Mehdi Teimouri; Omid Asadi Nalivan
Abstract
1- Introduction Underground water is one of the most important water resources that plays an important role in providing water for agricultural and drinking activities in arid and semi-arid regions (Usamah and Ahmad, 2018, Wu et al., 2019, Kumar et al., 2019). Awareness of the quality of water resources ...
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1- Introduction Underground water is one of the most important water resources that plays an important role in providing water for agricultural and drinking activities in arid and semi-arid regions (Usamah and Ahmad, 2018, Wu et al., 2019, Kumar et al., 2019). Awareness of the quality of water resources is one of the most important requirements in managing, planning, and developing, protecting, and controlling water resources. Using multivariate statistical techniques helps researchers identify the most important factors affecting the quality of water systems and is a valuable tool for water resources management (Pasandidehfard et al., 2019). On the other hand, geostatistical methods are also capable of zoning water quality at the watershed level and can play an important role in completing the assessment of water quality (Ahmadi et al., 2019). The aim of this study is to evaluate the quality of groundwater used for drinking and farming in Hable-Rood Basin, analyze and interpret the quality of these resources using ArcGIS, and perform statistical tests to determine the role of land use and geology formations in water quality. 2-Methodology To do this research, 132 water sources including wells, springs, and Qanats were used during the statistical period of 2008-2018. The watershed can be divided into fifteen main categories in terms of geology. Hable-Rood watershed has 11 main land uses, which has the largest area of the watershed for pasture and the smallest area of the dams. The main components were analyzed (factor analysis) to understand the most important parameters affecting the water quality. This method weighs the components and expresses a special value for each of them (Finkler et al., 2016). Factor analysis has three stages of producing a correlation matrix from all variables (Pearson correlation method), extracting the main factors, and interpreting the results. Duncan's test was also used to check the significance level of parameters among land uses and the type of formations. Geostatistical methods were used for zoning water quality for drinking and farming purposes in the GIS. The spatial relationship of a random variable in the geostatistics was determined by the semivariogram (software GS +). The root mean square error (RMSE) method was used to assess the geostatistical methods and select the best method. It should be noted that the Schoeller diagram and Wilcox diagram were used for the drinking water zoning and agricultural water quality zoning, respectively. 3-Results and Discussion The results showed that the Cl, EC, TDS, Na, Ca, TH, and SO4 vary significantly in different land uses. The highest average was related to industrial areas within the watershed due to the release of industrial materials and the spread and diffusion of groundwater pollution. Also, the parameters of Cl, EC, TDS, TH, and SO4 differed significantly in varied formations. The trend of water quality changes shows the water quality impact of land use, and water quality has decreased sharply in the industrial area, low-yielding land, saline lands, agriculture, and residential areas. The EC parameter showed the highest correlation with TDS at 5% significance level, which is due to a high correlation with the effect of increasing EC on TDS. The pH parameter did not correlate with the other parameters. The factor analysis on the basis of water quality characteristics showed that 88.16% of the water quality variations among land uses were controlled by a single factor (TDS with a weight of 0.99). The factor analysis on the basis of water quality characteristics showed that 91.59% of water quality changes in the formations were determined by two factors (the first and the second factors with weight loads of 0.95 and 0.95 belonged to the TDS and EC parameters, respectively), and the variance percentages of each of factors 1 and 2 were 77.29 and 14.3%, respectively. 4- Conclusion In this research, the effects of geology and land use on groundwater quality were evaluated using multivariate statistical methods and geostatistical methods in ArcGIS. It was determined that some of the groundwater quality parameters were affected by land use and some of the other parameters were under the influence of the geology in the watershed. In general, however, it can be stated that in the first priority, the land use factor and human activities, and in the second priority, the geological factor affecting groundwater quality have the most significant effects. In the formation part of the geology, the dissolution of calcareous and dolomite formations, the chemical processes of salt dissolution, and evaporative formations are the main factors controlling groundwater chemistry in the region. Based on the results, multivariate statistical techniques and geostatistical methods have the ability to recognize factors affecting groundwater quality and the zoning of water quality for different uses and are, therefore, suggested for similar research.
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.
