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Flooding is one of the most significant environmental challenges and can easily cause fatal incidents and economic losses. Flood reduction is costly and time-consuming task; so it is necessary to accurately detect flood susceptible areas. This work presents an effective flood susceptibility mapping framework by involving an adaptive neuro-fuzzy inference system (ANFIS) with two metaheuristic methods of biogeography based optimization (BBO) and imperialistic competitive algorithm (ICA). A total of 13 flood influencing factors, including slope, altitude, aspect, curvature, topographic wetness index, stream power index, sediment transport index, distance to river, landuse, normalized difference vegetation index, lithology, rainfall and soil type, were used in the proposed framework for spatial modeling and Dingnan County in China was selected for the application of the proposed methods due to data availability. There are 115 flood occurrences in the study area which were randomly separated into training (70% of the total) and verification (30%) sets. To perform the proposed framework, the step-wise weight assessment ratio analysis algorithm is first used to evaluate the correlation between influencing factors and floods. Then, two ensemble methods of ANFIS-BBO and ANFIS-ICA are constructed for spatial prediction and producing flood susceptibility maps. Finally, these resultant maps are assessed in terms of several statistical and error measures, including receiver operating characteristic (ROC) curve and area under the ROC curve (AUC), root-mean-square error (RMSE). The experimental results demonstrated that the two ensemble methods were more effective than ANFIS in the study area. For instance, the predictive AUC values of 0.8407, 0.9045 and 0.9044 were achieved by the methods of ANFIS, ANFIS-BBO and ANFIS-ICA, respectively. Moreover, the RMSE values for ANFIS, ANFIS-BBO and ANFIS-ICA using the verification set were 0.3100, 0.2730 and 0.2700, respectively. In addition, as regards ANFIS-BBO and ANFIS-ICA, a total areas of 39.30% and 35.39% were classified as highly susceptible to flooding. Therefore, the proposed ensemble framework can be used for flood susceptibility mapping in other sites with similar geo-environmental characteristics for taking measures to manage and prevent flood damages. Image 1 • Prediction power of two novel ensemble methods for flood susceptibility mapping. • The proposed ensemble methods can improve the prediction performance of ANFIS. • The proposed methods can accurately produce flood susceptibility maps for mitigation and management. [ABSTRACT FROM AUTHOR]

Inasmuch as channels are designed to mitigate continues sedimentation, sediment transport models have been developed to calculate flow velocity to keep sediment particles in motion. In order to promote the computation capability of sediment transport models, recently machine learning algorithms have attracted interests, extensively. However, accuracy of such a model is attributed to the range of data and applied technique for model construction. For this purpose, the current study scrutinizes the applicability of "non-deposition with deposited bed" (NDB) concept for design of large channels applying hybrid machine learning algorithms. Through the modeling, firstly, conventional adaptive neuro-fuzzy inference system (ANFIS) technique is applied to develop a stand-alone model. In furtherance of improving the model's performance, the ANFIS is hybridized with invasive weed optimization (IWO) algorithm to construct a hybrid ANFIS-IWO model. As a benchmark, the ANFIS is further hybridized with classical genetic algorithm (GA) to compare with ANFIS-IWO outcomes. Furthermore, the developed machine learning models are compared to multigene genetic programming (MGP) and particle swarm optimization (PSO) stand-alone machine learning results reported in the literature and classical regression models by means of variety of statistical performance measurements. Hybridization of ANFIS with IWO, enhances its accuracy with a factor of 30%. Respecting to the models performance examination, the ANFIS-IWO model is found superior to its alternatives for sediment transport computation. The thickness of the deposited bed and deposited bed width are found as effective parameters for sediment transport modeling in open channels with a bed deposit. Image 1 • Non-deposition with deposited bed (NDB) is applied for large channel design. • NDB sediment transport is modeled using wide ranges of experimental data. • ANFIS-IWO is implemented for NDB sediment transport modeling for the first time. • ANFIS-IWO outperforms ANFIS-GA, ANFIS, MGP and PSO models. • Ranges of data, parameters used and applied techniques are important in the modeling. [ABSTRACT FROM AUTHOR]