جستجوی مقالات مرتبط با کلیدواژه « آنتروپی شانون » در نشریات گروه « آب و خاک »

Preservation and proper management of water resources are one of the essential fields of study in the world. In arid and semi-arid regions like Iran, quantitative and qualitative management of underground water resources is particularly important. In most hydrological issues and groundwater resources studies, groundwater statistics and information availability are critical. To collect information without side effects, comprehensive and sufficient data collection with the help of a groundwater monitoring network is very important. In line with the sustainable management of renewable water resources, the need for a network of underground water observation (monitoring) wells to accurately measure the water level is necessary and necessary. Considering the complexities of the underground water environment and the high costs of conventional monitoring methods, inventing new technologies and using advanced methods in this matter will significantly help improve the underground water systems. One of the parameters of particular importance in monitoring groundwater quantity is the groundwater level. Therefore, this parameter should be measured or estimated as accurately as possible. In recent decades, the use of computer and calculation models to monitor the level of underground water has developed significantly. Considering the importance of underground water resources and network monitoring, to save time and money, in this research, principal component analysis and Shannon's entropy theory were used to monitor the underground water network of the Dezful-Andimeshk Plain.

This research used monthly groundwater level information from 77 observation wells in the Dezful-Andimeshk Plain during 2018-2019. Groundwater level information is collected twice a month. Principal component analysis and Shannon entropy methods were used for monitoring. In the current research, the number of statistical periods for each well is 24, less than the total number of observation wells. Twenty-four observation wells around it were used to monitor each well. In groundwater level monitoring, the relative importance of each well is defined by the ratio of the number of times that well is recognized as a compelling well to the number of times that well is included in the analysis of the main components. This ratio shows the importance of each well compared to other wells. Therefore, to save time and costs, less important wells can be removed in the monitoring of the underground water level. In 1948, Shannon showed that events with a high probability of occurrence show less information, and on the contrary, the lower the probability of an event, the more information it provides.  In this method, the weight of each well was obtained using Shannon's entropy theory. Any well that has a higher Shannon entropy weight contains more important and unpredictable information and should be preserved. On the contrary, a well that has a lower Shannon entropy weight can be removed from the network. Principal component analysis and Shannon's entropy method in the current research were done with the help of coding in Matlab software due to the high volume of calculations.

To rank the wells, the threshold limits are equal to zero, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 and one considered. At threshold one, only wells that have a rank of one remain (wells that are recognized as effective wells in all analyses) and threshold zero includes all wells (effective and ineffective). According to the obtained results, increasing the error in the threshold zero to 0.7 is gradual, but in the thresholds 0.8, 0.9, and one, the error value increases with a high slope. So, the amount of error in the thresholds of 0.7, 0.8, 0.9, and 1 has been calculated as 12.2, 17.7, 25.3 and 34.2 respectively. Therefore, the threshold limit in the current research is considered to be 0.7. However, the number of wells effective in monitoring the underground water level is reduced from 77 to 32. Shannon's entropy weight values were also calculated for all wells. 11 wells have the highest value of Shannon's entropy weight, which shows that they contain the most information.

The general comparison of the results of the two methods showed that all 11 wells with the highest weight in the Shannon entropy method were also observed as effective wells in the principal component analysis method. By knowing the effective wells in the region, firstly, in the face of lack of time and money, it is possible to use known effective wells for monitoring secondly, by removing ineffective wells, there will be little change in the average level of underground water. It is not possible, or in other words, the tracking error does not increase significantly. Comparing the results of the two methods showed that the remaining wells in Shannon's entropy theory are among the wells identified in the principal component analysis method. Also, considering that the wells in the region were built by the Khuzestan Water and Electricity Organization considering the types of uses, removing the ineffective wells will not affect the process of using the information of the wells. It is recommended to use principal component analysis and Shannon entropy for groundwater quality monitoring in the study area. Additionally, it is suggested to monitor the quality of the underground water network in the study area using the methods used in future research.

Evaluation of the changes in river water quality plays key role in sustainable management of surface water resources. This study evaluates the river water quality of Sofi Chai River using the water quality index (WQI) from 1972 to 2016. Shannon's Entropy Method and Analytical Hierarchy Process (AHP) have been used to determine the relative weight of water quality parameters and then these parameters have been compared with the weights provided by the World Health Organization (WHO). Qualitative parameters of Electrical Conductivity (EC), acidity (pH), Total Dissolved Solids (TDS), calcium (Ca2+), bicarbonate (HCO3-), sulfate (SO42-), chloride (Cl-), magnesium (Mg2+) and sodium (Na+) have been used to evaluate the water quality of Soufi Chai River. The results of this study show TDS and EC in AHP and sodium, chlorine and sulfate in the Entropy Method are the most effective parameters on the water quality of Soufi Chai River. The average of Water Quality Indicator calculated by the weighting methods such as entropy, AHP and standard weighting of the WHO during the period of 1972 and 2016 are 13, 38 and 16, accordingly, which indicates that the water quality of Soufi Chai River is excellent.

Landslide is one of the natural hazards that lead to a lot of human and financial losses. Researchers on the subject of landslide susceptibility are investigating the possibility of landslides with respect to topographic and geo-environmental conditions, and the obtained information is critical in landslide risk management Preparation of landslide sensitive points is an essential tool for assessing landslide risk and is very useful in better planning and management of these areas. In this research, models based on artificial intelligence and two statistical variables in determining landslide sensitive points in West Azerbaijan province have been studied and compared.

Methods based on artificial intelligence and two statistical variables were used to prepare landslide-sensitive points in the province of West Azerbaijan, which is located in northwestern Iran. This study was conducted in four stages. The first stage: the study of landslides in the studied region based on the database of the Forests, Rangelands and Watershed Organization of Iran (FRWO) and the identification of 110 landslides through field surveys, interpretation of aerial photographs and Google Earth satellite images, the second stage: data collecting and creating a spatial databases of effective factors, the third stage: applying the Frequency Ratio (FR), Shannon Entropy (SE), Bagging (BA), Random Forest (RF) and hybrid model (RF-BA) and stage four: methods validating using the system performance curve (ROC). Based on field surveys and similar studies, 12 factors affecting landslide occurrence including altitude, slope angle, slope direction, distance from fault, distance from river, distance from road, drainage density, road density, rainfall, soil, land use and lithology were identified. In the field survey, 110 landslides were identified in West Azerbaijan. 70 percent of the data were randomly selected and used for modeling and 30 percent of the data were used for validation.

In terms of geographical directions, the southern direction with a weight of 1.49 had the greatest impact on the occurrence of landslides in the province. The least weight was related to flat areas where no landslide occurred. The results of slope factor showed that the middle slopes had the greatest effect on the occurrence of landslides, so that in low slopes due to low gravity, less landslides occur and too much slopes were related to mountainous areas that were covered with rocks and there was very thin soil that is not suitable for landslide. The study of land use factor showed that 48 percent of landslides occured in agricultural areas. The results showed that most of the landslides occurred near rivers and faults. Also, in some areas, the closest distances to the road had the greatest risk of landslide

The results of this study showed that the artificial intelligence models (RF and the combined model RF-BA) had the higher efficiency than the statistical models (FR and SE). The accuracy of the combined models was higher than the single models. The ROC curve results showed the accuracy of 0.92, 0.91, 0.89 and 0.88 with RF-BA, RF, FR and SE models, respectively.

Identification of groundwater potential areas as one of the important sources for drinking water supply, agriculture, ecological diversity and various industries is one of the important and necessary issues in the management of water resources of any country, especially developing countries. In this study, a groundwater zoning map was presented to manage and plan groundwater resources using WOE, SI and Shannon models in ArcGIS10.5 in this area. For this purpose, the geographical location of 624 wells with a flow rate of more than 10 cubic meters per second in the region were divided into two groups: calibration (70%) and validation (30%). In implementing all three models, the effective parameters in groundwater potential identification include: height, slope, slope direction, land curvature, geology, land use, soil science, topographic moisture index, distance from fault, fault density, waterway density and Distance from the area waterway was used. Also, the ROC curve in SPSS software was used to validate the results of the models. The highest accuracy for this region was assigned to the statistical index model, the AUC value for this model is 84.5%, followed by evidence weight models with 81.6% and Shannon entropy with 80.6%, respectively. More than 60% of the area has moderate to very high groundwater potential. The statistical index model is more appropriate and accurate for this region. Determining areas with high groundwater potential using these methods is appropriate for implementing management programs.

To protect aquifers, it is necessary to assess their current and future stresses. For this purpose, in this paper, the indicators of groundwater resources sustainability in three dimensions of quantity, quality, and environment in the Varamin aquifer have been used to evaluate the spatial sustainability of different parts of this aquifer. In this study, a quantitative hydrogeological index; two qualitative hydrogeological indicators and two environmental indicators, and a total of 5 sustainability indicators have been used to study the spatial sustainability of the Varamin aquifer. The overall sustainability of the aquifer has been evaluated by AHP weight averaging and entropy methods from different dimensions of sustainability assessment indicators with the opinions of experts. The final results of the spatial sustainability assessment of the Varamin aquifer show that the eastern part of the aquifer is more stable than the western part of the aquifer. The low stability of the aquifer in the western parts between zero and 25 indicates the urgent need to implement aquifer sustainability management plans. The hydrogeological and hydrogeochemical status of the aquifer confirms the results obtained from the spatial sustainability of the aquifer. Hydrogeologically, the northern and eastern parts of the aquifer are very thick and have high transmissivity, while in the west the thickness decreases sharply and the alluvium is granular with low transmissivity. Also, the presence of the saline river in the western part of the aquifer, which is the passage of sewage, has reduced the quality stability and increased the vulnerability of the aquifer in the western part of the aquifer.

Designing of water quantity and quality monitoring system has been raised as one of the most complex issues in the field of water resources and the environment. The siutable quality of groundwatertable information recorded in the groundwater networks plays an important role in the sustainable design of water projects. In order to create an efficient and efficient network, groundwater networks should be periodically evaluated according to the needs and plans of the forward water resources The study area is the Nazlouchai catchment area located in west of Urmia Lake.

In this research, entropy theory was used to monitoring the quantity water level in the two historical statistical periods (2001-2016) and updated (2016-2021). The updated statistical period was developed using hybrid time series models (CARMA-ARCH). After initial data analysis and changes in the time series, the data were simulated to create the interaction of piezometers with multivariate regression. After confirming the accuracy of the multivariate regression model, entropy indicators were calculated and zoned on the Nazlouchai plain. After evaluating the groundwater network monitoring during the statistical period of 2001-2016, the Nazlochai plain groundwater network monitoring was updated for the statistical period of 2016-2021.

The results of evaluation of the CARMA-ARCH hybrid model accuracy indicate the ability of the hybrid model to simulate and predict the annual values of groundwater level in the study area. The performance factor of the model also confirmed this. The results of the evaluation of the groundwater network monitoring in Nazlouchai plain showed that more than 99% of the studied area is located in the surplus and relatively surplus situation in terms of the number of piezometers. The status of the plain in the statistical period of 2011-2016 is good and the transmission of information between the piezometers is complete. During the statistical period of 2016-2021, groundwater level changes in the study area have been reduced, which has affected the network's groundwater monitoring. So that the areas with excess wells has been reduced to moderate monitoring areas. In general, the results of the research indicate the necessity of using the groundwater monitoring network and it is recommended that this monitoring be carried out annually for different plains of Iran. Also, the results showed that with decreasing groundwater level in the studied area, information transfer between wells is also reduced.

The results show that there is no complete transfer of information between the piezometers in the study area during the statistical period of 2016-2021.

Designing of water quantity and quality monitoring system has been raised as one of the most complex issues in the field of water resources and the environment. Designing of these systems used to achieve qualitative and quantitative information, while their design process requires basic information. The study area in this research is Urmia Lake basin that located in the North West of Iran. Entropy literally means disorder. In this study used entropy theory to rain gaging monitoring in period of 1984-2011. Also The modified Mann - Kendal test was used to study the trend of the studied parameters The results of the study of the trend of precipitation values of Urmia Lake basin at annual scale showed that rainfall changes in this basin have been decreasing in the annual scale. In order to study and monitoring the rain gauge network using Entropy theory, two methods of support vector regression and kriging were used to estimate precipitation values. Results indicated that the accuracy of the regression model was higher than the Kriging model. The results of the evaluation of the entropy index at the aquifer showed that only 1.4% of the studied basin had a severe shortage of information that required the construction of a new station in the area. However, since more than 90 percent of the basin area is in terms of data transmission in excess and relatively excessive condition, the study area is relatively good at its monitoring. In general, the results indicated that the accuracy of the optimized method of support vector regression was used to estimate the annual rainfall in the Urmia Lake basin. The results of the stations' ranking in the study area showed that the stations of Jharabad, Badamlou and Orban received ratings ranging from 1 to 3, which indicates the transfer and reception of more information than other stations.

Rain water harvesting is one of the most significant water utilization management techniques to cope with water scarcity. Recognizing the potential of rain water is the first step in rain water management. The multi criteria decision analysis (MCDA) is known as an important tool in natural resources management decision particularly in water management. In this research¡ multi-criteria decision-making approach and geographic information system (GIS) were used to identify the areas with rainwater harvesting potential. The criteria including suitability for purpose¡ accuracy and ease of access¡ acceptance in the local communities¡ the cost and time were selected¡ and the weight of each criterion was calculated by using the Shannon entropy method. Totally¡ 31 indicators were selected for decision making and the weight each index was calculated and prioritized using TOPSIS. The results show that among the ranked indices¡ soil permeability has the highest weight (0.820) and the soil texture show the second order with the weight of 0.810¡ while the average annual rainfall and groundwater levels with the weights of 0.615 and 0.517 indicate the lower priority for rain water harvesting.