## 1 Introduction  
WUGSNs are widely used in the field of object surveillance under the ground surface. WUGSNs need multiple underground sensor nodes deployed sparsely or densely under the surface level. The underground sensor nodes detect the environmental objects and resources (oil substances, water substances, soil materials, etc.). Extensively, these networks help to observe the real-time object movements in defense sectors [1]. WUGSN nodes are operated with a limited set of resources (memory, processor, energy, and lifetime) amongst real-time issues such as vulnerable medium, channel distortions, and uncertain environmental conditions. Amid these network issues, learning and predicting the wireless channel parameters are major problems. Particularly, a WUGSN channel maintains three types of links for each data transmission. The channel creates the association between underground sensor node and surface sensor node. Next, the channel provides the link between surface sensor node and ground base station. In addition, a third type of link makes the data path between underground sensor nodes [2]. Each type of wireless channel carries network data under unique data traffic parameters and channel quality metrics. The channel qualities and traffic parameters are configured based on signal strength (amplitude), transmission energy, receiving energy, data transfer rate, transmission range, noise rate, interference rate, attenuation rate, traffic type, connection establishment rate, antenna type, medium access control policies, influence rate of legitimate sensor nodes, influence rate of malicious sensor nodes, and other uncertain events. The channel quality parameters are uncommon for different types of WUGSN mediums established for underground transmission, underground to surface transmission, and surface to base station transmission [3; 4; 5]. In this case, the channel parameter analysis model needs suitable channel quality management principles, adaptive learning functions, and reactive backing systems [6; 7]. The recent development in computing arena initiates the variants of Machine Learning (ML) and Deep Learning (DL) frameworks to make intelligent decisions against critical problems. Recently introduced channel quality assessment techniques use ML and DL approaches for predicting the channel behaviors. The artificial decision-making systems provide channel metric collection, data preprocessing, classification, decision making, and report generation phases. The existing solutions contribute better understanding practices against uncertain channel measurements. [PERSON] et al. [8] proposed ML-based channel quality and stability evaluation procedures for Wireless Sensor Networks (WSNs). This novel channel assessment model achieved baseline channel prediction principles for evaluating channel rank points, signal strength indicator, and connection quality metrics. In this work, the common aggregated channel quality indicator value showed the stability of wireless channels. Similarly, [PERSON] et al. [9] analyzed the channel modelling principles for wireless communication. The channel modelling is the process of computing channel measurement quantities using statistical data analysis, stochastic process, and ML principles. This work modelled the wireless channel qualities using signal fading rate, bandwidth rate, Doppler spread, and block error rate. The ML-based channel measurement functions were computed for observing upper bound and lower bound characteristics of wireless channels. In the same manner, many research works have evolved to track and analyze the wireless channel parameters [10; 11]. Moreover, the existing wireless channel models were limited against nonlinear event analysis policies and multi-channel attribute analysis policies. Firstly, the existing channel configuration models and parameter estimation models utilized default attributes of static wireless sensor networks. Secondly, the classification of channel distortions due to environ mental issues and malicious events were not identified separately.