Search published articles


Showing 3 results for Entropy
Landslide susceptibility mapping of Dalahoo Mountains using index of Entropy and Logistic Regression model
Sahar Darabi Shahmari, Amir Saffari,
Volume 6, Issue 2 (9-2019)
Abstract

Landslide susceptibility mapping is  essential for  land use  planning and decision-making especially in  the mountainous areas. The main objective of this  study is to produce landslide susceptibility maps (LSM) at Dalahoo basin, Iran  using two statistical models such as an  index of entropy and Logistic Regression and to compare the  obtained results. At the  first stage, landslide locations identified by Natural Resources Department of Kermanshah Province is used to prepare of LSM map. Of the 29 lanslides identified, 21 (≈ 70%) locations were used for the landslide susceptibility maps, while the remaining 8 (≈ 30%) cases were used for the model validation. The landslide conditioning factors such as slope degree, slope aspect, altitude, lithology, distance to faults, distance to rivers, distance to roads, land use, and  lithology  were extracted from the spatial database. Using these factors,  landslide susceptibility and weights of each factor were analyzed by index of entropy and Logistic Regression 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 showed that the index of entropy model (AUC = 86.08%) performed slightly better than conditional probability (AUC = 80. 13%) model. The produced susceptibility maps can be useful for general land use  planning in the Dalahoo basin, Iran.


Landslide susceptibility assessment Using Shannon Entropy Model in Folded Zagros, Case Study: Varansara basin
Dr Abdolmajid Ahmadi, ,
Volume 10, Issue 1 (5-2023)
Abstract
Extended abstract
Landslide risk zoning is one of the basic measures to deal with and reduce the effects of landslides. Vernesara watershed is one of the areas where many landslides have been observed in different parts of it. In this research, in order to zone the risk of landslides using the entropy index, first the ranges of landslides were determined, then the effective factors in the occurrence of range movements were prepared in the ArcGIS software environment, and a landslide susceptibility map of the studied area was prepared. . The prioritization of effective factors using Shannon's entropy index showed that the slope layers, land use, surface curvature, topographic humidity index and topographic position index had the greatest effect on the occurrence of landslides in the region. Also, zoning landslide sensitivity with the mentioned model and evaluating its accuracy using the ROC curve shows the very good accuracy of the model (79.6 percent) with a standard deviation of 0.0228 for the studied area. The zoning map shows that the low-risk areas cover only 13% of the area and more than 56% of the area is in the area with high risk of landslides, which indicates the high potential of the area in the occurrence of landslides. . Construction at a distance from fault lines, waterways and the steep Asmari Formation and safety of communication routes are the most important measures to reduce the amount of damage caused by landslides in Vernesara watershed.
Key words: natural hazards, landslide, entropy, folded Zagros.
 
Using Machine Learning Algorithms for Modeling Groundwater Resources in Arid Rangeland Western Iran
Nazanin Salimi , Marzban Faramarzi, Dr Mohsen Tavakoli, Dr Hasan Fathizad,
Volume 10, Issue 3 (9-2023)
Abstract
In recent years, groundwater discharge is more than recharge, resulting in a drop-down in groundwater levels. Rangeland and forest are considered the main recharge areas of groundwater, while the most uses of these resources are done in agricultural areas. The main goal of this research is to use machine learning algorithms including random forest and Shannon's entropy function to model groundwater resources in a semi-arid rangeland in western Iran. Therefore, the layers of slope degree, slope aspect, elevation, distance from the fault, the shape of the slope, distance from the waterway, distance from the road, rainfall, lithology, and land use were prepared. After determining the weight of the parameters using Shannon's entropy function and then determining their classes, the final map of the areas with the potential of groundwater resources was modeled from the combination of the weight of the parameters and their classes. In addition, R 3.5.1 software and the randomForest package were used to run the random forest (RF) model. In this research, k-fold cross-validation was used to validate the models. Moreover, the statistical indices of MAE, RMSE, and R2 were used to evaluate the efficiency of the RF model and Shannon's entropy for finding the potential of underground water resources. The results showed that the RF model with accuracy (RMSE: 3.41, MAE: 2.85, R² = 0.825) has higher accuracy than Shannon's entropy model with accuracy (R² = 0.727, RMSE: 4.36, MAE: 3.34). The findings of the random forest model showed that most of the studied area has medium potential (26954.2 ha) and a very small area (205.61 ha) has no groundwater potential. On the other hand, the results of Shannon's entropy model showed that most of the studied area has medium potential (24633.05 ha) and a very small area (1502.1 ha) has no groundwater potential.

Page 1 from 1     

© 2024 CC BY-NC 4.0 | Journal of Spatial Analysis Environmental hazarts

Designed & Developed by : Yektaweb