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.
Ali Haghizadeh; Lila Ghasemi
Abstract
In recent years, the flood situation of headwaters of the Dez River in Lorestan Province has increased. This is due to various factors such as climate change, reduction of vegetation cover, and increase in construction in the riparian zone. In 2022, floods occurred several times in the Dez headwaters ...
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In recent years, the flood situation of headwaters of the Dez River in Lorestan Province has increased. This is due to various factors such as climate change, reduction of vegetation cover, and increase in construction in the riparian zone. In 2022, floods occurred several times in the Dez headwaters in Lorestan province. These floods caused significant damage to life and property. Global conceptual models have been developed for more than two decades and their effectiveness in simulating streamflow has been proven. In this study, simulation of runoff rainfall in Silakhor-Rahimabad watershed was done using three daily (GR4J), monthly (GR2M) and annual (GR1A) models. The Nash-Sutcliffe (Nash), root mean square error (RMSE), and bias criteria were used to evaluate the model performance during the calibration and validation periods. The obtained results were highly significant. The GR1A model has Nash coefficients of 86.1 and 71.7 in both calibration and validation periods, respectively, so this model has a very good performance. For the other two models, the GR2M model and the GR4J model, the Nash coefficients in the two calibration and validation periods are 76.7, 70.2 and 61.4, 86.2, respectively. These coefficients also indicate the good and very good performance of these models in rainfall-runoff simulation. However, considering the satisfactory performance of the two evaluation criteria, RMSE and Bias, in the GR1A model, it can be concluded that the GR1A model had a better performance in simulating rainfall-runoff. Finally, the obtained results indicate that the GR4J, GR2M and GR1A conceptual models are suitable models for simulating the streamflow in the Silakhor-Rahimabad watershed.
Nasrin Beiranvand; Alireza Sepahvand; Ali Haghizadeh
Abstract
In this study, five soft computing techniques, GP-PUK, GP-RBF, M5P, REEP Tree and RF were used to predict the SL in Cham Anjir, Bahram Joo, Kaka Reza and Sarab Syed Ali hydrometry stations in Khorramabad, Biranshahr and Alashtar sub-watersheds, Lorestan province. Total data set consists of rain, discharge ...
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In this study, five soft computing techniques, GP-PUK, GP-RBF, M5P, REEP Tree and RF were used to predict the SL in Cham Anjir, Bahram Joo, Kaka Reza and Sarab Syed Ali hydrometry stations in Khorramabad, Biranshahr and Alashtar sub-watersheds, Lorestan province. Total data set consists of rain, discharge and solute load (SL) of three sub-watersheds out of which 70% data used to training and 30% data were used to testing phase. Finally, the models’ accuracy was assessed using three performance evaluation parameters, which were Correlation Coefficient (C.C.), Root Mean Square Error (RMSE) and Maximum Absolute Error (MAE). Results suggest that GP-PUK and GP-RBF models works well than other modeling approaches in estimating the SL in low and high water-periods. The result showed that, In the high-water period, in Cham Anjir, Sarab Said Ali and Kaka Reza stations the GP-RBF model and in the Bahram Joo station the GP-PUK model with the highest C.C and the lowest error were selected the optimal models in estimating the SL. Also, in the low water period, result shown that in Cham Anjir, Sarab Said Ali and Bahram Joo stations the GP-RBF model and in the Kaka Reza station the GP-PUK model were the best models in estimating the SL. Therefore, these models can be used to estimate the solute load of nearby rivers by/without hydrometry station for the management of the quantity and quality of surface water.
Bahram Mir Derikvand; Alireza Sepahvand; Hossein Zeinivand
Abstract
In recent years, extensive practices have been done on flood control, erosion and sediment in the fields of research and implementation of watershed management. Therefore, the purpose of this study was to assess the effects of watershed management practices on the characteristics of runoff and suspended ...
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In recent years, extensive practices have been done on flood control, erosion and sediment in the fields of research and implementation of watershed management. Therefore, the purpose of this study was to assess the effects of watershed management practices on the characteristics of runoff and suspended sediment load in two subwatersheds in Ghaleh Gol watershed in Lorestan province, Iran. In this research, for comparing the effect of watershed management practices (WMP) on discharge and suspended sediment load (SSL) from both subwatersheds, the flow velocity was measured and the SSL was sampled directly from the beginning of the rainfall events until the end of them. Results showed that in all measurements, the discharge and suspended sediment load of the southern subwatershed with watershed management practices was higher than the northern subwatershed without such practices. According to the results of ANOVA test, it was found that the difference between discharge peak (P=0.691) and suspended sediment load peak (P=0.840) was not significant in two subwatersheds. Also, according to the results, the difference between specific discharge and specific SSL was not significant (P>0.05). Based on these results, it was found that the implementation of WMP in the study area apparently has no the required performance to reduce the discharge and SSL, and the WMP have lost their performance before the end of their useful life. Therefore, in order to increase the performance of mechanical watershed management practices (MWMP), the biological and biomechanical practices has to be performed simultaneously.
Alireza Sepahvand; Hasan Ahmadi; Aliakbar Nazari Samani; Sebastiano Trevisani
Abstract
The geomorphometric indexes have been widely used for separation of surface landform features in the geomorphology science over the past decades. In this study, Multilayer Perceptron Neural Network (MPNN) was used to provide karstic landform classification. To that regard, initially, geomorphometric ...
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The geomorphometric indexes have been widely used for separation of surface landform features in the geomorphology science over the past decades. In this study, Multilayer Perceptron Neural Network (MPNN) was used to provide karstic landform classification. To that regard, initially, geomorphometric indicators were extracted from Digital Elevation Model (DEM), and then these indexes were used as neurons of input layer in artificial neural network. Furthermore, the box plots were applied to analyze the relationship between karstic landforms (such as dolines, hills, karstic plains, karstic valley and headland) and geomorphometric indexes. The results showed that 34, 6.9, 1.07, 48.5, 9.51 percent of the studying area are spatially covered by valleys, plains, dolines, highlands and hills respectively. It has also been found that the optimal structure of artificial neural networks for classification of landform is model No. 12-9-1 by having the learning rate 0.1 and 87.18 percent of determination coefficient. Also, it should be noted that the accuracy of the innovative method for classification of karstic landform is 90.58 percent. The analysis revealed that variations in geomorphometric indexes are very visible in the landform of hills, highlands and karstic valleys, whereas there are slightly overlapping in the plains and dolines.
