Mehdi Hayatzadeh; Sahar Amini; Ali Fathzadeh; Maryam Asadi
babak shahinejad; zohreh izadi; behzad javadi
Abstract
1-IntroductionRivers are the most important sources of drinking, agricultural, and industrial water supply. In recent decades, however, these resources have become the main receivers of sewer pipelines due to rapid population growth. To evaluate the effects of pollutant discharge on the self-purification ...
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1-IntroductionRivers are the most important sources of drinking, agricultural, and industrial water supply. In recent decades, however, these resources have become the main receivers of sewer pipelines due to rapid population growth. To evaluate the effects of pollutant discharge on the self-purification of rivers, it is necessary to use numerical simulations of water quality. Today, various softwares have been designed for this purpose. One of the most important of these softwares used in this research is the one-dimensional QUAL2Kw model that simulates water quality variables in a steady and non-uniform flow mode. In the present study, the water quality of the Khorramabad River was simulated with the help of this model over a distance of 35 km from the river.2-MethodologyThe range studied in this research is about 35 km along the Khorramabad River from the source of Khorramrud upstream of Robat Namaki village to Chamjangir hydrometric station, which in the geographical coordinates of 33°36'54" to 33°26'37" north latitude; it is located at 48°17'39" to 48°14'38" longitude. Khorramabad River pollution sources are divided into three main parts: urban, industrial, and agricultural. Due to the location of pollutant sources in the river, 5 points along the river were considered as sampling sites, two stations including the beginning and end of the study area, one station in the center of Khorramabad, and the other two stations were selected before the river entered the city and after leaving the city, respectively.In this research, the QUAL2Kw model version 5.1 was used. The required data of the model is divided into three parts: geometric-hydraulic data, qualitative data, and meteorological data. The river was divided into 11 sections and simulated using the hydraulic Manning equation. In this study, important water quality parameters such as DO, CBODf, COD, NO3, EC, and pH and temperature parameters in July and September of 2019 for calibration and validation, respectively, the model was used. Finally, RMSE, NRMSE, and MAE indices were used to evaluate the model in the simulation.3-Results and DiscussionThe results showed that the number of parameters including COD, CBODf, and NO3 increased after the Karganeh tributary joined the river and also the inflow of pollutant sources such as slaughterhouses, municipal treatment plant, milk factory, and alcohol production unit into the river. However, the pH (in both months) and EC (in July) parameters did not change much along the river; in other words, the river can self-purifying these parameters. In the research of Hashemi et al. (2019), for the simulation of the Talar River, the same result was obtained for these two parameters. Babakhani et al. (2019) in a study conducted on the Diwandara River reported a strong correlation between the measured and simulated values of the pH parameter because in surface water the pH value along the path with carbonate and bicarbonate in the path there reaches the equilibrium concentration. According to the results of the research and the fact that the Khorramabad River is used for agricultural and industrial purposes and is not a source of drinking water, at present, there is no limiting factor to achieve this purpose in the study route. Then, the calculation of statistical indices showed that the value of the NRMSE index in the calibration and validation stage of the model is the lowest for pH and equal to 8.83 and 9.22 percent and for EC is 11.05 and 13.86 percent, respectively. The simulation of DO parameter also had fluctuations along the river, while the statistical indices of NRMSE, RMSE, and MAE for this parameter in both calibration and validation stages were obtained at an acceptable level; thus, the above indices in the calibration stage of the model 12.49, 0.917 and 0.72, respectively, and in the validation stage of the model were calculated 24.65, 1.78, 1.55, respectively. In addition, the model was able to simulate the temperature parameter with high accuracy in July (RMSE = 1.92 and MAE = 1.57) and September (RMSE = 2.77 and MAE = 2.5709). Finally, the results of this study indicate the considerable accuracy of the QUAL2Kw model in simulating the above parameters in the Khorramabad River.4-ConclusionsThe results showed that the amount of chemical oxygen demand, biochemical oxygen demand, and nitrate parameters increased due to the entry of effluents from industrial pollutants. Besides, the evaluation index indicates that the QUAL2Kw model has shown good performance in estimating the acidity parameter compared to other parameters. It is suggested that in addition to the low water season, modeling be done in high water seasons and use two-dimensional quality models to simulate rivers. Keywords: Qualitative Parameters, Simulation, QUAL2Kw Model, Khorramabad River, Lorestan Province5-References Babakhani, Z., Saraee Tabrizi, M., & Babazadeh, H. (2019). Determining the Self-Purification capacity of Diwandara River using model qual2kw. Journal of Echo Hydrology, 6(3), 673-684.Hashemi, Z., Gholami Sefidkouhi, M. A., & Ahmadi, K. (2019). Evaluation and Simulation of Talar River Quality by using QUAL2KW Model. Iranian Journal of Irrigation & Drainage, 12(6), 1500-1510.
Mehdi Teimouri; Omid Asadi Nalivan
Volume 6, Issue 21 , March 2020, , Pages 155-179
Abstract
1-IntroductionThe main objective of this research is to prioritize the factors affecting the occurrence of landslide and its susceptibility zoning in Lorestan province using the maximum entropy and MaxEnt models. To do this research, 11 factors affecting the occurrence of landslide including height, ...
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1-IntroductionThe main objective of this research is to prioritize the factors affecting the occurrence of landslide and its susceptibility zoning in Lorestan province using the maximum entropy and MaxEnt models. To do this research, 11 factors affecting the occurrence of landslide including height, slope, aspect, surface curvature, distance from the stream, fault and road, lithology, land use, rainfall, and topographic humidity index have been used. In this research, 30, 40, 50, 60 and 70 percent of landslides were evaluated for validation to determine the sensitivity and accuracy of the model. For evaluation of the model, the relative recognition function curve (ROC) was used. From the total of 176 landslides, 70% of the data was used as the test data and 30% as the validation data using Mahalanobis distance method and the accuracy of the model in the testing and validation stages based on the curve level was reduced. The results showed that 35.5% of the province of Lorestan has a landslide sensitivity. Based on jackknife diagram, rainfall, distance from road, lithology and land use layers were the most important factors influencing the sensitivity of landslide. The AUC level based on the relative function recognition curve indicated a 90% accuracy (excellent) of the maximum entropy method at the training stage and 83% (very good) at the validation stage to determine the landslide susceptibility. The results of this study will be suitable for provincial administrators and managers in order to land planning and reduce the damage caused by landslide occurrence.Mass movements, including landslide, is one of the most important issues in natural hazards, because its occurrence can cause many human and economic losses, especially in mountainous areas (Symeonakis et al., 2016). Regarding the destructive effects of landslides on natural resources, as well as human habitats and erosion of significant volumes of valuable soils, the identification of susceptible areas and zoning of potential occurrence or landslide susceptibility is vital and very important (Zhang et al., 2019). In recent years, the use of GIS and remote sensing along with machine learning methods has created a new step in the zoning of landslide occurrences. Lorestan province is a vulnerable area to landslide hazard due to the mountainous and wetness conditions. Therefore, the main objective of this research was to prioritize the factors affecting the occurrence of landslide and its susceptibility zoning in Lorestan province using the maximum entropy and MaxEnt model.2-MethodologyLorestan province with an area of 2829612 hectares is one of the major provinces in the west of the country. To do this research, 11 factors affecting the occurrence of landslide including altitude, slope, aspect, surface curvature, distance from the stream, fault, and road, lithology, land use, rainfall, and topographic humidity index have been used. The required maps were prepared using GIS and RS techniques. In this research, 30, 40, 50, 60 and 70 percent of landslides` division were evaluated for validation to determine the sensitivity and accuracy of the model. For evaluation of the model, the relative recognition function curve (ROC) was used. Using Mahalanobis distance method, from the total of 176 landslides, 70% of the data was used as the test data and 30% were utilized as the validation data for having the best classification. The difference of the current research with other similar studies was that in this study, use was made of Mahalanobis distance method for classification of validation data and training instead of random classification. The Mahalanobis distance helps to classify data richness and prevents random selection of points for validation. Maximum entropy method (MaxEnt model) is one of the methods of machine learning and one of the main advantages of MaxEnt model is the ability of this model to identify the most important variables and sensitivity analysis of variables using Jackknife method, which has been investigated in the current study.3-ResultsThe results showed that 35.5% of the province of Lorestan had landslide susceptibility. Based on Jackknife diagram, rainfall, distance from road, lithology and land use were, respectively, the most important factors influencing the susceptibility of landslide. The AUC level, based on the relative function recognition curve, indicated 90% accuracy (excellent) of the maximum entropy method at the training stage and 83% (very good) at the validation stage to determine the susceptibility of landslide occurrence.4-Discussion and conclusionLandslide is considered as one of the most dangerous natural disasters in the world. In this study, taking into account the affective environmental and human factors, and using the maximum entropy method, the map of landslide susceptibility of Lorestan province was prepared. The results showed that factors such as rainfall, distance from the road, lithology, land use, distance from the fault and slope were the most important factors influencing landslide susceptibility with the participation of over 60%, regarding which, land use management and road construction principles need human activity interventions. The drawn ROC curve showed that the accuracy of the model in the estimation of landslide susceptibility regions both in the stage of the test and in the validation stage was excellent and very good, which meant the excellent performance of the model. According to the obtained results, it can be said that MaxEnt model had a high ability to determine areas with landslide susceptibility and due to the speed and accuracy of the model,it is suggested that in similar researches, especially in developing countries, due to the lack of facilities and financial resources, as well as the time consuming of identifying areas with landslide susceptibility, it can be used. In addition to natural factors, some human factors such as road construction, play an important role in the occurrence of landslide, which requires avoiding ecosystem change as a disaster risk factor to reduce relative risks. The results of this research can be applicable to the decision making and management of provincial lands as well as urban planning, and they can have a significant role in preventing and reducing the damage caused by landslide.
