Application of Nonhydraulic Delineation Method of Flood Hazard Areas Using LiDAR-Based Data

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.

Quantifying the Landscape’s Ecological Benefits—An Analysis of the Effect of Land Cover Change on Ecosystem Services

The increasing pressure from land cover change exacerbates the negative effect on ecosystems and ecosystem services (ES). One approach to inform holistic and sustainable management is to quantify the ES provided by the landscape. Using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, this study quantified the sediment retention capacity and water yield potential of different land cover in the Santee River Basin Network in South Carolina, USA. Results showed that vegetated areas provided the highest sediment retention capacity and lowest water yield potential. Also, the simulations demonstrated that keeping the offseason crop areas vegetated by planting cover crops improves the monthly ES provision of the landscape. Retaining the soil within the land area prevents possible contamination and siltation of rivers and streams. On the other hand, low water yield potential translates to low occurrence of surface runoff, which indicates better soil erosion control, regulated soil nutrient absorption and gradual infiltration. The results of this study can be used for landscape sustainability management to assess the possible tradeoffs between ecological conservation and economic development. Furthermore, the generated map of ES can be used to pinpoint the areas where ES are best provided within the landscape.