Amir Hamzeh Haghiabi
Articles written in Journal of Earth System Science
Volume 125 Issue 5 July 2016 pp 985-995
In this paper, multivariate adaptive regression splines (MARS) was developed as a novel soft-computingtechnique for predicting longitudinal dispersion coefficient (DL) in rivers. As mentioned in the literature,experimental dataset related to DL was collected and used for preparing MARS model. Results of MARSmodel were compared with multi-layer neural network model and empirical formulas. To define the mosteffective parameters on DL, the Gamma test was used. Performance of MARS model was assessed bycalculation of standard error indices. Error indices showed that MARS model has suitable performanceand is more accurate compared to multi-layer neural network model and empirical formulas. Results ofthe Gamma test and MARS model showed that flow depth (H) and ratio of the mean velocity to shearvelocity (u/u^∗) were the most effective parameters on the DL.
Volume 126 Issue 2 March 2017 Article ID 0020
In this paper, analytical methods, artificial neural network (ANN) and multivariate adaptive regression splines (MARS) techniques were utilised to estimate the discharge capacity of compound open channels (COC). To this end, related datasets were collected from literature. The results showed that the dividedchannel method with a coefficient of determination (R2) value of 0.76 and root mean square error (RMSE) value of 0.162 has the best performance, among the various analytical methods tested. The performance of applied soft computing models with R2 = 0.97 and RMSE = 0.03 was found to be more accurate than analytical approaches. Comparison of MARS with the ANN model, in terms of developed discrepancy ratio (DDR) index, showed that the accuracy of MARS model was better than that of MLP model. Reviewing the structure of the derived MARS model showed that the longitudinal slope of the channel (S), relative flow depth (Hr) and relative area (Ar) have a high impact on modelling and forecasting the discharge capacity of COCs.
Volume 126 Issue 3 April 2017 Article ID 0039
Withregard to the lack of quality information and data in watersheds, it is of high importance to present a new method for evaluating flood potential. Shannon’s entropy model is a new model in evaluating dangers and it has not yet been used to evaluate flood potential. Therefore, being a new model in determining flood potential, it requires evaluation and investigation in different regions and this study is going to deal with this issue. For to this purpose, 70 flooding areas were recognized and their distribution map was provided by ArcGIS10.2 software in the study area. Information layers of altitude, slope angle, slope aspect, plan curvature, drainage density, distance from the river, topographic wetness index (TWI), lithology, soil type, and land use were recognized as factors affecting flooding and the mentioned maps were provided and digitized by GIS environment. Then, flood susceptibility forecasting map was provided and model accuracy evaluation was conducted using ROC curve and 30% flooding areas express good precision of the model (73.5%) for the study area.