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. The dataset considered had multicollinearity heteroscedasticity and autocorrelation problems. The metaheuristic algorithms were evaluated by varying the number of population size. Among them, PSO performed better by providing an appropriate number of features with a lower number of iterations. We have analyzed the performance of SVM with different kernels; linear, radial basis function (RBF), sigmoid, and polynomial, as the original SVM is designed only for linear datasets but the hydraulic dataset possesses non-linear characteristics as well. The performance of different kernels in terms of their accuracies is evaluated and recorded. This study showed that RBF performed the best and sigmoid showed the least accuracy for GA, PSO, and ACO algorithms. The performance of KNN is evaluated in terms of accuracies by varying the K-values. It was found that KNN shows low accuracy with a small K-value which then attained a maximum level by increasing K-values, and it finally started decreasing, explicitly, by further enhancing K-values. While comparing the performance of hybrids of GA, PSO, and ACO with SVM and KNN, it was analyzed that KNN performed better with these meta-heuristics with PSO-KNN which performed the best among the baseline models. Thus, the study proposes that PSO-KNN can be utilized as a feature selection technique to obtain optimal data subsets for hydraulic modeling and analysis.

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. Based on the importance of green growth, the study seeks to investigate the progress towards sustainable economic development in Pakistan from 1990 to 2019. The study employs structural equation modeling (SEM) to determine the direct and indirect effects of the variables of the green growth model adopted in the MCED-5. The results of the study indicate that an increase in the net national income of the country leads to increased natural resource depletion. The declining stock of natural capital points towards the difficulty in fulfilling biocapacity sustainability in Pakistan while achieving social progress and declining carbon intensity in the quest for sustainable development. Based on the analysis, it can be claimed that the negative impact of increasing inclusive wealth on natural capital makes Pakistan in environmental terms a weakly sustainable nation. Thus, the conclusion is that Pakistan is following a path of weak sustainability. As a result, there is a need to shift the country's sustainable economic development from weak sustainability to strong sustainability if the increasing natural resource depletion is to be restrained.

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. for total petroleum hydrocarbon (TPH) degradation, alkane monooxygenase (alkb) gene expression and phytotoxicity analysis. A plant-microbe phytoremediation system was established to investigate the bacteria's ability to colonize the plant body and quantification of alkb gene to help withstand TPH stress in soil as well as in hydroponics. A real-time PCR method was developed to analyze bacterial colonization and survival within the plant body. Analysis revealed that J10 efficiently colonized all the tested plant species and expressed alkb gene under hydrocarbon stress ranging between 3.7 × 102-3.9 × 106 in A. thaliana and L. perenne (small), respectively. The colonization was more pronounced in soil as compared to hydroponic system. J10 inoculation reduced phytotoxicity and suggested that inoculation had a positive effect on plant growth under stress conditions as compared to control. L. perenne (small) showed significant TPH removal efficiency (45.6%) followed by L. perenne jumbo (24.5%) and A. thaliana (6.2%). In hydroponics, L. perenne (small) degraded about 28.2% TPH followed by L. perenne (jumbo) as 24.4%. Potential of the indigenously isolated plant endophytes may be exploited further for phytoremediation efficiency and industrial applications.