The economic sustainability of aquifers across the world relies on accurate and rapid estimates of groundwater storage changes, but this becomes difficult due to the absence of in-situ groundwater surveys in most areas. By closing the water balance, hydrologic remote sensing measures offer a possible method for quantifying changes in groundwater storage. However, it is uncertain to what extent remote sensing data can provide an accurate assessment of these changes. Therefore, a new framework is implemented in this work for predicting the underground water level using remote sensing images. Generally, the water level is defined into five levels: Critical, Overexploited, Safe, Saline, and Semi-critical, based on water quantity. In this manuscript, the remote sensing images were acquired from remote sensing images. At first, Wiener filtering was employed for preprocessing. Secondly, the Vegetation Indexes (VI) (Normalized Difference Vegetation Index (NDVI), Normalized Difference Snow Index (NDSI), Infrared index (IRI), Radar Vegetation Index (RVI)), and statistical features (entropy, Root Mean Square (RMS), Skewness, and Kurtosis) were extracted from the preprocessed remote sensing images. Then, the extracted features were combined as a novel hydro index, which was fed to the Ensemble Classifier (EC): Neural Networks (NN), Support Vector Machine (SVM), and improved Deep Convolutional Neural Network (DCNN) models for underground water level prediction in the remote sensing images. The obtained results prove the efficacy of the proposed framework by using different performance measures. The results shows that the False Positive Rate (FPR) of the proposed EC model is 0.0083, which is better than that of existing methods. On the other hand, the proposed EC model has a high accuracy of 0.90, which is superior to the existing traditional models: Long Short-Term Memory (LSTM) network, Naïve Bayes (NB), Random Forest (RF), Recurrent Neural Network (RNN), and Bidirectional Gated Recurrent Unit (Bi-GRU).

Wireless mesh networks (WMNs) have rapidly advanced in the last decade because of their ease of experimentation at minimum cost, simple network maintenance, and consistent provision coverage. However, the placement of router nodes remains a critical research challenge for network operators, as it directly impacts the overall performance and effectiveness of the WMN. Thus, this research proposes a novel approach of the somersault fraction strategy (SFS)-based giant armadillo optimization (GAO) for addressing an issue of mesh router (MR) node placement in WMN. A significance of the proposed SFS-GAO approach was estimated in terms of different scenarios under diverse settings, considering network connectivity as well as client coverage metrics. The simulation outcomes demonstrate that the proposed SFS-GAO method achieves better results in each performance metric like coverage, connectivity, and fitness, based on a varied count of mesh clients (MC), MR, and coverage radius. The findings show that the proposed function offers higher client coverage and optimal network connectivity with minimum computation power.