Alireza Sepahvand; Nasrin Beiranvand; Negar Arjmand
Abstract
Water quality (WQ) is influenced by various variables, including natural ones like rainfall and erosion and human ones like urban, agricultural, and industry operations, that plays a very important role in assessment and determining factors such as environmental conditions, public health, economic and ...
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Water quality (WQ) is influenced by various variables, including natural ones like rainfall and erosion and human ones like urban, agricultural, and industry operations, that plays a very important role in assessment and determining factors such as environmental conditions, public health, economic and social progress and development. Therefore, temporal and spatial trending of water quality is necessary for planning water resource management. In this research, the performance of the six soft computing techniques, including, Random Forest, Reduced Error Pruning Tree (REPt), M5P model, bagging RF, bagging REPt and bagging M5P were compared to estimate the water quality index (WQI) in Khorramabad, Biranshahr and Alashtar sub-watersheds, Lorestan province, Iran. At first, based on water quality data, water quality index (WQI) was calculated and ten distinct water quality parameters (2014 to 2023) were used as input variables and WQI as output. Total data set consists of water quality parameters of three sub-watersheds out of which 70% data used to training and 30% data were used to testing phase. Finally, the models were compared with Correlation Coefficient (C.C.), Root Mean Square Error (RMSE), Maximum Absolute Error (MAE), Taylor diagram and Violin plot box. The obtained results suggest that the BM5P is more accurate to estimate the water quality index (WQI) compared to the M5P, ReepTree and Random Forest (RF) models for the given study area. According to the results of the test part of the BM5P model, it has given us the best result, which are the correlation coefficient, the Root Mean Square Error and the Mean Absolute Error 0.99, 0.2, and 0.15, respectively. Also, the Taylor diagram and violin box plot were concluded that BM5P was the most reliable soft computing technique for the prediction of WQI. Finally, the structure of Artificial Intelligence Techniques (AIT) for modeling is very simple and very less time consumable. Thus, the BM5P model can be useful in the water quality index (WQI) modeling not only for accuracy but also for its time-saving and simple structure compared with other models.
vahid chitsaz; Aliakbar Nazari Samani; Saeed Soltani; sadat feiznia
Abstract
Sediment and erosion are two natural phenomena in watersheds. Due to irregular recording and sampling difficulties, daily data are not available for sediment records. Therefore, decision makers and researchers have to apply interpolating methods to estimated sediment yields. In this study, 30 watershed ...
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Sediment and erosion are two natural phenomena in watersheds. Due to irregular recording and sampling difficulties, daily data are not available for sediment records. Therefore, decision makers and researchers have to apply interpolating methods to estimated sediment yields. In this study, 30 watershed characteristics including physiography, geomorphology, vegetation, climate conditions in 69 watersheds located in the Karoon and Karkheh basins were used to statistical analysis. Based on the principle component analysis, eight characteristics including area, perimeter, river length, relief, mean of elevation at 85% upstream and 15% point of longest flow path and the number of landslide events were selected. Then using Cluster Analysis, six homogenous regions were identified and multiple regression models were applied. Due to constriction of large dames on the studied watersheds, access to the reliable data is a challenges for sediment yield analysis. Based on the sediment-precipitation double-mass curves 29 out of 35 stations were influenced by upstream dam. Results indicated that the effects of large reservoir dams can influence the downstream sediment yield along 98 Km of river length. The results show that in each group a particular combination of variables influence the sediment yields of the watersheds. According to the validation indices (NS and R2) the obtained models have the high performance (R2 = 0.71 and NS=0.72). In general, the physiographic characteristics of the watershed such as length, area, main flow path and relief are more important than other climatic, vegetation and geological factors. The total explain variance by the mentioned variables is 87.3%.
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.
shahin mohammadi; Hamidreza karimzadeh; saeid pourmanafi; Saeed Soltani
Abstract
Soil is one of the most important production factors that has a great impact on human socio-economic life and the process of soil erosion is one of the environmental issues that threatens the environment, natural resources and agriculture. Spatial and temporal information of the soil loss and soil erosion ...
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Soil is one of the most important production factors that has a great impact on human socio-economic life and the process of soil erosion is one of the environmental issues that threatens the environment, natural resources and agriculture. Spatial and temporal information of the soil loss and soil erosion on the land has a significant role in influencing management practices, soil erosion control and watershed management. Therefore, this study was conducted with the aim of studying the spatial and temporal estimation of soil erosion during 1994, 1999, 2008 and 2015 in the sub-basin of Menderjan with an area of 21100 hectares located in the west of Isfahan province using RS and GIS. In the present study, while conducting field studies, various data and information including the digital elevation model, satellite images, soil, and statistics on rain gauge stations were used as a research tool. Estimation of soil erosion in the study area was carried out using RUSLE Model. The results of this study showed that the amount of soil erosion in 1994, 1999, 2008 and 2015 was 0.001 to 233, 0.001 to 297, 0.001 to 231 and 0.001 to 215 "ton/”ha.year”. The topography factor in the study area with the correlation coefficient of 80% had the greatest effect on the estimation of annual soil erosion by the RUSLE model. This research corroborate the effectiveness of modern GIS technologies and remote sensing in temporal simulation for quantitative, exact, and point-to-point estimates in the whole area to obtain soil erosion content.
