This paper couples a Forward Feature Selection algorithm with Random Forest (FFS-RF) to create a transition index map, which then guides the spatial allocation for the extrapolation of urban growth using a Cellular Automata model. We used Landsat imagery to generate land cover maps at the years 1998, 2008, and 2018 for the Tehran-Karaj Region (TKR) in Iran. The FFS-RF considered the independent variables of slope, altitude, and distances from urban, crop, greenery, barren, and roads. The FFS-RF revealed temporal non-stationary of drivers from 1998–2008 to 2008–2018. The FFS-RF detected that altitude and distance from greenery were the most important drivers of urban growth during 1998–2008, then distances from crop and barren were the

Historical exploration of flash flood events and producing flash flood susceptibility maps are crucial steps for decision makers in disaster management. In this paper, classification and regression tree (CART) methodology and its ensemble models of random forest (RF), boosted regression trees (BRT), and extreme gradient boosting (XGBoost) were implemented to create a flash flood susceptibility map of the B?sca Chiojdului River Basin, one of the areas in Romania that is constantly exposed to flash floods. The torrential areas including 962 flash flood events were delineated from orthophotomaps and field observations. Furthermore, a set of conditioning forces to explain the flash floods was constructed which included aspect, land use and land

Ensemble Prediction Systems (EPSs) are increasingly applied for rainfall forecasts and flooding warning systems. In this paper, these forecasts and their skills are evaluated through relevant criteria, particularly by considering forecast performances for different lead times. Furthermore, to enhance their performance, we propose to preprocess the EPS forecasts’ output using bias correction methods. For this aim, forecasts for different ranges of precipitation as well as various climatic conditions are evaluated, which is particularly important for extreme events that can lead to flooding. The Karun River basin in Iran is used as case study, a large area including various climate conditions. The results showed that the performance of Euro

This paper evaluates the capability of visible-near-infrared (VIS-NIR) spectroscopy to estimate soil organic carbon (SOC) at multiple depths including 0-15, 15-40, 40-60, and 60-80 cm. Four modeling algorithms, namely partial least squares regression(PLSR), principal component regression(PCR), support vector regression(SVR), and Random forest(RF) were calibrated to process the spectroscopy data. Overall, 120 soil samples were taken from 30 profiles at the depth of 0–80 cm. The selected models were calibrated considering different pre-processing techniques including Savitzky-Golay first deviation(SGD), normalization(N), and standard normal variate transformation(SNV). Results revealed that RF outperformed other models at multiple depth

Spatial variation of Urban Land Surface Temperature (ULST) is a complex function of environmental, climatic, and anthropogenic factors. It thus requires specific techniques to quantify this phenomenon and its influencing factors. In this study, four models, Random Forest (RF), Generalized Additive Model (GAM), Boosted Regression Tree (BRT), and Support Vector Machine (SVM), are calibrated to simulate the ULST based on independent factors, i.e., land use/land cover (LULC), solar radiation, altitude, aspect, distance to major roads, and Normalized Difference Vegetation Index (NDVI). Additionally, the spatial influence and the main interactions among the influential factors of the ULST are explored. Landsat-8 is the main source for data extrac

Introduction: Gully erosion is a subtype of water erosion that makes agricultural lands impracticable during its development. Given the geographical and environmental conditions, various factors contribute to the development and expansion of gully erosion. In this study, due to the extensive expansion of gully erosion in Jafarabad Moghan, and damaging the agricultural lands and rangelands, the probability of gully occurrence and the spatial effects of its drivers has been investigated. Material and methods: In this study, using a boosted regression tree model, the effect of the following factors on the gully occurrence were investigated: slope, aspect, plan curvature, altitude, clay content of horizon A, clay content of horizon B, sand co

In this paper, we clustered and analyzed landslides and investigated their underlying driving forces at two levels, country and cluster, all over Iran. Considering 12 conditioning factors, the landslides were clustered into nine relatively homogeneous regions using the Contextual Neural Gas (CNG) algorithm. Next, their underlying driving forces were ranked using the Random Forest (RF) algorithm at country and cluster levels. Our results indicate that the mechanisms for landslide occurrence varied for each cluster and that driving forces of the landslides operated differently at a country level compared to the cluster level. Moreover, slope, altitude, average annual rainfall, and distance to the main roads were identified as the

This article uses the GlobeLand30 maps of land cover to characterize the difference between years 2000 and 2010 in Asia. Methods of Intensity Analysis and Difference Components dissect the transition matrix for nine categories: Barren, Grass, Cultivated, Forest, Shrub, Water, Artificial, Wetland and Ice. Results show that Barren, Grass, Cultivated, and Forest each account for more than 21% of Asia at both 2000 and 2010, while transitions among those four categories account for more than half of the temporal difference. Nearly ten percent of Asia shows overall temporal difference, which is the sum of three components: quantity, exchange and shift. Quantity accounts for less than a quarter of the temporal difference, while exchange accounts f

Quantifying the contribution of driving factors is crucial to urban expansion modeling based on cellular automata (CA). The objective of this study is to compare individual-factor-based (IFB) models and multi-factor-based (MFB) models as well as examine the impacts of each factor on future urban scenarios. We quantified the contribution of driving factors using a generalized additive model (GAM), and calibrated six IFB-DE-CA models and fifteen MFB-DE-CA models using a differential evolution (DE) algorithm. The six IFB-DE-CA models and five MFB-DE-CA models were selected to simulate the 2005–2015 urban expansion of Hangzhou, China, and all IFB-DE-CA models were applied to project future urban scenarios out to the year 2030.

The main objectives of this paper were 1) to estimate soil organic carbon (SOC) using remote sensing covariates, soil properties, and topographic factors , and 2) to evaluate the interaction and the relative influence of the selected factors on the spatial variation of SOC. Thirteen factors were considered for digital mapping of SOC in the west Urmia Lake in Iran. To quantify multicollinearity among the predictor variables, Variance Inflation Factor (VIF) was calculated. Among them, nine independent factors were remained including silt, sand, slope, enhanced vegetation index (EVI), brightness, wetness, land cover, and latitude and longitude. A machine learning algorithm called Gradient Boosting Machine (GBM) was calibrated for understanding

It is believed that climate change will cause the extinction of many species in the near future. In this study, we assessed the impact of climate change on the climatic suitability of the Persian oak in Zagros forests in southwest Iran, by simulating their conditions under four climate change scenarios in the 2030s, 2050s, 2070s, and 2080s. Additionally, we evaluated the predictive performance of different modelling algorithms by projecting the geographic distribution of Persian oak, using a block cross-validation technique. According to the results, the Persian oak shows a stronger response to temperature, particularly the maximum temperature of the warmest month, rather than precipitation variables. This indicates that temper

Driving factors are usually assumed temporally stationary in cellular automata (CA) based land use modeling, hence the persistence of their relationships. Therefore, major questions as to how much do the temporally stationary factors explain the past and future urban growth, and how long can these factors justify the projection of urban scenarios in the future, are worth further study. We selected seven explanatory driving factors to calibrate a DE-CA (differential evolution-based CA) model to simulate urban growth in Ningbo of China during 2000–2015 and project nine scenarios of urban growth from 2015 to 2060. We evaluated the effects of factors on urban growth using generalized additive models (GAM) based on fitting statistics such as a

This paper aims to improve the spatial accuracy of urban growth simulation models and clarify any associated uncertainties. Artificial Neural Networks (ANNs), Random Forest (RF), and Logistic Regression (LR) were implemented to simulate urban growth in the megacity of Tehran, Iran, as a case study. Model calibration was performed using data between 1985 and 1999 whereas the data between 1999 and 2014 was used for model validation. First of all, Transition Index Maps (TIMs) were computed by means of each model to assess the potential of urban growth for each cell. Using the standard deviation, consensus between the TIMs was evaluated. Because the TIMs of the individual models manifested discrepancies, they were combined using a number of ens

Impacts of global warming and local scale urbanization on precipitation are evident from observations; hence both must be considered in future projections of urban precipitation. Dynamic regional models at a fine spatial resolution can capture the signature of urbanization on precipitation, however simulations for multiple decades are computationally expensive. In contrast, statistical regional models are computationally inexpensive but incapable of assessing the impacts of urbanization due to the stationary relationship between predictors and predictand. This paper aims to develop a unique modelling framework with a demonstration for Mumbai, India, where future urbanization is projected using a Markov Chain Cellular Automata a