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).

Recently, serverless computing has become a newfound computing platform for the cloud-based deployment of applications and it took over all other contemporary computing platforms. It has two significant advantages over its contemporary computing platforms. First off, it enables software developers to delegate to cloud service providers all infrastructure maintenance and operational chores, allowing them to concentrate solely on the business logic of their applications. The second is that it has a strict pay-per-use business model, where customers are only charged for the resources, they utilize. Despite its advantages, researchers have been deeply examining the actual security guarantees offered by the current defences employed to safeguard container-based infrastructures over the past few years. This led to the discovery of significant flaws in the security controls employed for network security and process isolation. In this chapter, the authors highlight the security attacks/issues with the investigated serverless architecture platforms and suggest potential countermeasures. Request access from your librarian to read this chapter's full text.