Floods are one of nature's most destructive disasters because of the immense damage to land, buildings, and human fatalities. It is difficult to forecast the areas that are vulnerable to flash flooding due to the dynamic and complex nature of the flash floods. Therefore, earlier identification of flash flood susceptible sites can be performed using advanced machine learning models for managing flood disasters. In this study, we applied and assessed two new hybrid ensemble models, namely Dagging and Random Subspace (RS) coupled with Artificial Neural Network (ANN), Random Forest (RF), and Support Vector Machine (SVM) which are the other three state-of-the-art machine learning models for modelling flood susceptibility maps at the Teesta River basin, the northern region of Bangladesh. The application of these models includes twelve flood influencing factors with 413 current and former flooding points, which were transferred in a GIS environment. The information gain ratio, the multicollinearity diagnostics tests were employed to determine the association between the occurrences and flood influential factors. For the validation and the comparison of these models, for the ability to predict the statistical appraisal measures such as Freidman, Wilcoxon signed-rank, and t-paired tests and Receiver Operating Characteristic Curve (ROC) were employed. The value of the Area Under the Curve (AUC) of ROC was above 0.80 for all models. For flood susceptibility modelling, the Dagging model performs superior, followed by RF, the ANN, the SVM, and the RS, then the several benchmark models. The approach and solution-oriented outcomes outlined in this paper will assist state and local authorities as well as policy makers in reducing flood-related threats and will also assist in the implementation of effective mitigation strategies to mitigate future damage.

Flood susceptibility modelling using advanced ensemble machine learning models

The potential of new ensemble machine learning models for effluent quality parameters prediction and related uncertainty

A review of non-point source water pollution modeling for the urban-rural transitional areas of China: Research status and prospect.

Rainfall-runoff events can account for transferring a significant portion of agricultural nonpoint source (NPS) pollution loads to surface water bodies. Nevertheless, mechanistic understanding of intra- and inter-event characteristics and underlying controls are not sufficiently obtained especially under conditions of scarce monitoring data, which hampers the design of appropriate management strategies. This paper presented an approach for an increased understanding of NPS pollution characteristics and controls during events (intra-event) and between events (inter-event) by combining monitoring, modeling, and multivariate statistics. Event-based NPS modeling by the Hydrological Simulation Program-Fortran (HSPF) model was improved of its predictive ability by incorporating intra-event variability based on grab samplings during representative rainfall-runoff events. The validated model was then used to simulate the intra- and inter-event dynamics of flow, suspended sediment, total phosphorus (TP), and total nitrogen (TN) exports in 100 rainfall-runoff events with different event types. The simulation results suggested that the intra-event distribution (e.g., dispersion degree and maximum changing rate) varied between event types. A higher level of event types generally resulted in more sensitive and flashy catchment responses to rainfall-runoff events, while nutrient availability from fertilizer application, antecedent soil moisture, and precipitation intensity also drove the intra- and inter-event patterns. Load duration curves and flow-weighted concentrations showed that the TN pollution in the study area was extremely severe, especially for the high flow regime and V-E heavy (i.e. very heavy and extremely heavy) events which should be paid particular attention in designing vegetation- and storage-based conservation practices. The principal component analysis revealed that controlling factors varied among event types. Sediment and TP exports were transport-limited in medium and heavy events; TN export was transport-, pre-condition-, and source-limited in medium, heavy, and V-E heavy events, respectively. The combined framework offers insights into agricultural NPS pollution controls from an event-based prospective.

Intra- and inter-event characteristics and controlling factors of agricultural nonpoint source pollution under different types of rainfall-runoff events

Floods are considered as one of nature's most destructive fluvio-hydrological extremes because of the massive damage to agricultural land, roads and buildings and human fatalities. Rapid development of unplanned infrastructural conveniences and unplanned anthropogenic activities, the frequency and intensity of floods have been accelerated in recent years. Therefore, flood susceptibility analysis is considered as an important flood management approach. Identification of flood susceptibility areas has been performed by applying advanced machine learning (ML) algorithms (random forest (RF), support vector machine (SVM) and extreme gradient boosting (XGBoost)) at the lower part of Raidak river basin. The flood susceptibility maps have been generated based on 14 different flood conditioning factors. Models are evaluated in a conventional way using ROC (receiver operating characteristics) curve. The AUC value of ROC is above 0.80 for all models and XGBoost depicts the highest efficacy (AUC ​= ​0.92). Friedman test and Wilcoxon Signed rank test have been used to measure the statistical variances among the applied models. Models proficiently show that the upper part of Raidak river basin is a less flood probable region whereas the eastern and some middle parts have high flood probability. Around 27% area (285.39 sq.km) within the river basin is highly flood prone (based on XGBoost model) due to the fast changing dynamic landscape and large scale human intervention. The important outcomes of this research will definitely assist the local administrators to take proper sustainable management plans for the reduction of future damages. 

Flood susceptibility zonation using advanced ensemble machine learning models within Himalayan foreland basin

Hydrologic modeling is usually applied to two scenarios: continuous and event-based modeling, between which hydrologists often neglect the significant differences in model application. In this study, a comparison-based procedure concerning parameter estimation and uncertainty analysis is presented based on the Hydrological Simulation Program–Fortran (HSPF) model. Calibrated parameters related to base flow and moisture distribution showed marked differences between the continuous and event-based modeling. Results of the regionalized sensitivity analysis identified event-dependent parameters and showed that gravity drainage and storage outflow were the primary runoff generation processes for both scenarios. The overall performance of the event-based simulation was better than that of the daily simulation for streamflow based on the generalized likelihood uncertainty estimation (GLUE). The GLUE analysis also indicated that the performance of the continuous model was limited by several extreme events and low flows. In the event-based scenario, the HSPF model performances decreased as the precipitation became intense in the event-based modeling. The structure error of the HSFP model was recognized at the initial phase of the rainfall-event period. This study presents a valuable opportunity to understand dominant controls in different hydrologic scenario and guide the application of the HSPF model.

https://www.mdpi.com/2073-4441/11/1/171/pdf

Parameter Estimation and Uncertainty Analysis: A Comparison between Continuous and Event-Based Modeling of Streamflow Based on the Hydrological Simulation Program–Fortran (HSPF) Model

Genistein is an isoflavone phytoestrogen that has been shown to improve obesity; however, the underlying molecular mechanisms involved therein have not been clearly elucidated. In this study, we administered genistein to high-fat diet-induced obese mice to investigate its effect on hepatic gluconeogenesis. The results showed that genistein treatment significantly inhibited body weight gain, hyperglycemia, and adipose and hepatic lipid deposition in high-fat diet-induced obese mice. Glucose tolerance test (GTT), insulin tolerance test (ITT) and pyruvate tolerance test (PTT) showed that genistein treatment significantly inhibited gluconeogenesis and improved insulin resistance in obese mice. In addition, this study also found that genistein could promote the expression of miR-451 in vitro and in vivo, and the dual-luciferase reporter system showed that G6pc (glucose-6-phosphatase) may be a target gene of miR-451. Both genistein treatment and in vivo injection of miR-451 agomir significantly inhibited gluconeogenesis and inhibited the expression of G6pc and Gk (glycerol kinase, a known target gene of miR-451). In conclusion, genistein may inhibit gluconeogenesis in obese mice by regulating the expression of Gk and G6pc through miR-451. These results may provide insights into the functions of miR-451 and food-derived phytoestrogens in ameliorating and preventing gluconeogenesis-related diseases.

/pdf/genistein-alleviates-high-fat-diet-induced-obesity-by-3nw3dg9y.pdf

Genistein Alleviates High-Fat Diet-Induced Obesity by Inhibiting the Process of Gluconeogenesis in Mice

Uncertainties in watershed modeling need to be addressed to increase predictive accuracy and enhance model interpretation. Nevertheless, event-based characteristics incorporating management concerns were rarely considered in the sediment modeling within an uncertainty assessment framework, which might lead to biased decision makings in watershed management. In this study, the event-based likelihood measure was developed to improve the understanding and prediction of the sediment dynamics in a data-scarce catchment in the Three Gorge Reservoir Region (TGRR), China. The uncertainty assessment is based on the Generalized Likelihood Uncertainty Estimation (GLUE) approach applied to the Hydrological Simulation Program - Fortran (HSPF) model. The impact of the Nash-Sutcliffe efficiency (NSE) as the traditionally used likelihood measure was also investigated as a comparison. The results showed that the event-based likelihood measure had advantages in judging critical parameters. Sensitive parameters in the soil erosion and channel transport and their impacts on different phrases of sedimentograph were recognized. The event-based likelihood measure was much more discriminating by efficiently eliminating unsatisfactory parameter sets. Better validity in efficiently reducing the parameter uncertainty and a higher identifiability was achieved by the newly developed likelihood measure. The event-based likelihood measure resulted in more progressive reductions in predictive uncertainty in terms of uncertainty bounds and evaluation criteria. The proposed uncertainty assessment framework yields improved predictions and an increased understanding of sediment response behaviors in a data-limited environment.

Event-based uncertainty assessment of sediment modeling in a data-scarce catchment

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Intra- and inter-event characteristics and controlling factors of agricultural nonpoint source pollution under different types of rainfall-runoff events

Parameter Estimation and Uncertainty Analysis: A Comparison between Continuous and Event-Based Modeling of Streamflow Based on the Hydrological Simulation Program–Fortran (HSPF) Model

Genistein Alleviates High-Fat Diet-Induced Obesity by Inhibiting the Process of Gluconeogenesis in Mice

Event-based uncertainty assessment of sediment modeling in a data-scarce catchment