A comparative analysis of feature selection models for spatial analysis of floods using hybrid metaheuristic and machine learning models.

The research aims to propose a feature selection model for hydraulic analysis as such a model has not been proposed previously. For this purpose, hybrids of three metaheuristic algorithms, particle swarm optimization (PSO), ant colony optimization (ACO), and genetic algorithm (GA) with two machine learning models which are support vector machine (SVM) and K-nearest neighbor (KNN) are employed. The dataset considered was hydraulic having an association with flood and possessed topographic, geo-environmental, and human-induced variables.

Economic growth, social inclusion, and environmental protection: assessing the existence of green growth in Pakistan.

The Fifth Ministerial Conference on Environment and Development in Asia and the Pacific (MCED-5) provided a regional implementation plan to pursue economic development in Asia-Pacific. Achieving environmentally sustainable economic growth or green growth is considered necessary by the ministerial declaration of the conference. The ministerial declaration defines green growth as an approach to sustaining economic growth and employment creation, a prerequisite for effective poverty reduction while coping with natural resource constraints and climate change.

Development of plant-microbe phytoremediation system for petroleum hydrocarbon degradation: An insight from alkb gene expression and phytotoxicity analysis.

Aim of present work was to assess in-planta association potential of isolated endophytic bacterial strain Pseudomonas sp. (J10) (KY608252) with two cultivars of Lolium perenne L. (small & jumbo) and Arabidopsis thaliana L.