| 1. |
- Arora, Aman, et al.
(författare)
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Optimization of state-of-the-art fuzzy-metaheuristic ANFIS-based machine learning models for flood susceptibility prediction mapping in the Middle Ganga Plain, India
- 2021
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Ingår i: Science of the Total Environment. - : Elsevier. - 0048-9697 .- 1879-1026. ; 750
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Tidskriftsartikel (refereegranskat)abstract
- This study is an attempt to quantitatively test and compare novel advanced-machine learning algorithms in terms of their performance in achieving the goal of predicting flood susceptible areas in a low altitudinal range, sub-tropical floodplain environmental setting, like that prevailing in the Middle Ganga Plain (MGP), India. This part of the Ganga floodplain region, which under the influence of undergoing active tectonic regime related subsidence, is the hotbed of annual flood disaster. This makes the region one of the best natural laboratories to test the flood susceptibility models for establishing a universalization of such models in low relief highly flood prone areas. Based on highly sophisticated flood inventory archived for this region, and 12 flood conditioning factors viz. annual rainfall, soil type, stream density, distance from stream, distance from road, Topographic Wetness Index (TWI), altitude, slope aspect, slope, curvature, land use/land cover, and geomorphology, an advanced novel hybrid model Adaptive Neuro Fuzzy Inference System (ANFIS), and three metaheuristic models-based ensembles with ANFIS namely ANFIS-GA (Genetic Algorithm), ANFIS-DE (Differential Evolution), and ANFIS-PSO (Particle Swarm Optimization), have been applied for zonation of the flood susceptible areas. The flood inventory dataset, prepared by collected flood samples, were apportioned into 70:30 classes to prepare training and validation datasets. One independent validation method, the Area-Under Receiver Operating Characteristic (AUROC) Curve, and other 11 cut-off-dependent model evaluation metrices have helped to conclude that the ANIFS-GA has outperformed other three models with highest success rate AUC = 0.922 and prediction rate AUC = 0.924. The accuracy was also found to be highest for ANFIS-GA during training (0.886) & validation (0.883). Better performance of ANIFS-GA than the individual models as well as some ensemble models suggests and warrants further study in this topoclimatic environment using other classes of susceptibility models. This will further help establishing a benchmark model with capability of highest accuracy and sensitivity performance in the similar topographic and climatic setting taking assumption of the quality of input parameters as constant.
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| 2. |
- Bui, Dieu Tien, et al.
(författare)
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A Hybrid Intelligence Approach to Enhance the Prediction Accuracy of Local Scour Depth at Complex Bridge Piers
- 2020
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Ingår i: Sustainability. - Switzerland : MDPI. - 2071-1050. ; 12:3, s. 1-24
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Tidskriftsartikel (refereegranskat)abstract
- Local scour depth at complex piers (LSCP) cause expensive costs when constructing bridges. In this study, a hybrid artificial intelligence approach of random subspace (RS) meta classifier, based on the reduced error pruning tree (REPTree) base classifier, namely RS-REPTree, was proposed to predict the LSCP. A total of 122 laboratory datasets were used and portioned into training (70%: 85 cases) and validation (30%: 37 cases) datasets for modeling and validation processes, respectively. The statistical metrics such as mean absolute error (MAE), root mean squared error (RMSE), correlation coefficient (R), and Taylor diagram were used to check the goodness-of-fit and performance of the proposed model. The capability of this model was assessed and compared with four state-of-the-art soft-computing benchmark algorithms, including artificial neural network (ANN), support vector machine (SVM), M5P, and REPTree, along with two empirical models, including the Florida Department of Transportation (FDOT) and Hydraulic Engineering Circular No. 18 (HEC-18). The findings showed that machine learning algorithms had the highest goodness-of-fit and prediction accuracy (0.885 < R < 0.945) in comparison to the other models. The results of sensitivity analysis by the proposed model indicated that pile cap location (Y) was a more sensitive factor for LSCP among other factors. The result also depicted that the RS-REPTree ensemble model (R = 0.945) could well enhance the prediction power of the REPTree base classifier (R = 0.885). Therefore, the proposed model can be useful as a promising technique to predict the LSCP.
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| 3. |
- Bui, Dieu Tien, et al.
(författare)
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Effects of Inter-Basin Water Transfer on Water Flow Condition of Destination Basin
- 2020
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Ingår i: Sustainability. - Switzerland : MDPI. - 2071-1050. ; 12:338
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Tidskriftsartikel (refereegranskat)abstract
- In recent years, the intensification of drought and unsustainable management and use of water resources have caused a significant decline in the water level of the Urmia Lake in the northwest of Iran. This condition has affected the lake, approaching an irreversible point such that many projects have been implemented and are being implemented to save the natural condition of the Urmia Lake, among which the inter-basin water transfer (IBWT) project from the Zab River to the lake could be considered an important project. The main aim of this research is the evaluation of the IBWT project effects on the Gadar destination basin. Simulations of the geometrical properties of the river, including the bed and flow, have been performed, and the land cover and flood map were overlapped in order to specify the areas prone to flood after implementing the IBWT project. The results showed that with the implementation of this project, the discharge of the Gadar River was approximately tripled and the water level of the river rose 1 m above the average. In April, May, and June, about 952.92, 1458.36, and 731.43 ha of land adjacent to the river (floodplain) will be inundated by flood, respectively. Results also indicated that UNESCO’s criteria No. 3 (“a comprehensive environmental impact assessment must indicate that the project will not substantially degrade the environmental quality within the area of origin or the area of delivery”) and No. 5 (“the net benefits from the transfer must be shared equitably between the area of origin and the area of water delivery”) have been violated by implementing this project in the study area. The findings could help the local government and other decision-makers to better understand the effects of the IBWT projects on the physical and hydrodynamic processes of the Gadar River as a destination basin.
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| 4. |
- Pham, Binh Thai, et al.
(författare)
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A Comparative Study of Kernel Logistic Regression, Radial Basis Function Classifier, Multinomial Naïve Bayes, and Logistic Model Tree for Flash Flood Susceptibility Mapping
- 2020
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Ingår i: Water. - Switzerland : MDPI. - 2073-4441. ; 12:1, s. 1-21
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Tidskriftsartikel (refereegranskat)abstract
- Risk of flash floods is currently an important problem in many parts of Vietnam. In this study, we used four machine-learning methods, namely Kernel Logistic Regression (KLR), Radial Basis Function Classifier (RBFC), Multinomial Naïve Bayes (NBM), and Logistic Model Tree (LMT) to generate flash flood susceptibility maps at the minor part of Nghe An province of the Center region (Vietnam) where recurrent flood problems are being experienced. Performance of these four methods was evaluated to select the best method for flash flood susceptibility mapping. In the model studies, ten flash flood conditioning factors, namely soil, slope, curvature, river density, flow direction, distance from rivers, elevation, aspect, land use, and geology, were chosen based on topography and geo-environmental conditions of the site. For the validation of models, the area under Receiver Operating Characteristic (ROC), Area Under Curve (AUC), and various statistical indices were used. The results indicated that performance of all the models is good for generating flash flood susceptibility maps (AUC = 0.983–0.988). However, performance of LMT model is the best among the four methods (LMT: AUC = 0.988; KLR: AUC = 0.985; RBFC: AUC = 0.984; and NBM: AUC = 0.983). The present study would be useful for the construction of accurate flash flood susceptibility maps with the objectives of identifying flood-susceptible areas/zones for proper flash flood risk management.
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| 5. |
- Rahmati, Omid, et al.
(författare)
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TET : An automated tool for evaluating suitable check-dam sites based on sediment trapping efficiency
- 2020
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Ingår i: Journal of Cleaner Production. - : Elsevier BV. - 0959-6526 .- 1879-1786. ; 266
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Tidskriftsartikel (refereegranskat)abstract
- Sediment control is important for supplying clean water. Although check dams control sediment yield, site selection for check dams based on the sediment trapping efficiency (TE) is often complex and time-consuming. Currently, a multi-step trial-and-error process is used to find the optimal sediment TE for check dam construction, which limits this approach in practice. To cope with this challenge, we developed a user-friendly, cost- and time-efficient geographic information system (GIS)-based tool, the trap efficiency tool (TET), in the Python programming language. We applied the tool to two watersheds, the Hableh-Rud and the Poldokhtar, in Iran. To identify suitable sites for check dams, four scenarios (S1: TE ≥ 60%, S2: TE ≥ 70%, S3: TE ≥ 80%, S4: TE ≥ 90%) were tested. TET identified 189, 117, 96, and 77 suitable sites for building check dams in S1, S2, S3, and S4, respectively, in the Hableh-Rud watershed, and 346, 204, 156, and 60 sites in S1, S2, S3, and S4, respectively, in the Poldokhtar watershed. Evaluation of 136 existing check dams in the Hableh-Rud watershed indicated that only 10% and 5% were well-located and these were in the TE classes of 80–90% and ≥90%, respectively. In the Poldokhtar watershed, only 11% and 8% of the 207 existing check dams fell into TE classes 80–90% and ≥90%, respectively. Thus, the conventional approach for locating suitable sites at which check dams should be constructed is not effective at reaching suitable sediment control efficiency. Importantly, TET provides valuable insights for site selection of check dams and can help decision makers avoid monetary losses incurred by inefficient check-dam performance.
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