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Showing 6 results for Entropy
Rainfall Runoff Modelling Using the Principle of Maximum Entropy(Case Study: Kasilian Watershed)
R. Mirabbasi Najafabadi, Y. Dinpazhoh , A. Fakheri-Fard,
Volume 15, Issue 58 (3-2012)
Abstract
Accurate estimation of runoff for a watershed is a very important issue in water resources management. In this study, the monthly runoff was estimated using the rainfall information and conditional probability distribution model based on the principle of maximum entropy. The information of monthly rainfall and runoff data of Kasilian River basin from 1960 to 2006 were used for the development of model. The model parameters were estimated using the prior information of the watershed such as mean of rainfall, runoff and their covariance. Using the developed model, monthly runoff was estimated for different values of runoff coefficient, , return period, , at different probability levels of rainfall for the basin under study. Results showed that the developed model estimates runoff for all return periods satisfactorily if the runoff coefficient value is taken 0.6. Also, it is observed that at a particular probability level and runoff coefficient, the estimated runoff decreases as return period increases. However, the rate of change of runoff decreases slightly as return period increases.
Prioritizing Effective Factors in Landslide Occurrence and its Susceptibility Mapping Using Shannon’s Entropy Index
H.r. Pourghasemi, H.r. Moradi, S.m. Fatemi Aghda,
Volume 18, Issue 70 (3-2015)
Abstract

The objective of the current research was to prioritize effective factors in landslide occurrence and its susceptibility zonation using Shannon’s entropy index in North of Tehran metropolitan. To this end, 528 landslide locations were identified using satellite images such as Geoeye (2011-2012), SPOT-5 (2010), and field surveys, and then landslide inventory map was created for the study area in ArcGIS environment. Data layers such as slope degree, slope aspect, plan curvature, altitude, lithology, land use, distance of road, distance of fault, distance of drainage, drainage density, road density, sediment transport index (STI), stream power index (SPI), topographic wetness index (TWI), normalized difference vegetation index (NDVI), surface area ratio (SAR) and topographic position index (TPI) were created and the mentioned maps were digitized in GIS environment. Prioritization of effective factors by Shannon’s entropy index showed that the layers such as land use, lithology, slope degree, stream power index, and NDVI had the most effect on landslide occurrence. However, factors of topographic position index and plan curvature had the least effect. Also, landslide susceptibility zoning by the mentioned model and its accuracy assessment using relative operating characteristics (ROC) curve and 30 percent of landslide locations showed an accuracy of 82.83% with a standard error of 0.0233 in the study area.


Modelling of Landslide Susceptibility Zonation Using Shannon’s Entropy Index and weight of evidence model (Case Study: Sarkhoon's Karoon)
K. Shirani,
Volume 21, Issue 1 (6-2017)
Abstract

Landslide susceptibility mapping is essential for land use planning and decision-making especially in the mountainous areas. The main objective of present research is to produce landslide hazard zonation at Sarkhoun basin in Karoon basin using two statistical models such as an index of Shannon’s entropy and weight of evidence and to assess the obtained results. At the first stage, landslide locations were identified in the study area by interpretation of aerial photographs, image sattellites, and from field investigations and then landslide inventory map was created for study area. The landslide conditioning factors such as slope degree, slope aspect, altitude, lithology, land use,distance of road, distance of fault, distance of drainage, topographic wetness index (TWI), Convergence Index, and precipitation were extracted from the spatial database and they were digitized in GIS environment. With integrated variables, landslides were calculated in each variable class and ‎weighted in index of entropy and weight of evidence model. In the last, landslide hazard zonation map ‎were obtained with both of models. The results of landslide susceptibility mapps of both statistical models were indicated more than 70 percent the occurred landslides were located in very high and high zones that about half of the basin area (over 45 percent) constitute. Also, the results of both models together were revealed that land use, has the greatest impact on the occurred landslides. Resolution of the zones, based on the seed cell area index (SCAI) and frequency ratio (FR) were evaluated suitable for both statistical models. Finally, the ROC (receiver operating characteristic) curves for landslide susceptibility maps were drawn and the areas under the curve (AUC) were calculated. The verification results of both models were evaluated very well and showed that the index of entropy model (AUC=89%) performed slightly better than weight of evidence model (AUC=82%).


Evaluation of Uncertainty in the Spatial and Temporal Variability of Groundwater Level along with Geostatistacal and Entropy Analysis
F. Moosiri, N. Ganji Khorramdel, M. Moghaddasi,
Volume 22, Issue 1 (6-2018)
Abstract
To continue or develop the exploitation of underground water for different different uses and purposes, as well as building any water structure, set of quantitative features of aquifers can be detected. To achieve this goal, quantitative monitoring of groundwater level is only possible. Accordingly, this study compared the impact of both the concept of marginal entropy and ordinary kriging for groundwater level monitoring network design in the case Gotvand-Aghili Plain, Khuzestan province. It is important to note that a key aspect in groundwater level monitoring of the quantity measured was the variability or uncertainty in it. This created a considerable confidence to monitor and ultimately achieve favorable conditions in the future. In this study, the variability of the groundwater level was considered to evaluate the combined effects of marginal entropy and ordinary kriging. In order to determine the suitable areas for further monitoring or thinning as well as the compatibility of these two methods, the monitor network design was designed. The map classified according to the marginal entropy method, in a range between 0.07 to 5.26 of the marginal entropy change, areas with the higher rates of 2.13 in terms of density; this indicated the need for more observation wells. Ordinary Kriging method also changed the range of values; they also represented areas that needed monitoring more than 13.16. Comparison of the results obtained by the two methods showed that the marginal entropy of the kriging method with less uncertainty and by using it, there was less the need to be monitored and classified. Comparison of the two methods by the zoning map showed that fewer errors were taken to the marginal entropy method and it could be recommended for the groundwater level monitoring network design. The network was also based on the Cross validation estimation error evaluated. These tests and additional analysis were employed in this study to determine the suitable areas for the higher density of wells and the need for thinning areas. The results further confirmed the proper performance of the methods employed, as well as the superiority of the marginal entropy in the design of a small groundwater monitoring network.

Application of a Self-Organizing Map for Clustering the Groundwater Quality in Kerman Province and Assessment its Suitability for Drinking and Irrigation Purposes
F. Soroush, A. Seifi,
Volume 23, Issue 2 (9-2019)
Abstract
Evaluation of groundwater hydro chemical characteristics is necessary for planning and water resources management in terms of quality. In the present study, a self-organizing map (SOM) clustering technique was used to recognize the homogeneous clusters of hydro chemical parameters in water resources (including well, spring and qanat) of Kerman province; then, the quality classification of groundwater samples was investigated for drinking and irrigation uses by employing SOM clusters. Patterns of water quality parameters were visualized by SOM planes, and similar patterns were observed for those parameters that were correlated with each other, indicating a same source. Based on the SOM results, the 729-groundwater samples in the study area were grouped into 4 clusters, such that the clusters 1, 2, 3, and 4 contained 73%, 6.2%, 6.7%, and 14.1% of groundwater samples, respectively. The increase order of electrical conductivity parameter in the clusters was as 1, 4, 3 and 2. The results of water quality index based on the entropy weighting (EWQI) showed that all of the samples with excellent and good quality (36.3% of samples) for drinking belonged to the cluster 1. According to the Wilcox diagram, 435-groundwater samples (81.7%) in the cluster 1 had the permitted quality for irrigation activities, and the other 285-groundwater samples were placed in all four clusters, indicating the unsuitable quality for irrigation. The Piper diagram also revealed that the dominant hydro chemical faces of cluster 1 were Na-Cl, Mixed Ca-Mg-Cl and Ca-HCO3, whereas the clusters 2, 3, and 4 had the Na-Cl face. This study, therefore, shows that the SOM approach can be successfully used to classify and characterize the groundwater in terms of hydrochemistry and water quality for drinking and irrigation purposes on a provincial scale.

Evaluation of Soil Quality Index in the Presence of Sugarcane Bagasse Hydrochar Using Shannon Entropy Combination and Principal Component Analysis
L. Divband Hafshejani, M. Mirnaseri, A. A. Naseri,
Volume 29, Issue 4 (12-2025)
Abstract
Soil, as one of the vital natural resources, plays a fundamental role in ecosystem sustainability and global food security; however, degradation caused by unsustainable management, intensive agriculture, and pollution threatens its capacity. The use of organic amendments such as hydrochar is considered an innovative approach to improve soil physicochemical properties and enhance the Soil Quality Index (SQI). This study aimed to investigate the effects of different levels of hydrochar on soil properties and evaluate SQI. The treatments included control and three hydrochar levels (H10, H20, and H50). Soil properties such as pH, porosity, bulk density, electrical conductivity, organic carbon, total nitrogen, and available phosphorus were measured and normalized, and parameter weighting was conducted using entropy and principal component analysis (PCA). Results showed that nitrogen and organic carbon had the greatest importance in soil quality. The H50 treatment recorded the highest SQI (0.815), significantly greater than other treatments, while H20 (0.546) and H10 (0.336) also showed positive effects compared to the control (0.159). Hydrochar application improved organic carbon, nitrogen, and phosphorus and reduced bulk density. Although an increase in electrical conductivity was observed in H50. Overall, hydrochar application had a positive and gradual effect on SQI, with H20 recommended as an optimal level to improve fertility and reduce long-term salinity risks.

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