Using machine learning techniques in multi-hazards assessment of Golestan National Park, Iran

Golestan National Park is one of the oldest biosphere reserves exposed to environmental hazards due to growing demand, geographical location of the park, mountainous conditions, and developments in the last five decades. This study aimed to evaluate potential environmental hazards using machine-learning techniques. This study applied maximum entropy, random forest, boosted regression tree, generalized additive model, and support vector machine methods to model environmental hazards and evaluated the impact of affecting agents and their area of influence. After data collection and preprocessing, the models were implemented, tuned, and trained, and their accuracies were determined using the “receiver operating characteristic curve”. The results indicate the high importance of climatic and human variables, including rainfall, temperature, presence of shepherds, and villagers for fire hazards, elevation, transit roads, temperature, and rainfall for the formation of floodplains, and elevation, transit roads, rainfall, and topographic wetness index in the occurrence of landslides in the national park. The boosted regression tree model with a “AUC value” of 0.98 for flooding, 0.97 for fire, and 0.93 for landslides hazards, had the best performance. The modeling estimated that, on average, 16.2% of the area of Golestan National Park has a high potential for landslides, 14% has a high potential for fire, and 7.2% has a high potential for flooding. So, results of this study can be applied by land use planners, decision makers, and managers of various organizations to decrease effects of these hazards Golestan National Park (GNP).</p

Performance assessment of individual and ensemble data-mining techniques for gully erosion modeling

Gully erosion is identified as an important sediment source in a range of environments and plays a conclusive role in redistribution of eroded soils on a slope. Hence, addressing spatial occurrence pattern of this phenomenon is very important. Different ensemble models and their single counterparts, mostly data mining methods, have been used for gully erosion susceptibility mapping; however, their calibration and validation procedures need to be thoroughly addressed. The current study presents a series of individual and ensemble dataminingmethods including artificial neural network (ANN), support vector machine (SVM), maximum entropy (ME), ANN-SVM, ANN-ME, and SVM-ME to map gully erosion susceptibility in Aghemam watershed, Iran. To this aim, a gully inventory map along with sixteen gully conditioning factors was used. A 70:30% randomly partitioned sets were used to assess goodness-of-fit and prediction power of the models. The robustness, as the stability ofmodels' performance in response to changes in the dataset, was assessed through three training/test replicates. As a result, conducted preliminary statistical tests showed that ANN has the highest concordance and spatial differentiation with a chi-square value of 36,656 at 95% confidence level,while theME appeared to have the lowest concordance (1772). The ME model showed an impractical result where 45% of the study area was introduced as highly susceptible to gullying, in contrast, ANN-SVMindicated a practical resultwith focusing only on 34% of the study area. Through all three replicates, the ANN-SVM ensemble showed the highest goodness-of-fit and predictive power with a respective values of 0.897 (area under the success rate curve) and 0.879 (area under the prediction rate curve), on average, and correspondingly the highest robustness. This attests the important role of ensemble modeling in congruently building accurate and generalized models which emphasizes the necessity to examine different models integrations. The result of this study can prepare an outline for further biophysical designs on gullies scattered in the study area

Changes in morphometric meander parameters identified on the Karoon River, Iran, using remote sensing data

International audienc

Investigation of plant contamination to Ni, Pb, Zn and Cd and its relationship with spectral reflections

This study aims to investigate the toxicity of the plant to heavy elements (HMs). For this purpose, the estimated daily intake (EDI) parameters of potentially toxic elements (PTE) per kilogram of body weight, target hazard quotient (THQ) for non-carcinogenic disorders, total hazard index (HI), and bioconcentration factor (BCF) are determined in the plant at different stages of growth. In this study, the reaction of the plant to different electromagnetic waves at different stages of growth (DSG) is also investigated, and the relationship between the THQ values and electromagnetic waves is prepared. The results show that Pb has the highest EDI value (5.97), Pb (74.67) and Cd (9.75) have the highest THQ values, and Cd has the highest BCF value (30.44). Also, the results show that HI values are higher than the threshold in the growth (69.98), flowering (71.38), and fruiting (68.06) stages. Results of BCF indicate Pb, and Cd has absorption rate in Capsicum towards. Contaminated Capsicum plants submitted to electromagnetic waves showed a significant relationship between Pb and the b490, and b560 spectra, Cd and Ni the b450 spectrum, and Zn the b460 spectrum. This finding highlights the salience of employing electromagnetic waves in assessing contamination in plants. Put differently, THQ can be estimated using electromagnetic waves without any need for laboratory studies

Effects of urbanization on river morphology of the Talar River, Mazandarn Province, Iran

In the present study, we investigate the effects of urbanization growth on river morphology in the downstream part of Talar River, east of Mazandaran Province, Iran. Morphological and morphometric parameters in 10 equal sub-reaches were defined along a 11.5 km reach of the Talar River after land cover maps were produced for 1955, 1968, 1994, 2005 and 2013. Land cover types changed extremely during the study period. Residential lands were found to have increased in area by about 1631%, while forest land and riparian vegetation decreased in by approximately 99.9 and 96.2%, respectively. The results of morphometric and morphological factors showed that average channel width (W) for all 11.5 km of the study river decreased by 84% during the study period, while the flow length increased by about 2.14%

Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling-Narayanghat road section in Nepal Himalaya

Landslide susceptibility maps are vital for disaster management and for planning development activities in the mountainous country like Nepal. In the present study, landslide susceptibility assessment of Mugling-Narayanghat road and its surrounding area is made using bivariate (certainty factor and index of entropy) and multivariate (logistic regression) models. At first, a landslide inventory map was prepared using earlier reports and aerial photographs as well as by carrying out field survey. As a result, 321 landslides were mapped and out of which 241 (75 %) were randomly selected for building landslide susceptibility models, while the remaining 80 (25 %) were used for validating the models. The effectiveness of landslide susceptibility assessment using GIS and statistics is based on appropriate selection of the factors which play a dominant role in slope stability. In this case study, the following landslide conditioning factors were evaluated: slope gradient; slope aspect; altitude; plan curvature; lithology; land use; distance from faults, rivers and roads; topographic wetness index; stream power index; and sediment transport index. These factors were prepared from topographic map, drainage map, road map, and the geological map. Finally, the validation of landslide susceptibility map was carried out using receiver operating characteristic (ROC) curves. The ROC plot estimation results showed that the susceptibility map using index of entropy model with AUC value of 0.9016 has highest prediction accuracy of 90.16 %. Similarly, the susceptibility maps produced using logistic regression model and certainty factor model showed 86.29 and 83.57 % of prediction accuracy, respectively. Furthermore, the ROC plot showed that the success rate of all the three models performed more than 80 % accuracy (i.e. 89.15 % for IOE model, 89.10 % for LR model and 87.21 % for CF model). Hence, it is concluded that all the models employed in this study showed reasonably good accuracy in predicting the landslide susceptibility of Mugling-Narayanghat road section. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation purpose.ArticleNATURAL HAZARDS. 65(1):135-165 (2013)journal articl

Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling-Narayanghat road section in Nepal Himalaya.

Landslide susceptibility maps are vital for disaster management and for planning development activities in the mountainous country like Nepal. In the present study, landslide susceptibility assessment of Mugling–Narayanghat road and its surrounding area is made using bivariate (certainty factor and index of entropy) and multivariate (logistic regression) models. At first, a landslide inventory map was prepared using earlier reports and aerial photographs as well as by carrying out field survey. As a result, 321 landslides were mapped and out of which 241 (75 %) were randomly selected for building landslide susceptibility models, while the remaining 80 (25 %) were used for validating the models. The effectiveness of landslide susceptibility assessment using GIS and statistics is based on appropriate selection of the factors which play a dominant role in slope stability. In this case study, the following landslide conditioning factors were evaluated: slope gradient; slope aspect; altitude; plan curvature; lithology; land use; distance from faults, rivers and roads; topographic wetness index; stream power index; and sediment transport index. These factors were prepared from topographic map, drainage map, road map, and the geological map. Finally, the validation of landslide susceptibility map was carried out using receiver operating characteristic (ROC) curves. The ROC plot estimation results showed that the susceptibility map using index of entropy model with AUC value of 0.9016 has highest prediction accuracy of 90.16 %. Similarly, the susceptibility maps produced using logistic regression model and certainty factor model showed 86.29 and 83.57 % of prediction accuracy, respectively. Furthermore, the ROC plot showed that the success rate of all the three models performed more than 80 % accuracy (i.e. 89.15 % for IOE model, 89.10 % for LR model and 87.21 % for CF model). Hence, it is concluded that all the models employed in this study showed reasonably good accuracy in predicting the landslide susceptibility of Mugling–Narayanghat road section. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation purpose

Assessment of fractal dimension and geometrical characteristics of the landslides identified in North of Tehran, Iran

The aim of the presented study is to assess the fractal dimension (D) and the geometrical characteristics (length and width) of the landslides identified in North of Tehran, Iran. At first, the landslide locations (528 landslides) were identified by interpretation of aerial photographs, satellite images and field surveys, and then to calculate the fractal dimension (D), we used the computer programming named as FRACEK. In the next step, geometrical characteristics of each landslide such as length (L) and width (W) were calculated by ArcGIS software. The landslide polygons were digitized from the mentioned landslide inventory map and rotated based on movement direction. The fractal dimension for all landslides varied between 1.665 and 1.968. Subsequently, the relationship between the length/width ratios and theirs fractal D values for 528 landslides was calculated. The results showed that correlation coefficients (R), which are different regression models such as exponential, linear, logarithmic, polynomial, and power, between D and L/W ratio are relatively high, respectively (0.75, 0.75, 0.76, 0.78, and 0.75). It can be concluded that the fractal dimension values and geometry characteristics of landslides would be useful indices for the management of hazardous areas, susceptible slopes, land use planning, and landslide hazard mitigation

Assessment and comparison of combined bivariate and AHP models with logistic regression for landslide susceptibility mapping in the Chaharmahal-e-Bakhtiari Province, Iran

Landslide is one of the most important natural hazards that make numerous financial damages and life losses each year in the worldwide. Identifying the susceptible areas and prioritizing them in order to provide an efficient susceptibility management is very vital. In current study, a comparative analysis was made between combined bivariate and AHP models (bivariate-AHP) with a logistic regression. At first, landslide inventory map of the study area was prepared using extensive field surveys and aerial photographs interpretation. In the next step, nine landslide causative factors were selected including altitude, slope percentage, slope aspect, lithology, distance from faults, streams and roads, land use, and precipitation which affect occurrence of the landslides in the study area. Subsequently, landslide susceptibility maps were produced using weighted (AHP) bivariate and logistic regression models. Finally, receiver operating characteristics (ROC) curve was used in order to evaluate the prediction capability of the mentioned models for landslide susceptibility mapping. According to the results, the combined bivariate and AHP models provided slightly higher prediction accuracy than logistic regression model. The combined bivariate and AHP, and logistic regression models had the area under the curve (AUC-ROC) values of 0.914, and 0.865, respectively. The resultant landslide susceptibility maps can be useful in appropriate watershed management practices and for sustainable development in the regions with similar conditions

Soil science challenges in a new era: A transdisciplinary overview of relevant topics

Concise ReviewTransdisciplinary approaches that provide holistic views are essential to properly understand soil processes and the importance of soil to society and will be crucial in the future to integrate distinct disciplines into soil studies. A myriad of challenges faces soil science at the beginning of the 2020s. The main aim of this overview is to assess past achievements and current challenges regarding soil threats such as erosion and soil contamination related to different United Nations sustainable development goals (SDGs) including (1) sustainable food production, (2) ensure healthy lives and reduce environmental risks (SDG3), (3) ensure water availability (SDG6), and (4) enhanced soil carbon sequestration because of climate change (SDG13). Twenty experts from different disciplines related to soil sciences offer perspectives on important research directions. Special attention must be paid to some concerns such as (1) effective soil conservation strategies; (2) new computational technologies, models, and in situ measurements that will bring new insights to in-soil process at spatiotemporal scales, their relationships, dynamics, and thresholds; (3) impacts of human activities, wildfires, and climate change on soil microorganisms and thereby on biogeochemical cycles and water relationships; (4) microplastics as a new potential pollutant; (5) the development of green technologies for soil rehabilitation; and (6) the reduction of greenhouse gas emissions by simultaneous soil carbon sequestration and reduction in nitrous oxide emission. Manuscripts on topics such as these are particularly welcomed in Air, Soil and Water Researchinfo:eu-repo/semantics/publishedVersio