Mapping Groundwater Potential Using Remote Sensing with Tri-Plateau Exponential Stereoscopic Scalable Quantum Cascaded Visual Attention Network

Though effective, remote sensing-groundwater potential mapping has limitations in the form of feature selection complexity, atmospheric errors, and the need for high-resolution imagery. Land cover variations, differences in soil makeup, and annual cycles could potentially affect accuracy, and deep models require significant computation for extensive application. Through the use of KOMPSAT-2 data, this paper demonstrates how Tri-Plateau Exponential Stereoscopic Scalable Quantum Cascaded Visual Attention Network (TP-ES-SQCVANet) beats groundwater potential mapping when it comes to enhancing the accuracy of classification and surmounting traditional hurdles. The Exponential Distribution Optimization (EDO) technique is applied for feature selection following preprocessing using the Adaptive Tri-Plateau Limit Tri-Histogram Algorithm (ATP-LTH). Reliable groundwater potential mapping using remote sensing is guaranteed by using the Planet Optimization Algorithm (POA) for optimization following the Stereoscopic Scalable Quantum Cascaded Visual Attention Network (SSQCVANet) for classification. The Python test script assesses groundwater potential mapping with the KOMPSAT-2 dataset. Receiver Operating Characteristic (ROC) technique was utilized to assess the performance of the model. Groundwater potential maps were also produced and compared through an ensemble method known as FR-BCT (Feature Reduction-Based Brightness Contrast Transformation). Test outcomes indicate that TP-ES-SQCVANet performs superior to existing methods, with 99.8% accuracy with FR-BCT and 99.9% accuracy with BCT (Brightness Contrast Transformation). These findings suggest that automatic systems could be more effective than manual systems. By identifying areas of high groundwater potential, the findings of this study can assist in sustainable groundwater resource management.