel distortion analysis against malicious events;
* Supporting optimal wireless channel utilization and data communication solutions through proposed channel behavior learning techniques. Unlike other existing techniques, the testbed of the proposed model was configured for both surface level channel parameters and underground channel parameters. According to real-time assumptions, the proposed DMCAP model was enriched with a dual channel propagation model such as acoustic (underground) and ground (surface) features. These are the notable features of the proposed DMCAP model compared with existing techniques.