Asghar Rahmani; Fereydoon Sarmadian; Sayed Roholla Mousavi; Seyyed Erfan Khamoshi
Abstract
Conventional soil mapping is related to High density sampling, affected by scale and expert knowledge So using of new data mining methods in digital soil properties mapping was the main aim of this study for resolving conventional soil survey problems. In this research, 62 surface soil samples based ...
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Conventional soil mapping is related to High density sampling, affected by scale and expert knowledge So using of new data mining methods in digital soil properties mapping was the main aim of this study for resolving conventional soil survey problems. In this research, 62 surface soil samples based on regular grid and expert knowledge opinion were selected after that soil organic carbon(SOC), clay content and CaCO3 were determined in some part of Dryland Kuhin region with area of 372 ha. Data sets were divided to two 80%(calibration) and 20%(validation), respectively. From digital elevation model with 10-meter spatial resolution were derived 19 geomorphometric attribute in SAGA GIS software. Three geomorphometric covariate included TPI, TRI, DEM and landform map unit were chosen PCA and expert knowledge. RStudio and SoLIM Solution software were used for random forest (RF) and fuzzy logic modelling, respectively. The RF modelling results show that for SOC, clay and CaCO3 based on determination coefficient (R2) had 0.63,0.75,0.63 and RMSE 0.17,7.5,5.77 percentage and for SoLIM method revealed that R2 0.47,0.42,0.42 and RMSE 0.2,8.08,4.68 percentage, respectively. Generally, the RF model with creating nonlinear relationship among soil properties and environmental covariate can predicted digital map with appropriate precision for management and sustainable land utilization
Eisa Gholami; Mehdi Vatakhah; Seyed Jalil Alavi
Abstract
Due to the lack of information in most of the watersheds, many researchers attempt to use spatial analysis within Geographic Information System (GIS) in hydrological and Flood Prone (FP) area studies. The present study was designed to compare the efficiency of three models i.e. Support Vector Machine ...
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Due to the lack of information in most of the watersheds, many researchers attempt to use spatial analysis within Geographic Information System (GIS) in hydrological and Flood Prone (FP) area studies. The present study was designed to compare the efficiency of three models i.e. Support Vector Machine (SVM), Generalized Linear Model (GLM) and Generalized Additive Model (GAM) for preparing the flood susceptibility mapping in Guilan province, Iran. For this purpose, slope, aspect, plan curvature, elevation, distance from the river, drainage density, geology, land use, Topographic Wetness Index (TWI) and Stream Power Index (SPI) layers were derived in GIS (ArcGIS and SAGA-GIS). Using 220 flood locations, 70% and 30% out of total flood locations were then used to calibrate and to validate the performance of the models, respectively. The evaluation results of the models accuracy using the area under the curve (AUC) and Kappa indices showed that in terms of AUC, the SVM with 0.835 and the GAM with 0.827, and the GLM with of 0.79 performed very good and good classes, respectively. In terms of Kappa index, the SVM with 0.58, GAM with 0.53 and GLM with 0.48 are performed good and acceptable classes, respectively. Therefore, based on the mentioned indices, the SVM superior to other two models for identifying the flood susceptibility areas.
Mohammad Taghi avand; Saeed Janizadeh; Mohsen Farzin
Abstract
Increasing population and agricultural development need dramatically water resources groundwater resources, therefore, are increasingly being considered, especially in arid and semi-arid regions. Aim of this research is mapping potential of groundwater resources on Yasouj-Sisakht region using data mining ...
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Increasing population and agricultural development need dramatically water resources groundwater resources, therefore, are increasingly being considered, especially in arid and semi-arid regions. Aim of this research is mapping potential of groundwater resources on Yasouj-Sisakht region using data mining method Random Forest (RF) and Generalized Linear Statistical Model (GLM). For this purpose. For this purpose, information layers including slope, slope direction, slope length, aspect, topographic wetness index (TWI), distance from fault, distance from the stream, rainfall, land use, lithology, topographic position index (TPI) and stream power index (SPI) as the main factors influencing groundwater potential were identified and developed in ArcGIS and SAGAGIS software. From the distribution of 263 springs in the area, 70% (253 springs) were used as educational springs and 30% (109 springs) were used as experimental springs. The results showed that the level of underground water with low, medium, high and very high potential in the map of the random forest was 37.78, 22.22, 18.89 and 21.11%, respectively, and in the generalization linear model were 14.49, 32.04, 31.11 and 22.36%, respectively. Moreover, Sensitivity Analysis show that the factors affecting both methods are rainfall, altitude and distance from the fault factors. The accuracy of the data mining models used in this research was also evaluated using a relative performance curve (ROC). The area under curve (AUC) for both RF and GLM models is 92% and 65%, respectively. The accuracy of RF model, therefore, mapping groundwater potential in the study area is more than GLM model.
Seyed Masoud Soleimanpour; Bahram Hedayati; Majid Soufi; Mohammad Javad Rousta; Samad Shadfar
Abstract
One of the important relations in the erosion of gullies is to study the threshold of erosion creation and expansion. In recent decade, creation of new knowledge in determination of relation between variables was led to develop prediction methods in different science and therefore, investigating the ...
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One of the important relations in the erosion of gullies is to study the threshold of erosion creation and expansion. In recent decade, creation of new knowledge in determination of relation between variables was led to develop prediction methods in different science and therefore, investigating the ability to use these methods in erosion and soil conservation is essential. Also, in order to control the erosion of the gully, the mechanism of gullies growth and its dimension expansion, especially increasing in gullies length, has to be carefully determine; for this purpose, the present study aimed to determine the threshold of the most effective factors on increasing the length of the gully, using the K-Means data mining algorithms and the CART decision tree in the Ghazian watershed in the north of Fars province. The results of this study, which include measuring various variables of gullies under field condition and in laboratory, and using data mining techniques, showed that increasing the length of gully in this area depended on the factors of the area above headcut, saturated extract electrical conductivity, forehead slope, canopy cover percentage, and sodium adsorption ratio. It is recommended control of erosion in the foreheads is highly important in reducing the increase in gullies length and sediment production. Also, improving the soils of this area with soil amendments and the restoration of compatible vegetation and the increase in soil organic matter should be considered as the priority of effective actions to control the increasing length of gullies.
Maryam Asadi; Ali Fathzadeh
Abstract
Understanding of suspended sediment rate is one of the fundamental problems in water projects which water engineers consistently have involved with it. Wrong estimations in sediment transport cause incorrect design and destruction of hydraulic systems. Due to the difficulty of suspended sediment measurements, ...
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Understanding of suspended sediment rate is one of the fundamental problems in water projects which water engineers consistently have involved with it. Wrong estimations in sediment transport cause incorrect design and destruction of hydraulic systems. Due to the difficulty of suspended sediment measurements, sediment rating curves is considered as the most common method for estimating the suspended sediment load. The main purpose of this research is the capability challenge of this method in comparison to some state of the art models. In this study, we selected some computational intelligence models (i.e. K-nearest neighbor (KNN), artificial neural networks (ANN), Gaussian processes (GP), decision trees of M5, support vector machine (SVM) and evolutionary support vector machine (ESVM)) and compared them with their sediment rating model in 8 basins located in Gilan province. Daily sediment and discharge data considered as the input data for 30-years. Evaluation of the results indicated that the Gaussian process model has the lowest residual sum of squares (RMSE) and the highest correlation coefficient (r) than the other models.
ali rezazadeh joudi; Mohammad Taghi Sattari
Abstract
Estimation of suspended sediment load or specifying the damages incured as a result of inattention to such estimation is one of the most important and fundamental challenges in river engineering and sediment transport studies. Given the importance and role of sediment in the design and maintenance of ...
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Estimation of suspended sediment load or specifying the damages incured as a result of inattention to such estimation is one of the most important and fundamental challenges in river engineering and sediment transport studies. Given the importance and role of sediment in the design and maintenance of hydraulic structures such as dams, as well its significance in planning for efficient tilization of downstream river and also conservation of nutrients at the upstream of river, many attempts have been made to estimate suspended sediment load of rivers and numerical methods have been developed in this regard. But due to the high cost of most procedures or lack of adequate precision in most common experimental methods, a new method is needed that can estimate suspended sediment load with the greatest possible precision. In this study, the amount of suspended sediment load of Lighvan River has been estimated through support vector regression and k-Nearest neighbor methods. Results indicated the appropriateness of both data mining techniques applied in this study. Among examined methods in this study, the support vector regression method predicted the amount of suspended sediment load in LighvanChay River with representing evaluation indexes such as (CC=0.959, RMSE=43.547(ton/day)) more accurately than K-nearest neighbor method
Fereydoon Sarmadian; Ali Keshavarzi
Abstract
Data mining enables generalization of data of soil to remote areas and which is able to up/down scale of data in wide ranges of level that facilitate the decision-making process of executives. Cation Exchange Capacity (CEC) is one of the most important parameters in soil database and shows the ability ...
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Data mining enables generalization of data of soil to remote areas and which is able to up/down scale of data in wide ranges of level that facilitate the decision-making process of executives. Cation Exchange Capacity (CEC) is one of the most important parameters in soil database and shows the ability of a soil to retention of minerals and pollutants. Due to low organic matter and specific mineralogy of soils in arid and semi-arid regions, measurement of CEC is time consuming and expensive. The objective of this study was to evaluate Coactive Neuro-Fuzzy Inference System (CANFIS) in prediction of CEC in soils of arid and semi-arid regions. A total of 85 soil samples from target area were selected among 440 soil sample database (available reference database) with a ratio of 1:5. Correlation test was conducted to assess the co-linearity of independent variables. Forward regression model was used to determine the most important and influential input parameters on the output results. The results indicated the reliability and high performance of the CANFIS approach in estimation of CEC using easily measurable characteristics, organic material, and satellite images.
