Recent Advances in Computer Science and Communications - Online First

A critical technique that provides quality service for users by solving the conflicts between severe spectrum scarcity and the explosive growth of traffic is Cognitive Radio Ad Hoc Networks (CRAHNs). Nevertheless, a critical challenge is the coexistence of primary and secondary users for reasonable resource allocation to satisfy system performance. Many approaches have been developed to allocate resources efficiently; however, they possess some existing limitations, such as abnormal traffic networks, user collisions, and high data transmission error rates.

So, to overcome such limitations, this paper proposes an efficient policy-configured reinforcement learning-based Ad Hoc Network (AHN) model. The system begins with modeling the Cognitive Radio (CR) network in which the nodes are initialized and clustered using the Link Reliability K-Means clustering Algorithm (LR-KMA) method to derive the optimal policy configuration for the network. Then, to sense the available spectrum and divide it into several bands, spectrum sensing using Coherent Based Detection (CBD) and signal source prediction using the Parzen-Rosenblatt Window-based Restricted Boltzmann Machine (PRW-RBM) were performed.

Next, the suitable bands are selected in the learning model using the Weibull Distribution-based Blue Monkey Optimization (WD-BMO) technique for the resource allocation process. The experimental outcomes were ultimately analyzed to evaluate the proposed resource allocation model's performance in CRAHNs. The LR-KMA algorithm showed 1.5% higher clustering efficiency than traditional methods, while PRW-RBM achieved 1.07% higher classification accuracy.

The optimal resource allocation strategy, WD-BMO, led to lower Normalized Objective Values (NOV) compared to existing methods.

Floods are among the deadliest natural calamities, devastating ecosystems and human lives worldwide. In India, Bihar is a state grappling with economic hardships and faces severe agricultural devastation due to recurring floods, destroying crops and natural resources, which significantly impacts local farmers. This research addresses the critical need to deeply understand the flood dynamics of selected study areas.

This research presents a case study that focuses on leveraging Remote Sensing tools and Machine Learning techniques for comprehensive flood mapping and damage analysis in Gopalganj District, Bihar, India, using remote sensing data. More specifically, this research presents three major objectives: (i) Flood damage mapping and change analysis before and after the flood using the Sentinel-2 satellite dataset, (ii) Evaluation of the impact of integrating spectral indices on the accuracy of classification, (iii) Identification of most robust predictor spectral indices for the classification.

The Sentinel-2 satellite dataset encompasses 13 bands with resolutions of 10m, 20m, and 60m. Here, four spectral bands (NIR, Red, Green, and Blue) with the finest resolution of 10m have been selected for this study. These bands are integrated with four spectral indices, namely Normalized Difference Water Index (NDWI), MNDWI (Modified NDWI), Normalized Difference Vegetation Index (NDVI), and Soil Adjusted Vegetation Index (SAVI). Two ML classifiers, namely Support Vector Machine (SVM) and Random Forest (RF) have been employed for pixel-based supervised classification.

Results have shown that RF outperformed and worked well in extracting water bodies and flood-damaged areas effectively. The results demonstrated that RF obtained (Overall Accuracy (OA)= 89.54% and kappa value (ka) = 0.872) and SVM reported (OA= 87.69%, ka= 0.849) for pre-crisis data, whereas, for post-crisis, RF reported (OA=91.54%, ka = 0.897), SVM reported (OA= 89.77%, ka= 0.875).

It was reported that the integration of spectral indices improved the OA by +3.41% and +2.86% using RF and SVM, respectively. The results of this study demonstrated that the waterbody area increased from 12.72 to 88.23 km^2, as shown by the RF classifier. The variable importance computation results indicated that MNDWI is the most important predictor variable, followed by NDWI. This study recommends the use of these two predictor variables for flood mapping.