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The predicts current and future flood risk in the Kalvan watershed of northwestern Markazi Province, Iran. To do this, 512 flood and non-flood locations were identified and mapped. Twenty flood-risk factors were selected to model flood risk using several machine learning techniques: conditional inference random forest (CIRF), the gradient boosting model (GBM), extreme gradient boosting (XGB) and their ensembles. To investigate the future (year 2050) effects of changing climates and changing land use on future flood risk, a general circulation model (GCM) with representative concentration pathways (RCPs) of the 2.6 and 8.5 scenarios by 2050 was tested for impacts on 8 precipitation variables. In addition, future land uses in 2050 was prepared using a CA-Markov model. The performances of the flood risk models were validated with Receiver Operating Characteristic-Area Under Curve (ROC-AUC) and other statistical analyses. The AUC value of the ROC curve indicates that the ensemble model had the highest predictive power (AUC = 0.83) and was followed by GBM (AUC = 0.80), XGB (AUC = 0.79), and CIRF (AUC = 0.78). The results of climate and land use changes on future flood-prone areas showed that the areas classified as having moderate to very high flood risk will increase by 2050. Due to the changes occurring with land uses and in climates, the area classified as moderate to very high risk increased in the predictions from all four models. The areal proportion classes of the risk zones in 2050 under the RCP 2.6 scenario using the ensemble model have changed of the following proportions from the current distribution Very Low = −12.04 %, Low = −8.56 %, Moderate = +1.56 %, High = +11.55 %, and Very High = +7.49 %. The RCP 8.5 scenario has caused the following changes from the present percentages: Very Low = −14.48 %, Low = −6.35 %, Moderate = +4.54 %, High = +10.61 %, and Very High = +5.67 %. The results of current and future flood risk mapping can aid planners and flood hazard managers in their efforts to mitigate impacts.
Saeid Janizadeh; Subodh Chandra Pal; Asish Saha; Indrajit Chowdhuri; Kourosh Ahmadi; Sajjad Mirzaei; Amir Hossein Mosavi; John P. Tiefenbacher. Mapping the spatial and temporal variability of flood hazard affected by climate and land-use changes in the future. Journal of Environmental Management 2021, 298, 113551 .
AMA StyleSaeid Janizadeh, Subodh Chandra Pal, Asish Saha, Indrajit Chowdhuri, Kourosh Ahmadi, Sajjad Mirzaei, Amir Hossein Mosavi, John P. Tiefenbacher. Mapping the spatial and temporal variability of flood hazard affected by climate and land-use changes in the future. Journal of Environmental Management. 2021; 298 ():113551.
Chicago/Turabian StyleSaeid Janizadeh; Subodh Chandra Pal; Asish Saha; Indrajit Chowdhuri; Kourosh Ahmadi; Sajjad Mirzaei; Amir Hossein Mosavi; John P. Tiefenbacher. 2021. "Mapping the spatial and temporal variability of flood hazard affected by climate and land-use changes in the future." Journal of Environmental Management 298, no. : 113551.
This paper examines regional cooperation in disaster risk management (DRM) in the transboundary regions of five Central Asian states: Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan. Regional cooperation to reduce disaster potential is a rather recent endeavour both internationally and in the region. Cooperation to enhance environmental security in post-Soviet Central Asia is slowly strengthening monitoring, planning, and prevention of natural disasters with a new approach that anticipates risks and hazards and seeks to reduce the likelihood of disasters instead of responding to the aftermath. Empowerment of regional associations to coordinate states’ activities to understand and solve common problems is needed. The legacy of the Soviet past and the contemporary states’ efforts to participate in regional cooperative organizations are reviewed and the prospects for new instruments for DRM cooperation are discussed. The needs are multifaceted and complex, but there are glimmers of promise for regional and borderland cooperation.
Nadira G. Mavlyanova; Viacheslav A. Lipatov; John P. Tiefenbacher. Regional Cooperative Disaster Risk Management in Central Asian Borderlands. Journal of Borderlands Studies 2021, 1 -23.
AMA StyleNadira G. Mavlyanova, Viacheslav A. Lipatov, John P. Tiefenbacher. Regional Cooperative Disaster Risk Management in Central Asian Borderlands. Journal of Borderlands Studies. 2021; ():1-23.
Chicago/Turabian StyleNadira G. Mavlyanova; Viacheslav A. Lipatov; John P. Tiefenbacher. 2021. "Regional Cooperative Disaster Risk Management in Central Asian Borderlands." Journal of Borderlands Studies , no. : 1-23.
When it comes to projecting the potential effects of climate change on hydro-climatic variables using time-series models, the conventional approach has been to examine correlations with exogenous variables. Establishing correlations among endogenous and exogenous variables, however, cannot guarantee that there is a cause-effect relationship among the variables. This study, therefore, used Granger-causality for a more accurate alternative to the exogenous variables needed to expand time-series models. To demonstrate this, Maharlou Lake, Iran was selected for a case study not only because this inland water body has been exhibiting unprecedented depletion patterns recently, but also because studies are projecting that a changing local climate could add pressure to the region’s water resources. Both restricted and extended models reveal that shrinkage observed in the lake’s time-series data is expected to continue in the near future. This depletion, however, is projected to be more pronounced in August, September, and October, and milder in February, March, and April. Furthermore, the results from the extended model hint at a more severe pattern of shrinkage rooted in the adverse impacts of projected climate change.
Babak Zolghadr-Asli; Maedeh Enayati; Hamid Reza Pourghasemi; Mojtaba Naghdyzadegan Jahromi; John P. Tiefenbacher. Application of Granger-causality to study the climate change impacts on depletion patterns of inland water bodies. Hydrological Sciences Journal 2021, 1 .
AMA StyleBabak Zolghadr-Asli, Maedeh Enayati, Hamid Reza Pourghasemi, Mojtaba Naghdyzadegan Jahromi, John P. Tiefenbacher. Application of Granger-causality to study the climate change impacts on depletion patterns of inland water bodies. Hydrological Sciences Journal. 2021; ():1.
Chicago/Turabian StyleBabak Zolghadr-Asli; Maedeh Enayati; Hamid Reza Pourghasemi; Mojtaba Naghdyzadegan Jahromi; John P. Tiefenbacher. 2021. "Application of Granger-causality to study the climate change impacts on depletion patterns of inland water bodies." Hydrological Sciences Journal , no. : 1.
Land subsidence (LS) is significant problem that can lead to casualties, destruction of infrastructure, and socio-economic and environmental problems. In this study, we examine the Damghan Plain of Iran where LS poses a major obstacle to growth and management of the region. Dagging and random subspace (RSS) as meta- or ensemble-classifiers of a radial basis function neural network (RBFnn) were combined into two novel-ensemble intelligence approaches (Dagging-RBFnn and RSS-RBFnn) to predict and map the susceptibility of land units to subsidence. The goodness-of-fit (of training data) and prediction accuracy (of testing data) for the ensemble models were contrasted with the RBFnn, which is used as the benchmark for improvement. Details of 120 LS locations were examined and the data for twelve LS conditioning factors (LSCFs) were compiled. The LS points were randomly divided into four groups or folds, each comprised of 25 percent of the cases. The novel ensemble models were constructed using 75 percent (3 folds) and tested with the remaining 25 percent (onefold) in a four-fold cross-validation (CV) mechanism. Information-gain ratio and multicollinearity tests were used to select the LSCFs that would be used to estimate LS probabilities. The importance of each factor was calculated using a random forest (RF) model. The most important LSCFs were groundwater drawdown, land uses and land covers, elevation, and lithology. Twelve land subsidence susceptibility maps were generated using the k-fold CV approaches as each of the three models (RBFnn, Dagging-RBFnn and RSS-RBFnn) was applied to each of the four folds. The LS susceptibility models reveal a strong probability for LS on 15% to 24% of the plain. All of the maps generated achieved adequate levels of prediction accuracies and goodness-of-fits. The Dagging-RBFnn ensemble yielded the most robust maps, however. The ensemble of Dagging-RBFnn enhances the accuracy of modeling but the opposite condition was found for the RSS-RBFnn ensemble. It is evident that ensembles with meta classifiers might not always increase the accuracy of the base classifier. Overall, the southern part of the plain shows the highest LS risk. The results of this study suggests that groundwater withdrawal levels should be tracked and possibly restricted in regions with higher (extreme or moderate) probabilities of LS. This demonstrates that new approaches can support land use planning and decision making to minimize LS and improve sustainability.
Alireza Arabameri; M. Santosh; Fatemeh Rezaie; Sunil Saha; Romulus Coastache; Jagabandhu Roy; Kaustuv Mukherjee; John Tiefenbacher; Hossein Moayedi. Application of novel ensemble models and k-fold CV approaches for Land subsidence susceptibility modelling. Stochastic Environmental Research and Risk Assessment 2021, 1 -23.
AMA StyleAlireza Arabameri, M. Santosh, Fatemeh Rezaie, Sunil Saha, Romulus Coastache, Jagabandhu Roy, Kaustuv Mukherjee, John Tiefenbacher, Hossein Moayedi. Application of novel ensemble models and k-fold CV approaches for Land subsidence susceptibility modelling. Stochastic Environmental Research and Risk Assessment. 2021; ():1-23.
Chicago/Turabian StyleAlireza Arabameri; M. Santosh; Fatemeh Rezaie; Sunil Saha; Romulus Coastache; Jagabandhu Roy; Kaustuv Mukherjee; John Tiefenbacher; Hossein Moayedi. 2021. "Application of novel ensemble models and k-fold CV approaches for Land subsidence susceptibility modelling." Stochastic Environmental Research and Risk Assessment , no. : 1-23.
Fires have increased in northeastern Iran as its semi-arid climate landscape is desiccated by human activities. To combat fire outbreaks in any region, fire susceptibility must be mapped using accurate and efficient models. This research mapped fire susceptibility in the forests and rangelands of Golestan Province in northeastern Iran using new data-mining models. Fire effective factors, including elevation, slope angle, annual mean rainfall, annual mean temperature, wind effect, topographic wetness index (TWI), plan curvature, distance to river, distance to road, and distance to village were obtained from several sources. The relative importance of each variable was determined using a random-forest algorithm. Fire-susceptibility maps were produced in R 3.0.2 software using GAM, MARS, SVM algorithms, and a new ensemble of the three models: GAM-MARS-SVM. The four fire-susceptibility maps were validated using the area under the curve. The results show that the distance to the village, annual mean rainfall, and elevation were of greatest importance in predicting fire susceptibility. The new GAM-MARS-SVM ensemble model achieved the highest precision of fire-susceptibility mapping. The fire-susceptibility map produced using the GAM-MARS-SVM ensemble model best detected the high fire risk areas in Golestan Province. The fire-susceptibility map produced by the ensemble model can be very useful for creating and enhancing management strategies for preventing fires, particularly in the higher-risk portions of Golestan Province.
Saeedeh Eskandari; Hamid Reza Pourghasemi; John P. Tiefenbacher. Fire-susceptibility mapping in the natural areas of Iran using new and ensemble data-mining models. Environmental Science and Pollution Research 2021, 28, 47395 -47406.
AMA StyleSaeedeh Eskandari, Hamid Reza Pourghasemi, John P. Tiefenbacher. Fire-susceptibility mapping in the natural areas of Iran using new and ensemble data-mining models. Environmental Science and Pollution Research. 2021; 28 (34):47395-47406.
Chicago/Turabian StyleSaeedeh Eskandari; Hamid Reza Pourghasemi; John P. Tiefenbacher. 2021. "Fire-susceptibility mapping in the natural areas of Iran using new and ensemble data-mining models." Environmental Science and Pollution Research 28, no. 34: 47395-47406.
This study aims to determine the effect of a flood-spreading system on the morphometric characteristics of alluvial fans (AFs) in Gribayegan Fasa, Iran, and its relationship with erosion, age, texture, and type of formations. After determining the AFs using the semiautomatic method and determining their recharged watersheds, 25 morphometric characteristics were investigated. The most important morphometric characteristics were identified using principal component analysis (PCA). The group method of data handling (GMDH) neural network is used to predict erosion, soil texture, age, and formation material based on the morphometric characteristics of the fan. The results demonstrate that the semiautomatic method can effectively extract AF from the landscape. The results of AF morphometry before and after flood spreading show that the fan area, drainage basin circularity (Cirb), fan perimeter, relief ratio of the fan, fan length, minimum fan height, and maximum fan height were higher before flood spreading, and basin shape, St (soil texture), upper fan slope, fan volume, and sweep angle had higher values after the flood. In addition, the results of PCA show that fan area, fan perimeter, fan length, fan radius, fan volume, basin area, basin perimeter, main channel length, basin length, and drainage density are important factors. The results of the GMDH algorithm reveal that this method can accurately predict the formation, age, soil texture, and erosion rate using morphometric characteristics. Therefore, the R2 is 0.92 for the formation age and R2 is 0.86 for the erosion rates, formation types, and soil texture, respectively. Therefore, the most important morphometric parameters can be determined using PCA, and the conditions and processes in the basin, such as formation material, age, soil texture, and erosion rate, can be predicted with high accuracy using the GMDH algorithm.
Marzieh Mokarram; Hamid Reza Pourghasemi; John P. Tiefenbacher. Morphometry of AFs in upstream and downstream of floods in Gribayegan, Iran. Natural Hazards 2021, 108, 425 -450.
AMA StyleMarzieh Mokarram, Hamid Reza Pourghasemi, John P. Tiefenbacher. Morphometry of AFs in upstream and downstream of floods in Gribayegan, Iran. Natural Hazards. 2021; 108 (1):425-450.
Chicago/Turabian StyleMarzieh Mokarram; Hamid Reza Pourghasemi; John P. Tiefenbacher. 2021. "Morphometry of AFs in upstream and downstream of floods in Gribayegan, Iran." Natural Hazards 108, no. 1: 425-450.
Determining areas of high groundwater potential is important for exploitation, management, and protection of water resources. This study assesses the spatial distribution of groundwater potential in the Zarrineh Rood watershed of Kurdistan Province, Iran using combinations of five statistical and machine learning algorithms – frequency ratio (FR), radial basis function (RBF), index of entropy (IOE), evidential belief function (EBF) and fuzzy art map (FAM). To accomplish this, 1448 well locations in the study area were randomly divided into two data sets for training (70%= 1013 locations) and validation (30%= 435 locations) based on the holdout method. Fourteen factors that can affect the presence or absence of groundwater were identified, measured, and mapped using ArcGIS and SAGA-GIS software. The models were used to predict the locations of groundwater based on suitable combinations of the conditioning factors to produce groundwater potential maps. The probability of groundwater at any location was classified as low, moderate, high, or very high based on natural breaks in the data spectrum. The model predictions were tested for validity and their success was determined using receiver operating characteristic (ROC) curves, standard errors (SE), positive predictive value (PPV), negative predictive value (NPV), sensitivity (SST), specificity (SPF) and accuracy (ACC), and the Friedman test. The performance assessments of groundwater potential predictions using the area under the curve (AUC) and accuracy (ACC) showed that the FR-RBF model had very good performance (AUC= 0.889, ACC= 87.51). And FR-FAM (AUC= 0.869, ACC= 84.67), EBF-FAM (AUC= 0.864, ACC= 84.42), EBF-RBF (AUC= 0.854, ACC= 83.94), FR-IOE (AUC= 0.836, ACC= 83.62), and EBF-IOE (AUC= 0.833, ACC= 80.42) also had acceptable performance. The results of the Friedman test also show that there are significant differences between the models and the highest mean rank was generated by the FR-FAM model (3.642). Therefore, the hybrid models can be used to increase the accuracy of groundwater-prediction models in the study region and perhaps in similar settings.
Peyman Yariyan; Mohammadtaghi Avand; Ebrahim Omidvar; Quoc Bao Pham; Nguyen Thi Thuy Linh; John P. Tiefenbacher. Optimization of statistical and machine learning hybrid models for groundwater potential mapping. Geocarto International 2021, 1 -35.
AMA StylePeyman Yariyan, Mohammadtaghi Avand, Ebrahim Omidvar, Quoc Bao Pham, Nguyen Thi Thuy Linh, John P. Tiefenbacher. Optimization of statistical and machine learning hybrid models for groundwater potential mapping. Geocarto International. 2021; ():1-35.
Chicago/Turabian StylePeyman Yariyan; Mohammadtaghi Avand; Ebrahim Omidvar; Quoc Bao Pham; Nguyen Thi Thuy Linh; John P. Tiefenbacher. 2021. "Optimization of statistical and machine learning hybrid models for groundwater potential mapping." Geocarto International , no. : 1-35.
Land subsidence is a hazard that results from conditioning factors that cause environmental change and generate social and economic impacts. Some of these factors may increase dissolution of calcareous stones, change groundwater storage, or stem from mining, faulting, and seismic activity. Semnan Plain, Iran is experiencing land subsidence that, along with secondary impacts like surface fissures, is becoming increasingly troublesome. This study modeled land subsidence and created a susceptibility map using multivariate adaptive regression spline (MARS), mixture discriminant analysis (MDA), and boosted regression tree (BRT) machine-learning methods. Analysis of satellite imagery, documents reporting past subsidence, and a contemporary field survey identified 65 sinkholes on Semnan Plain. Twelve conditioning factors were selected for analysis from a review of the scholarly literature, field investigation, and data availability. The three methods were used to model subsidence from a training subset of the known sites. The models were validated with the remaining subset of subsidence locations. Finally, susceptibility maps were used to predict other sites that are highly likely to see subsidence. Receiver operating characteristic curves and the area under the curves (AUC) were applied to assess the accuracies of the maps. AUC values (0.637, 0.783, and 0.712 for the BRT, MARS, and MDA models respectively) showed that MARS generated the most accurate model, MDA generated the second most accurate, and BRT’s was the least accurate model. Subsidence susceptibility maps as produced here can be useful, meaningful, and functional tools for local and regional planners and policy makers involved in land use planning, resource management, and hazard mitigation.
Majid Mohammady; Hamid Reza Pourghasemi; Mojtaba Amiri; John P. Tiefenbacher. Spatial modeling of susceptibility to subsidence using machine learning techniques. Stochastic Environmental Research and Risk Assessment 2021, 1 -12.
AMA StyleMajid Mohammady, Hamid Reza Pourghasemi, Mojtaba Amiri, John P. Tiefenbacher. Spatial modeling of susceptibility to subsidence using machine learning techniques. Stochastic Environmental Research and Risk Assessment. 2021; ():1-12.
Chicago/Turabian StyleMajid Mohammady; Hamid Reza Pourghasemi; Mojtaba Amiri; John P. Tiefenbacher. 2021. "Spatial modeling of susceptibility to subsidence using machine learning techniques." Stochastic Environmental Research and Risk Assessment , no. : 1-12.
Changing climate and human interference with natural phenomena are causing unprecedented changing patterns in hydro-climatic variables. These changes can manifest as dynamic changes of the stochastic properties of the datasets over time, which pose challenges for conventional time-series modeling. These datasets are dynamic in nature, even when trend and seasonality components are eliminated. Shrinking lakes are among the most notable examples of hydro-climatic-driven phenomena. This study demonstrates a framework that can capture the underlying dynamic and non-stationary structure of such environments using a case study of Maharlou Lake, Iran. To that end, a hybrid time-series model was developed to account for volatility in the data [i.e., SARIMA (1,1,2) × (1,1,2)12/GARCH(1,0)]. A series of statistical tests (i.e., augmented Dickey–Fuller test, the Ljung-Box test, the heteroskedasticity test, and the two-sample Kolmogorov–Smirnov test) were used to create, calibrate, and assess the model in the 95% confidence interval. The results indicate the decline and depletion of the lake. This reduction manifests as a general downward trend and a widening gap between the lake’s intra-annual fluctuations. The changes could be an alarming signal, as this saline lake could be mimicking the tragic fate of similar inland water bodies like Lake Urmia or the Aral Sea.
Babak Zolghadr-Asli; Maedeh Enayati; Hamid Reza Pourghasemi; Mojtaba Naghdyzadegan Jahromi; John P. Tiefenbacher. A linear/non-linear hybrid time-series model to investigate the depletion of inland water bodies. Environment, Development and Sustainability 2020, 23, 10727 -10742.
AMA StyleBabak Zolghadr-Asli, Maedeh Enayati, Hamid Reza Pourghasemi, Mojtaba Naghdyzadegan Jahromi, John P. Tiefenbacher. A linear/non-linear hybrid time-series model to investigate the depletion of inland water bodies. Environment, Development and Sustainability. 2020; 23 (7):10727-10742.
Chicago/Turabian StyleBabak Zolghadr-Asli; Maedeh Enayati; Hamid Reza Pourghasemi; Mojtaba Naghdyzadegan Jahromi; John P. Tiefenbacher. 2020. "A linear/non-linear hybrid time-series model to investigate the depletion of inland water bodies." Environment, Development and Sustainability 23, no. 7: 10727-10742.
Electrical conductivity (EC), one of the most widely used indices for water quality assessment, has been applied to predict the salinity of the Babol-Rood River, the greatest source of irrigation water in northern Iran. This study uses two individual—M5 Prime (M5P) and random forest (RF)—and eight novel hybrid algorithms—bagging-M5P, bagging-RF, random subspace (RS)-M5P, RS-RF, random committee (RC)-M5P, RC-RF, additive regression (AR)-M5P, and AR-RF—to predict EC. Thirty-six years of observations collected by the Mazandaran Regional Water Authority were randomly divided into two sets: 70% from the period 1980 to 2008 was used as model-training data and 30% from 2009 to 2016 was used as testing data to validate the models. Several water quality variables—pH, HCO3−, Cl−, SO42−, Na+, Mg2+, Ca2+, river discharge (Q), and total dissolved solids (TDS)—were modeling inputs. Using EC and the correlation coefficients (CC) of the water quality variables, a set of nine input combinations were established. TDS, the most effective input variable, had the highest EC-CC (r = 0.91), and it was also determined to be the most important input variable among the input combinations. All models were trained and each model’s prediction power was evaluated with the testing data. Several quantitative criteria and visual comparisons were used to evaluate modeling capabilities. Results indicate that, in most cases, hybrid algorithms enhance individual algorithms’ predictive powers. The AR algorithm enhanced both M5P and RF predictions better than bagging, RS, and RC. M5P performed better than RF. Further, AR-M5P outperformed all other algorithms (R2 = 0.995, RMSE = 8.90 μs/cm, MAE = 6.20 μs/cm, NSE = 0.994 and PBIAS = −0.042). The hybridization of machine learning methods has significantly improved model performance to capture maximum salinity values, which is essential in water resource management.
Assefa M. Melesse; Khabat Khosravi; John P. Tiefenbacher; Salim Heddam; Sungwon Kim; Amir Mosavi; Binh Thai Pham. River Water Salinity Prediction Using Hybrid Machine Learning Models. Water 2020, 12, 2951 .
AMA StyleAssefa M. Melesse, Khabat Khosravi, John P. Tiefenbacher, Salim Heddam, Sungwon Kim, Amir Mosavi, Binh Thai Pham. River Water Salinity Prediction Using Hybrid Machine Learning Models. Water. 2020; 12 (10):2951.
Chicago/Turabian StyleAssefa M. Melesse; Khabat Khosravi; John P. Tiefenbacher; Salim Heddam; Sungwon Kim; Amir Mosavi; Binh Thai Pham. 2020. "River Water Salinity Prediction Using Hybrid Machine Learning Models." Water 12, no. 10: 2951.
Infectious disease outbreaks pose a significant threat to human health worldwide. The outbreak of pandemic coronavirus disease 2019 (COVID-19) has caused a global health emergency. Thus, identification of regions with high risk for COVID-19 outbreak and analyzing the behaviour of the infection is a major priority of the governmental organizations and epidemiologists worldwide. The aims of the present study were to analyze the risk factors of coronavirus outbreak for identifying the areas having high risk of infection and to evaluate the behaviour of infection in Fars Province, Iran. A geographic information system (GIS)-based machine learning algorithm (MLA), support vector machine (SVM), was used for the assessment of the outbreak risk of COVID-19 in Fars Province, Iran whereas the daily observations of infected cases were tested in the—polynomial and the autoregressive integrated moving average (ARIMA) models to examine the patterns of virus infestation in the province and in Iran. The results of the disease outbreak in Iran were compared with the data for Iran and the world. Sixteen effective factors were selected for spatial modelling of outbreak risk. The validation outcome reveals that SVM achieved an AUC value of 0.786 (March 20), 0.799 (March 29), and 86.6 (April 10) that displays a good prediction of outbreak risk change detection. The results of the third-degree polynomial and ARIMA models in the province revealed an increasing trend with an evidence of turning, demonstrating extensive quarantines has been effective. The general trends of virus infestation in Iran and Fars Province were similar, although a more volatile growth of the infected cases is expected in the province. The results of this study might assist better programming COVID-19 disease prevention and control and gaining sorts of predictive capability would have wide-ranging benefits.
Hamid Reza Pourghasemi; Soheila Pouyan; Zakariya Farajzadeh; Nitheshnirmal Sadhasivam; Bahram Heidari; Sedigheh Babaei; John P. Tiefenbacher. Assessment of the outbreak risk, mapping and infection behavior of COVID-19: Application of the autoregressive integrated-moving average (ARIMA) and polynomial models. PLoS ONE 2020, 15, e0236238 .
AMA StyleHamid Reza Pourghasemi, Soheila Pouyan, Zakariya Farajzadeh, Nitheshnirmal Sadhasivam, Bahram Heidari, Sedigheh Babaei, John P. Tiefenbacher. Assessment of the outbreak risk, mapping and infection behavior of COVID-19: Application of the autoregressive integrated-moving average (ARIMA) and polynomial models. PLoS ONE. 2020; 15 (7):e0236238.
Chicago/Turabian StyleHamid Reza Pourghasemi; Soheila Pouyan; Zakariya Farajzadeh; Nitheshnirmal Sadhasivam; Bahram Heidari; Sedigheh Babaei; John P. Tiefenbacher. 2020. "Assessment of the outbreak risk, mapping and infection behavior of COVID-19: Application of the autoregressive integrated-moving average (ARIMA) and polynomial models." PLoS ONE 15, no. 7: e0236238.
Snow avalanches can destroy lives and infrastructure and are very important phenomena in some regions of the world. This study maps snow avalanche susceptibility in Sirvan Watershed, Iran, using a new approach. Two statistical models – belief function (Bel) and probability density (PD) – are combined with two learning models – multi-layer perceptron (MLP) and logistic regression (LR) – to predict avalanche susceptibility using remote sensing data in a geographic information system (GIS). A snow avalanche inventory map was generated from Google Earth imagery, regional documentation, and field surveys. Of 101 avalanche locations, 71 (70%) were used to train the models and 30 (30%) were used to validate the resulting models. Fourteen snow avalanche conditioning factors were used as independent variables in the predictive modeling process. First, the weight of Bel and PD techniques were applied to each class of factors. Then, they were combined with two MLP and LR learning models for snow avalanche susceptibility mapping (SASM). The results were validated using positive predictive values, negative predictive values, sensitivity, specificity, accuracy, root-mean-square error, and area-under-the-curve (AUC) values. Thus, the AUCs for the PD-LR, Bel-LR, Bel-MLP, and PD-MLP hybrid models are 0.941, 0.936, 0.931 and 0.924, respectively. Based on the validation results, the PD-LR hybrid model achieved the best accuracy among the models. This hybrid modeling approach can provide accurate and reliable evaluations of snow avalanche-prone areas for management and decision making.
Peyman Yariyan; Mohammadtaghi Avand; Rahim Ali Abbaspour; Mohammadreza Karami; John P. Tiefenbacher. GIS-based spatial modeling of snow avalanches using four novel ensemble models. Science of The Total Environment 2020, 745, 141008 .
AMA StylePeyman Yariyan, Mohammadtaghi Avand, Rahim Ali Abbaspour, Mohammadreza Karami, John P. Tiefenbacher. GIS-based spatial modeling of snow avalanches using four novel ensemble models. Science of The Total Environment. 2020; 745 ():141008.
Chicago/Turabian StylePeyman Yariyan; Mohammadtaghi Avand; Rahim Ali Abbaspour; Mohammadreza Karami; John P. Tiefenbacher. 2020. "GIS-based spatial modeling of snow avalanches using four novel ensemble models." Science of The Total Environment 745, no. : 141008.
Development of zoning and flood-forecasting models is essential for making optimal management decisions before and after floods. The Komijan watershed of Markazi Province, Iran is often affected by floods that have caused great material damage and loss of life. The main objective of this study is to use a new machine-learning method to create three models: best-first decision tree (BFT), a bagging best-first decision tree (BBFT) ensemble and a dagging best-first decision tree (DBFT) ensemble to spatially predict flood probability. Twelve conditioning-factor measures for 272 locations of past floods were used to train and test three models. Receiver operating characteristic (ROC), positive predictive value (PPV), negative predictive value (NPV), sensitivity (SST), specificity (SPF), accuracy (ACC), kappa (K), and root mean square error (RMSE) are applied to compare and validate the models. The results are that all three models performed well in mapping, flood probabilities (AUC > 0.904). The BBFT model was best, however, with an AUC = 0.96. Based on the results of the Relief-F attribute evaluation method, two soil and slope factors were weighted highest among the parameters, indicating that they are the most important flood-conditioning factors. These models may improve identification of zones that are most susceptible to flooding, improving the capacity for risk management and providing more detailed information for managers and decision-makers.
Peyman Yariyan; Saeid Janizadeh; Tran Van Phong; Huu Duy Nguyen; Romulus Costache; Hiep Van Le; Binh Thai Pham; Biswajeet Pradhan; John P. Tiefenbacher. Improvement of Best First Decision Trees Using Bagging and Dagging Ensembles for Flood Probability Mapping. Water Resources Management 2020, 34, 3037 -3053.
AMA StylePeyman Yariyan, Saeid Janizadeh, Tran Van Phong, Huu Duy Nguyen, Romulus Costache, Hiep Van Le, Binh Thai Pham, Biswajeet Pradhan, John P. Tiefenbacher. Improvement of Best First Decision Trees Using Bagging and Dagging Ensembles for Flood Probability Mapping. Water Resources Management. 2020; 34 (9):3037-3053.
Chicago/Turabian StylePeyman Yariyan; Saeid Janizadeh; Tran Van Phong; Huu Duy Nguyen; Romulus Costache; Hiep Van Le; Binh Thai Pham; Biswajeet Pradhan; John P. Tiefenbacher. 2020. "Improvement of Best First Decision Trees Using Bagging and Dagging Ensembles for Flood Probability Mapping." Water Resources Management 34, no. 9: 3037-3053.
In recent years, land uses have been changing and aridity has been increasing in the forests and rangelands of central Koohdasht which is a region in the forests of the Zagros Mountains in western Iran. Consequently, the number of fires has also increased. This study employs data-mining techniques to model fire danger using information regarding land cover, climate, topography, and other fire-danger influencing factors. A land cover map was prepared using Sentinel-2A satellite images and a maximum likelihood (ML) algorithm. Digital data describing other factors that influence fire danger (slope angle, aspect, elevation, climate, topographic wetness index, and distances from rivers and roads) were compiled from several sources and imported into a GIS. The locations of past fires in the study area were also determined from MODIS satellite images and data acquired from the region’s fire service. The quantitative and qualitative spatial relationships between effective factors and patterns of fires were investigated to model fire danger. A new machine-learning algorithm (the Boruta algorithm) was used to assess the relative importance of the fire-danger factors. Fire danger maps were created using several new data-mining algorithms including support vector machine (SVM), generalized linear model (GLM), functional data analysis (FDA), and random forest (RF). All were run in R 3.3.3 software. Finally, the fire danger maps were validated with several indices to determine the model that best predicts the fire danger in Koohdasht County. The results reveal that fire locations were determined mostly by elevation (low), aspect (south and southwest facing slopes), and aridity (semi-arid regions). Most fires occurred in non-natural landscapes: residential areas (46.74% of fires), agricultural lands (25.77%), and gardens (5.42%). In total, 77.93% of fires occurred in non-natural landscapes and within 500 m of roads. Only 22.07% of fires occurred on rangelands and forests. Three factors (distance from roads, climate, and aspect) were the strongest predictors of fire locations in the study area. Furthermore, area-under-the-curve (AUC) values indicate that the FDA (0.777) and GLM (0.772) algorithms generated the most accurate fire danger maps. These results have practical implications for fire danger management in the Zagros forests and provide baseline information for forest managers about the most important factors affecting fire danger in the similar regions. This methodology can be used by forest managers to predict the areas with greatest fire danger to prevent future fires through land use management, planning, and strategic decision-making. The results enable forest managers to find the best methods to monitor, manage, and control fire occurrence based on fire danger maps in the forests of western Iran, or in forests of other regions with similar conditions.
Saeedeh Eskandari; Hamid Reza Pourghasemi; John P. Tiefenbacher. Relations of land cover, topography, and climate to fire occurrence in natural regions of Iran: Applying new data mining techniques for modeling and mapping fire danger. Forest Ecology and Management 2020, 473, 118338 .
AMA StyleSaeedeh Eskandari, Hamid Reza Pourghasemi, John P. Tiefenbacher. Relations of land cover, topography, and climate to fire occurrence in natural regions of Iran: Applying new data mining techniques for modeling and mapping fire danger. Forest Ecology and Management. 2020; 473 ():118338.
Chicago/Turabian StyleSaeedeh Eskandari; Hamid Reza Pourghasemi; John P. Tiefenbacher. 2020. "Relations of land cover, topography, and climate to fire occurrence in natural regions of Iran: Applying new data mining techniques for modeling and mapping fire danger." Forest Ecology and Management 473, no. : 118338.
Gully erosion has become one of the major environmental issues, due to the severity of its impact in many parts of the world. Gully erosion directly and indirectly affects agriculture and infrastructural development. The Golestan Dam basin, where soil erosion and degradation are very severe problems, was selected as the study area. This research maps gully erosion susceptibility (GES) by integrating four models: maximum entropy (MaxEnt), artificial neural network (ANN), support vector machine (SVM), and general linear model (GLM). Of 1042 gully locations, 729 (70%) and 313 (30%) gully locations were used for modeling and validation purposes, respectively. Fourteen effective gully erosion conditioning factors (GECFs) were selected for spatial gully erosion modeling. Tolerance and variance inflation factors (VIFs) were used to examine the collinearity among the GECFs. The random forest (RF) model was used to assess factors’ effectiveness and significance in gully erosion modeling. An ensemble of techniques can provide more accurate results than can single, standalone models. Therefore, we compared two-, three-, and four-model ensembles (ANN-SVM, GLM-ANN, GLM-MaxEnt, GLM-SVM, MaxEnt-ANN, MaxEnt-SVM, ANN-SVM-GLM, GLM-MaxEnt-ANN, GLM-MaxEnt-SVM, MaxEnt-ANN-SVM and GLM-ANN-SVM-MaxEnt) for GES modeling. The susceptibility zones of the GESMs were classified as very-low, low, medium, high, and very-high using Jenks’ natural break classification method (NBM). Subsequently, the receiver operating characteristics (ROC) curve and the seed cell area index (SCAI) methods measured the reliability of the models. The success rate curve (SRC) and predication rate curve (PRC) and their area under the curve (AUC) values were obtained from the GES maps. The results show that the ANN model combined with two and three models are more accurate than the other combinations, but the ANN-SVM model had the highest accuracy. The rank of the others from best to worst accuracy is GLM, MaxEnt, SVM, GLM-ANN, GLM-MaxEnt, GLM-SVM, MaxEnt-ANN, MaxEnt-SVM, GLM-ANN-SVM-MaxEnt, GLM-MaxEnt-ANN, GLM-MaxEnt-SVM and MaxEnt-ANN-SVM. The resulting gully erosion susceptibility models (GESMs) are efficient and powerful and could be used to improve soil and water conservation and management.
Alireza Arabameri; Omid Asadi Nalivan; Sunil Saha; Jagabandhu Roy; Biswajeet Pradhan; John Tiefenbacher; Phuong Thi Ngo. Novel Ensemble Approaches of Machine Learning Techniques in Modeling the Gully Erosion Susceptibility. Remote Sensing 2020, 12, 1890 .
AMA StyleAlireza Arabameri, Omid Asadi Nalivan, Sunil Saha, Jagabandhu Roy, Biswajeet Pradhan, John Tiefenbacher, Phuong Thi Ngo. Novel Ensemble Approaches of Machine Learning Techniques in Modeling the Gully Erosion Susceptibility. Remote Sensing. 2020; 12 (11):1890.
Chicago/Turabian StyleAlireza Arabameri; Omid Asadi Nalivan; Sunil Saha; Jagabandhu Roy; Biswajeet Pradhan; John Tiefenbacher; Phuong Thi Ngo. 2020. "Novel Ensemble Approaches of Machine Learning Techniques in Modeling the Gully Erosion Susceptibility." Remote Sensing 12, no. 11: 1890.
In this study, Dempster–Shafer theory (DST) is integrated into a geographic information system to model vulnerability of the land surface to earthquake events in northwestern Kermanshah Province, Iran, to predict where damage is most likely to occur. DST has never been used to spatially model earthquake vulnerability. To achieve this, data layers for several environmental attributes—aspect, elevation, lithology, slope angle, land use, distance from river courses, distance from roads, and distance from faults—were compiled in ArcGIS 10.2.2 software. Using membership functions, fuzzy maps were generated for each parameter. These fuzzy maps provided input data for the DST model. The predicted values were analyzed and compared at three confidence levels to determine the effectiveness of the model. The results are that 11.14%, 14.14%, and 17.18% (95%, 99%, and 99.5% confidence levels, respectively) of the study area are predicted to be susceptible to earthquakes based on receiver operating characteristic curves. The results also show that, according to the area under the curve (AUC) values (0.967, 0.828, and 0.849 for 95%, 99%, and 99.5% confidence levels, respectively), DST model generates earthquake zoning maps with high accuracy. Therefore, this model can be used for generating earthquake zoning maps with confidence levels that best suit the economic conditions and significance of the region.
Marzieh Mokarram; Hamid Reza Pourghasemi; John P. Tiefenbacher. Using Dempster–Shafer theory to model earthquake events. Natural Hazards 2020, 103, 1943 -1959.
AMA StyleMarzieh Mokarram, Hamid Reza Pourghasemi, John P. Tiefenbacher. Using Dempster–Shafer theory to model earthquake events. Natural Hazards. 2020; 103 (2):1943-1959.
Chicago/Turabian StyleMarzieh Mokarram; Hamid Reza Pourghasemi; John P. Tiefenbacher. 2020. "Using Dempster–Shafer theory to model earthquake events." Natural Hazards 103, no. 2: 1943-1959.
Dust particles are transported globally. Dust storms can adversely impact both human health and the environment, but they also impact transportation infrastructure, agriculture, and industry, occasionally severely. The identification of the locations that are the primary sources of dust, especially in arid and semi-arid environments, remains a challenge as these sites are often in remote or data-scarce regions. In this study, a new method using state-of-the-art machine-learning algorithms – random forest (RF), support vector machines (SVM), and multivariate adaptive regression splines (MARS) – was evaluated for its ability to spatially model the distribution of dust-source potential in eastern Iran. To accomplish this, empirically identified dust-source locations were determined with the ozone monitoring instrument aerosol index and the Moderate-Resolution Imaging Spectroradiometer (MODIS) Deep Blue aerosol optical thickness methods. The identified areas were divided into training (70%) and validation (30%) sets. Measurements of the conditioning factors (lithology, wind speed, maximum air temperature, land use, slope angle, soil, rainfall, and land cover) were compiled for the study area and predictive models were developed. The area-under-the-receiver operating characteristics curve (AUC) and true-skill statistics (TSS) were used to validate the maps of the models' predictions. The results show that the RF algorithm performed best (AUC = 89.4% and TSS = 0.751), followed by the SVM (AUC = 87.5%, TSS = 0.73) and the MARS algorithm (AUC = 81%, TSS = 0.69). The results of the RF indicated that wind speed and land cover are the most important factors affecting dust generation. The region of highest dust-source potential that was identified by the RF is in the eastern parts of the study region. This model can be applied to other arid and semi-arid environments that experience dust storms to promote management that prevents desertification and reduces dust production.
Omid Rahmati; Farnoush Mohammadi; Seid Saeid Ghiasi; John Tiefenbacher; Davoud Davoudi Moghaddam; Frederic Coulon; Omid Asadi Nalivan; Dieu Tien Bui. Identifying sources of dust aerosol using a new framework based on remote sensing and modelling. Science of The Total Environment 2020, 737, 139508 .
AMA StyleOmid Rahmati, Farnoush Mohammadi, Seid Saeid Ghiasi, John Tiefenbacher, Davoud Davoudi Moghaddam, Frederic Coulon, Omid Asadi Nalivan, Dieu Tien Bui. Identifying sources of dust aerosol using a new framework based on remote sensing and modelling. Science of The Total Environment. 2020; 737 ():139508.
Chicago/Turabian StyleOmid Rahmati; Farnoush Mohammadi; Seid Saeid Ghiasi; John Tiefenbacher; Davoud Davoudi Moghaddam; Frederic Coulon; Omid Asadi Nalivan; Dieu Tien Bui. 2020. "Identifying sources of dust aerosol using a new framework based on remote sensing and modelling." Science of The Total Environment 737, no. : 139508.
Infectious disease outbreaks pose a significant threat to human health worldwide. The outbreak of pandemic coronavirus disease 2019 (COVID-2019) has caused a global health emergency. Identification of regions with high risk for COVID-19 outbreak is a major priority of the governmental organizations and epidemiologists worldwide. The aims of the present study were to analyze the risk factors of coronavirus outbreak and identify areas with a high risk of human infection with virus in Fars Province, Iran. A geographic information system (GIS)-based machine learning algorithm (MLA), support vector machine (SVM), was used for the assessment of the outbreak risk of COVID-19 in Fars Province, Iran. The daily observations of infected cases was tested in the third-degree polynomial and the autoregressive and moving average (ARMA) models to examine the patterns of virus infestation in the province and in Iran. The results of disease outbreak in Iran were compared with the data for Iran and the world. Sixteen effective factors including minimum temperature of coldest month (MTCM), maximum temperature of warmest month (MTWM), precipitation in wettest month (PWM), precipitation of driest month (PDM), distance from roads, distance from mosques, distance from hospitals, distance from fuel stations, human footprint, density of cities, distance from bus stations, distance from banks, distance from bakeries, distance from attraction sites, distance from automated teller machines (ATMs), and density of villages – were selected for spatial modelling. The predictive ability of an SVM model was assessed using the receiver operator characteristic – area under the curve (ROC-AUC) validation technique. The validation outcome reveals that SVM achieved an AUC value of 0.786 (March 20), 0.799 (March 29), and 86.6 (April 10) a good prediction of change detection. The growth rate (GR) average for active cases in Fars for a period of 41 days was 1.26, whilst it was 1.13 in country and the world. The results of the third-degree polynomial and ARMA models revealed an increasing trend for GR with an evidence of turning, demonstrating extensive quarantines has been effective. The general trends of virus infestation in Iran and Fars Province were similar, although an explosive growth of the infected cases is expected in the country. The results of this study might assist better programming COVID-19 disease prevention and control and gaining sorts of predictive capability would have wide-ranging benefits.
Hamid Reza Pourghasemi; Soheila Pouyan; Zakariya Farajzadeh; Nitheshnirmal Sadhasivam; Bahram Heidari; Sedigheh Babaei; John P. Tiefenbacher. Assessment of the outbreak risk, mapping and infestation behavior of COVID-19: Application of the autoregressive and moving average (ARMA) and polynomial models. 2020, 1 .
AMA StyleHamid Reza Pourghasemi, Soheila Pouyan, Zakariya Farajzadeh, Nitheshnirmal Sadhasivam, Bahram Heidari, Sedigheh Babaei, John P. Tiefenbacher. Assessment of the outbreak risk, mapping and infestation behavior of COVID-19: Application of the autoregressive and moving average (ARMA) and polynomial models. . 2020; ():1.
Chicago/Turabian StyleHamid Reza Pourghasemi; Soheila Pouyan; Zakariya Farajzadeh; Nitheshnirmal Sadhasivam; Bahram Heidari; Sedigheh Babaei; John P. Tiefenbacher. 2020. "Assessment of the outbreak risk, mapping and infestation behavior of COVID-19: Application of the autoregressive and moving average (ARMA) and polynomial models." , no. : 1.
Land subsidence (LS) is a significant problem that can cause loss of life, damage property, and disrupt local economies. The Semnan Plain is an important part of Iran, where LS is a major problem for sustainable development and management. The plain represents the changes occurring in 40% of the country. We introduce a novel-ensemble intelligence approach (called ANN-bagging) that uses bagging as a meta- or ensemble-classifier of an artificial neural network (ANN) to predict LS spatially on the Semnan Plain in Semnan Province, Iran. The ensemble model's goodness-of-fit (to training data) and prediction accuracy (of the validation data) are compared to benchmarks set by ANN-bagging. A total of 96 locations of LS and 12 LS conditioning factors (LSCFs) were collected. Each feature in the LS inventory map (LSIM) was randomly assigned to one of four groups or folds, each comprising 25% of cases. The novel ensemble model was trained using 75% (3 folds) and validated with the remaining 25% (1 fold) in a four-fold cross-validation (CV) system, which is used to control for the effects of the random selection of the training and validation datasets. LSCFs for LS prediction were selected using the information-gain ratio and multi-collinearity test methods. Factor significance was evaluated using a random forest (RF) model. Groundwater drawdown, land use and land cover, elevation, and lithology were the most important LSCFs. Using the k-fold CV approaches, twelve LS susceptibility maps (LSSMs) were prepared as each fold employed all three models (ANN-bagging, ANN, and bagging). The LS susceptibility mapping showed that between 5.7% and 12.6% of the plain had very high LS susceptibility. All three models produced LS susceptibility maps with acceptable prediction accuracies and goodness-of-fits, but the best maps were produced by the ANN-bagging ensemble method. Overall, LS risk was highest in agricultural areas with high groundwater drawdown in the flat lowlands on quaternary sediments (Qcf). Groundwater extraction rates should be monitored and potentially limited in regions of severe or high LS susceptibility. This investigation details a novel methodology that can help environmental planners and policy makers to mitigate LS to help achieve sustainability.
Alireza Arabameri; Sunil Saha; Jagabandhu Roy; John P. Tiefenbacher; Artemi Cerda; Trent Biggs; Biswajeet Pradhan; Phuong Thao Thi Ngo; Adrian L. Collins. A novel ensemble computational intelligence approach for the spatial prediction of land subsidence susceptibility. Science of The Total Environment 2020, 726, 138595 .
AMA StyleAlireza Arabameri, Sunil Saha, Jagabandhu Roy, John P. Tiefenbacher, Artemi Cerda, Trent Biggs, Biswajeet Pradhan, Phuong Thao Thi Ngo, Adrian L. Collins. A novel ensemble computational intelligence approach for the spatial prediction of land subsidence susceptibility. Science of The Total Environment. 2020; 726 ():138595.
Chicago/Turabian StyleAlireza Arabameri; Sunil Saha; Jagabandhu Roy; John P. Tiefenbacher; Artemi Cerda; Trent Biggs; Biswajeet Pradhan; Phuong Thao Thi Ngo; Adrian L. Collins. 2020. "A novel ensemble computational intelligence approach for the spatial prediction of land subsidence susceptibility." Science of The Total Environment 726, no. : 138595.
The morphometric characteristics of the Kalvārī basin were analyzed to prioritize sub-basins based on their susceptibility to erosion by water using a remote sensing-based data and a GIS. The morphometric parameters (MPs)—linear, relief, and shape—of the drainage network were calculated using data from the Advanced Land-observing Satellite (ALOS) phased-array L-type synthetic-aperture radar (PALSAR) digital elevation model (DEM) with a spatial resolution of 12.5 m. Interferometric synthetic aperture radar (InSAR) was used to generate the DEM. These parameters revealed the network’s texture, morpho-tectonics, geometry, and relief characteristics. A complex proportional assessment of alternatives (COPRAS)-analytical hierarchy process (AHP) novel-ensemble multiple-criteria decision-making (MCDM) model was used to rank sub-basins and to identify the major MPs that significantly influence erosion landforms of the Kalvārī drainage basin. The results show that in evolutionary terms this is a youthful landscape. Rejuvenation has influenced the erosional development of the basin, but lithology and relief, structure, and tectonics have determined the drainage patterns of the catchment. Results of the AHP model indicate that slope and drainage density influence erosion in the study area. The COPRAS-AHP ensemble model results reveal that sub-basin 1 is the most susceptible to soil erosion (SE) and that sub-basin 5 is least susceptible. The ensemble model was compared to the two individual models using the Spearman correlation coefficient test (SCCT) and the Kendall Tau correlation coefficient test (KTCCT). To evaluate the prediction accuracy of the ensemble model, its results were compared to results generated by the modified Pacific Southwest Inter-Agency Committee (MPSIAC) model in each sub-basin. Based on SCCT and KTCCT, the ensemble model was better at ranking sub-basins than the MPSIAC model, which indicated that sub-basins 1 and 4, with mean sediment yields of 943.7 and 456.3 m 3 km − 2 year − 1 , respectively, have the highest and lowest SE susceptibility in the study area. The sensitivity analysis revealed that the most sensitive parameters of the MPSIAC model are slope (R2 = 0.96), followed by runoff (R2 = 0.95). The MPSIAC shows that the ensemble model has a high prediction accuracy. The method tested here has been shown to be an effective tool to improve sustainable soil management.
Alireza Arabameri; John P. Tiefenbacher; Thomas Blaschke; Biswajeet Pradhan; Dieu Tien Bui. Morphometric Analysis for Soil Erosion Susceptibility Mapping Using Novel GIS-Based Ensemble Model. Remote Sensing 2020, 12, 874 .
AMA StyleAlireza Arabameri, John P. Tiefenbacher, Thomas Blaschke, Biswajeet Pradhan, Dieu Tien Bui. Morphometric Analysis for Soil Erosion Susceptibility Mapping Using Novel GIS-Based Ensemble Model. Remote Sensing. 2020; 12 (5):874.
Chicago/Turabian StyleAlireza Arabameri; John P. Tiefenbacher; Thomas Blaschke; Biswajeet Pradhan; Dieu Tien Bui. 2020. "Morphometric Analysis for Soil Erosion Susceptibility Mapping Using Novel GIS-Based Ensemble Model." Remote Sensing 12, no. 5: 874.