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Viet-Ha Nhu Khabat Khosravi James R. Cooper Mahshid Karimi Ozgur Kisi Binh Thai Pham 《水文科学杂志》2020,65(12):2116-2127
ABSTRACT The predictive capability of a new artificial intelligence method, random subspace (RS), for the prediction of suspended sediment load in rivers was compared with commonly used methods: random forest (RF) and two support vector machine (SVM) models using a radial basis function kernel (SVM-RBF) and a normalized polynomial kernel (SVM-NPK). Using river discharge, rainfall and river stage data from the Haraz River, Iran, the results revealed: (a) the RS model provided a superior predictive accuracy (NSE = 0.83) to SVM-RBF (NSE = 0.80), SVM-NPK (NSE = 0.78) and RF (NSE = 0.68), corresponding to very good, good, satisfactory and unsatisfactory accuracies in load prediction; (b) the RBF kernel outperformed the NPK kernel; (c) the predictive capability was most sensitive to gamma and epsilon in SVM models, maximum depth of a tree and the number of features in RF models, classifier type, number of trees and subspace size in RS models; and (d) suspended sediment loads were most closely correlated with river discharge (PCC = 0.76). Overall, the results show that RS models have great potential in data poor watersheds, such as that studied here, to produce strong predictions of suspended load based on monthly records of river discharge, rainfall depth and river stage alone. 相似文献
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Dou Jie Yunus Ali P. Xu Yueren Zhu Zhongfan Chen Chi-Wen Sahana Mehebub Khosravi Khabat Yang Yong Pham Binh Thai 《Natural Hazards》2019,97(2):579-609
Natural Hazards - This study investigated the characteristics of rainfall-triggered landslides during the Typhoon Bilis in the Dongjiang Reservoir Watershed, China. The comparative shallow... 相似文献
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Khabat Khosravi Amir Hooshang Nezamivand Chegini James Cooper Luca Mao Mahmood Habibnejad Kaka Shahedi Andrew Binns 《国际泥沙研究》2021,36(2):229-234
Dam break flows and resulting river bed erosion can have disastrous impacts on human safety,infrastructure,and environmental quality.However,there is a lack of research on the mobility of non-uniform sediment mixtures resulting from dam break flows and how these differ from uniform sized sediment.In this paper,laboratory flume experiments revealed that coarse and fine fractions in non-uniform sediment had a higher and a lower bed-load parameter,respectively,than uniform sediments of the same size.Thus,the finer fractions were more stable and the coarser fractions were more erodible in a nonuniform bed compared to a uniform-grained bed.These differences can be explained by the hiding and protrusion of these fractions,respectively.By investigating changes in mobility of the mixed-size fractions with reservoir water levels,the results revealed that at low water levels,when the coarser fractions were only just mobile,the bed-load parameter of the finer fractions was higher than the coarser fractions.The opposite was observed at a higher water level,when a significant proportion of the coarsest fractions was mobilized.The higher protrusion of these grains had an important effect on their mobility relative to the finer grains.The transported sediment on these mixed-sized beds was coarser than the initial bed sediment,and became coarser with an increase in reservoir water level. 相似文献
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AbstractIn this study, we introduced novel hybrid of evidence believe function (EBF) with logistic regression (EBF-LR) and logistic model tree (EBF-LMT) for landslide susceptibility modelling. Fourteen conditioning factors were selected, including slope aspect, elevation, slope angle, profile curvature, plan curvature, topographic wetness index (TWI), stream sediment transport index (STI), stream power index (SPI), distance to rivers, distance to faults, distance to roads, lithology, normalized difference vegetation index (NDVI), and land use. The importance of factors was assessed using correlation attribute evaluation method. Finally, the performance of three models was evaluated using the area under the curve (AUC). The validation process indicated that the EBF-LMT model acquired the highest AUC for the training (84.7%) and validation (76.5%) datasets, followed by EBF-LR and EBF models. Our result also confirmed that combination of a decision tree-logistic regression-based algorithm with a bivariate statistical model lead to enhance the prediction power of individual landslide models. 相似文献
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Phuong Thao Thi Ngo Mahdi Panahi Khabat Khosravi Omid Ghorbanzadeh Narges Kariminejad Artemi Cerda Saro Lee 《地学前缘(英文版)》2021,12(2):505-519
The identification of landslide-prone areas is an essential step in landslide hazard assessment and mitigation of landslide-related losses.In this study,we applied two novel deep learning algorithms,the recurrent neural network(RNN)and convolutional neural network(CNN),for national-scale landslide susceptibility mapping of Iran.We prepared a dataset comprising 4069 historical landslide locations and 11 conditioning factors(altitude,slope degree,profile curvature,distance to river,aspect,plan curvature,distance to road,distance to fault,rainfall,geology and land-sue)to construct a geospatial database and divided the data into the training and the testing dataset.We then developed RNN and CNN algorithms to generate landslide susceptibility maps of Iran using the training dataset.We calculated the receiver operating characteristic(ROC)curve and used the area under the curve(AUC)for the quantitative evaluation of the landslide susceptibility maps using the testing dataset.Better performance in both the training and testing phases was provided by the RNN algorithm(AUC=0.88)than by the CNN algorithm(AUC=0.85).Finally,we calculated areas of susceptibility for each province and found that 6%and 14%of the land area of Iran is very highly and highly susceptible to future landslide events,respectively,with the highest susceptibility in Chaharmahal and Bakhtiari Province(33.8%).About 31%of cities of Iran are located in areas with high and very high landslide susceptibility.The results of the present study will be useful for the development of landslide hazard mitigation strategies. 相似文献
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Khabat Khosravi Ebrahim Nohani Edris Maroufinia Hamid Reza Pourghasemi 《Natural Hazards》2016,82(2):947-961
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Khabat Khosravi Rahim Barzegar Shaghayegh Miraki Jan Adamowski Prasad Daggupati Mohammad Reza Alizadeh Binh Thai Pham Mohammad Taghi Alami 《Ground water》2020,58(5):723-734
While it remains the primary source of safe drinking and irrigation water in northwest Iran's Maku Plain, the region's groundwater is prone to fluoride contamination. Accordingly, modeling techniques to accurately predict groundwater fluoride concentration are required. The current paper advances several novel data mining algorithms including Lazy learners [instance-based K-nearest neighbors (IBK); locally weighted learning (LWL); and KStar], a tree-based algorithm (M5P), and a meta classifier algorithm [regression by discretization (RBD)] to predict groundwater fluoride concentration. Drawing on several groundwater quality variables (e.g., concentrations), measured in each of 143 samples collected between 2004 and 2008, several models predicting groundwater fluoride concentrations were developed. The full dataset was divided into two subsets: 70% for model training (calibration) and 30% for model evaluation (validation). Models were validated using several statistical evaluation criteria and three visual evaluation approaches (i.e., scatter plots, Taylor and Violin diagrams). Although Na+ and Ca2+ showed the greatest positive and negative correlations with fluoride (r = 0.59 and −0.39, respectively), they were insufficient to reliably predict fluoride levels; therefore, other water quality variables, including those weakly correlated with fluoride, should be considered as inputs for fluoride prediction. The IBK model outperformed other models in fluoride contamination prediction, followed by KStar, RBD, M5P, and LWL. The RBD and M5P models were the least accurate in terms of predicting peaks in fluoride concentration values. Results of the current study can be used to support practical and sustainable management of water and groundwater resources. 相似文献
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Binh T. Pham Indra Prakash Khabat Khosravi Kamran Chapi Phan T. Trinh Trinh Q. Ngo 《国际地球制图》2013,28(13):1385-1407
AbstractIn this study, the main goal is to compare the predictive capability of Support Vector Machines (SVM) with four Bayesian algorithms namely Naïve Bayes Tree (NBT), Bayes network (BN), Naïve Bayes (NB), Decision Table Naïve Bayes (DTNB) for identifying landslide susceptibility zones in Pauri Garhwal district (India). First, landslide inventory map was built using 1295 historical landslide data, then in total sixteen influencing factors were selected and tested for landslide susceptibility modelling. Performance of the model was evaluated and compared using Statistical based index methods, Area under the Receiver Operating Characteristic (ROC) curve named AUC, and Chi-square method. Analysis results show that that the SVM has the highest prediction capability, followed by the NBT, DTNBT, BN and NB, respectively. Thus, this study confirms that the SVM is one of the benchmark models for the assessment of susceptibility of landslides. 相似文献
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Sinan Q. Salih Ahmad Sharafati Khabat Khosravi Hossam Faris Ozgur Kisi Hai Tao 《水文科学杂志》2020,65(4):624-637
ABSTRACTSuspended sediment load (SSL) is one of the essential hydrological processes that affects river engineering sustainability. Sediment has a major influence on the operation of dams and reservoir capacity. This investigation is aimed at exploring a new version of machine learning models (i.e. data mining), including M5P, attribute selected classifier (AS M5P), M5Rule (M5R), and K Star (KS) models for SSL prediction at the Trenton meteorological station on the Delaware River, USA. Different input scenarios were examined based on the river flow discharge and sediment load database. The performance of the applied data mining models was evaluated using various statistical metrics and graphical presentation. Among the applied data mining models, the M5P model gave a superior prediction result. The current and one-day lead time river flow and sediment load were the influential predictors for one-day-ahead SSL prediction. Overall, the applied data mining models achieved excellent predictions of the SSL process. 相似文献
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