Geological parameters affected land subsidence in Mashhad plain, north-east of Iran

Moteahd, Fahimeh Salehi; Moghaddas, Nasser Hafezi; Lashkaripour, Gholam Reza; Dehghani, Maryam

doi:10.1007/s12665-019-8413-y

Cited by 21 publications

(5 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Kashafrood river and the streams that originate from the southern mountains of this city make up the primary drainage network of the Mashhad Plain. With a marginalized population of over 1 million people (Islamic Republic News Agency, 2018), Mashhad has experienced rapid population growth in recent decades (Naserikia, Asadi Shamsabadi, Rafieian, & Leal Filho, 2019), mainly because of the large number of migrants from small cities and rural areas which has led to unprecedented growth in the urban area (Salehi Moteahd, Hafezi Moghaddas, Lashkaripour, & Dehghani, 2019; Soltanifard & Aliabadi, 2019). The city of Mashhad has expanded in different directions, especially to the west and northwest, resulting in the loss of croplands.…”

Section: Methodsmentioning

confidence: 99%

On the spatiotemporal generalization of machine learning and ensemble models for simulating built‐up land expansion

Shafizadeh‐Moghadam

Valavi

Asghari

et al. 2021

Transactions in GIS

View full text Add to dashboard Cite

This study evaluates the spatiotemporal generalization of statistical and machine learning models to simulate built‐up land expansion and compare it to ensemble approaches. Integrated with cellular automata, six individual models—artificial neural networks, support vector machines (SVM), random forest (RF), boosted regression trees, the generalized additive model, the lasso, and two ensemble approaches called ensemble median and ensemble weighted area under the curve—were implemented. Each model was calibrated based on data from 1975–1990, and their extrapolation power was evaluated for 1990–1996, 1996–2000, 2000–2011, and 2011–2017. Total operating characteristics revealed that the RF model achieved the highest calibration accuracy and the highest performance loss during the validation period. The lowest calibration accuracy was related to the SVM model, yet its performance during the validation period increased. In the third time interval (1996–2002), the highest accuracy was again related to the SVM model. A sharp drop in simulation accuracy was seen in all models during the fourth (2002–2011) and fifth intervals (2011–2017). None of the ensemble models appeared to be superior to the individual models. Further, the accuracy of built‐up land expansion models drops noticeably for long‐term simulations.

show abstract

Section: Methodsmentioning

confidence: 99%

On the spatiotemporal generalization of machine learning and ensemble models for simulating built‐up land expansion

Shafizadeh‐Moghadam

Valavi

Asghari

et al. 2021

Transactions in GIS

View full text Add to dashboard Cite

show abstract

“…According to the available information, subsidence has become critical in 6 plains of Razavi Khorasan, which include Mashhad, Neishabur, Kashmer, Bardskan, Khaf and Joyn plains (Salehi Moteahd et al, 2019). Based on the estimates, subsidence in the Mashhad plain occurs between 0 and 17 cm in different places every year, and the protrusions and outcrops of these settlements are mainly outside the residential context (Rahmati et al, 2019).…”

Section: Land Subsidence In Khorasanmentioning

confidence: 99%

“…The reason for subsidence is that, in natural equilibrium, the hydraulic pressure of the groundwater in the pores of the aquifer supports some of the weight of the earth (Amin et al, 2019). When groundwater is overdrawn by humans, pore pressures in the aquifer drop and compression and compaction of the aquifer happen (Salehi Moteahd et al, 2019). When the aquifer is compressed, it causes the ground to subside, to go down, or to drop (Abbasnejad et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Review on current status and challenging issues of land subsidence in Iran

Sadjadi¹

2022

10.5267/j.jfs

View full text Add to dashboard Cite

Land subsidence in Iran is one of the environmental issues of recent years in Iran, which is mostly due to shortage of the groundwater resources. Iran is considered as the country with the most land subsidence. The amount of land subsidence in Iran is believed to be far more than the average of developed countries. Presently, over 300 plains of Iran are suffering from land subsidence and in some plains of Iran, the conditions have passed the subsidence and the ground has entered the critical stage of creating sinkholes. This paper presents an overview of the current status and challenging issues of land subsidence in Iran.

show abstract

“…Mashhad plain is located in a special position and its the important agricultural and social-political center in Khorassan-Razavi province, Iran. Mashhad plain is in critical condition during the recent decades in terms of ground water resources and land subsidence (Salehi Moteahd et al 2019). This has caused problems for the sustainable development of the region so land management strategies and digital mapping of soil properties are very important for monitoring and optimization of land-use management in this plain.…”

Section: Introductionmentioning

confidence: 99%

Digital mapping of selected soil properties using machine learning and geostatistical techniques in Mashhad plain, northeastern Iran

et al. 2023

View full text Add to dashboard Cite

Understanding the spatial variation of soil properties is essential to monitor land capabilities as well as sustainable management of soil resources. The aim of this study was to predict digital soil properties mapping using 23 environmental variables i.e. terrain attributes and remote sensing (RS) indicis through 1500 km 2 of Mashhad plain lands. To achieve this purpose, a total of 180 soil samples (0-10 cm) were taken. The random forest (RF) model combined with ordinary kriging (OK), as well as regression kriging (RK) were applied to relate environmental variables and the studied soil properties. The results revealed that RF-OK was the best model with R 2 and RMSE for silt (0.89 and 0.10%) followed by calcium carbonate equivalent (0.88 and 3.30%), clay (0.87 and 2.26%), soil organic carbon (0.86 and 0.24%), sand (0.84 and 4.21%), and pH (0.82 and 5.42%). The RS covariates including band 5 (B5), modified soil adjusted vegetation index (MSAVI), difference vegetation index (DVI), band 2 (B2), carbonate rock index (CRI2), gypsum index (GI) and enhanced vegetation index (EVI) and terrain attributes including topographic wetness index (TWI) and elevation (EL), and topographic position index (TPI) were the most important variables in modeling of different soil properties. Finally, RF-OK model with low cost and high accuracy can be applicable to use for predicting different soil properties as well as spatial information acquired from an effort to maps to managing agriculture in areas at different conditions. This method can be applied to other regions of similar characteristics and for similar purposes.

show abstract

Geological parameters affected land subsidence in Mashhad plain, north-east of Iran

Cited by 21 publications

References 22 publications

On the spatiotemporal generalization of machine learning and ensemble models for simulating built‐up land expansion

On the spatiotemporal generalization of machine learning and ensemble models for simulating built‐up land expansion

Review on current status and challenging issues of land subsidence in Iran

Digital mapping of selected soil properties using machine learning and geostatistical techniques in Mashhad plain, northeastern Iran

Contact Info

Product

Resources

About