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Sinkholes and landslides occur when parts of a soil collapse mainly in more gentle or steeper slopes respectively, both often triggered by intensive rainfall. These processes often cause problems in the hilly regions in the “Golestan province” of Iran, and their detection is the essential aim for this research. The production of soil landforms maps is typically based on visual interpretation of aerial and satellite images eventually supported by field surveys. Recent advances in the acquisition of images from “unmanned aerial vehicles (UAV)” and of “deep learning (DL)” methods inherited from computer vision have made it feasible to propose semi-automated soil landforms detection methodologies for large areas at an unprecedented spatial resolution. In this study, we evaluate the potential of two cutting-edge DL segmentation models, the vanilla “U-Net model” and the “Attention Deep Supervision Multi-Scale U-Net” model, applied to “UAV”-derived products, to map landslides and sinkholes in a semi-arid environment, the “Golestan Province” (north-east Iran) Results show that our framework can successfully map landslides in a challenging environment (with an F1-score of 69%), and that topographical derivates from “UAV-derived DSM” decrease the capacity of mapping sinkholes of the models calibrated with optical data.

Air pollution, as one of the most significant environmental challenges, has adversely affected the global economy, human health, and ecosystems. Consequently, comprehensive research is being conducted to provide solutions to air quality management. Recently, it has been demonstrated that environmental parameters, including temperature, relative humidity, wind speed, air pressure, and vegetation, interact with air pollutants, such as particulate matter (PM), NO2, SO2, O3, and CO, contributing to frameworks for forecasting air quality. The objective of the present study is to explore these interactions in three Iranian metropolises of Tehran, Tabriz, and Shiraz from 2015 to 2019 and develop a machine learning-based model to predict daily air pollution. Three distinct assessment criteria were used to assess the proposed XGBoost model, including R squared (R2), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). Preliminary results showed that although air pollutants were significantly associated with meteorological factors and vegetation, the formulated model had low accuracy in predicting (R2PM2.5 = 0.36, R2PM10 = 0.27, R2NO2 = 0.46, R2SO2 = 0.41, R2O3 = 0.52, and R2CO = 0.38). Accordingly, future studies should consider more variables, including emission data from manufactories and traffic, as well as sunlight and wind direction. It is also suggested that strategies be applied to minimize the lack of observational data by considering second-and third-order interactions between parameters, increasing the number of simultaneous air pollution and meteorological monitoring stations, as well as hybrid machine learning models based on proximal and satellite data.

Greenhouse gases play a vital role in the climate system by absorbing the longwave infrared radiation and cause warming of the earth’s atmosphere. Therefore, it is important to be aware of the spat...

Over the past decade, monitoring of the carbon cycle has become a major concern accented by the severe impacts of global warming. Here, we develop an information theory-based optimization model using the NSGA-II algorithm that determines an optimum ground-based CO2 monitoring layout with the highest spatial coverage using a finite number of stations. The value of information (VOI) concept is used to assess the efficacy of the monitoring stations given their construction cost. In conjunction with VOI, the entropy theory-in terms of transinformation-is adopted to determine the redundant (overlapping) information rendered by the selected monitoring stations. The developed model is used to determine a ground-based CO2 monitoring layout for Iran, the eighth-ranked country emitting CO2 worldwide. An NSGA-II optimization model provides a tradeoff curve given the objectives of (1) minimizing the size of monitoring network; (2) maximizing VOI, i.e., spatial coverage; and (3) minimizing transinformation, i.e., overlapping information. Borda count method is then employed to select the most appropriate compromise monitoring layout from the Pareto-front solutions given regional priorities and concerns.

Over the past decade, monitoring of the carbon cycle has become a major concern accented by the severe impacts of global warming. Here, we develop an information theory-based optimization model using the NSGA-II algorithm that determines an optimum ground-based CO

This study investigates the accuracy of the ISCCP FD and AIRS surface air and skin temperatures, as well as the ISCCP FD and MODIS surface albedos and aerosol optical depths. The comparisons showed that the AIRS air and skin temperatures were slightly more accurate compared to the surface measurements than those in ISCCP FD. The MODIS surface albedos differed from the ISCCP FD values by no more than 0.02–0.07, but because these differences are mostly at longer wavelengths, they did not change the net solar radiation very much. Therefore, to obtain the best estimate of surface net radiation with the best combination of spatial and temporal resolution, we developed a method to adjust the ISCCP FD surface longwave fluxes using the AIRS surface air and skin temperatures to obtain the higher spatial resolution of the latter, while retaining the 3-hr time intervals of the former.

In arid areas, the variation of air temperature can be considerable, so instantaneous air temperature (Tai) estimation is needed in different environmental researches. In this research, two differe...

Flood and drought have caused several damages in natural and unnatural ecosystems in recent decade. Rainfall prediction can be useful in water resource management. The goal of this study is modeling the monthly precipitation of south east of Iran in South-Baluchistan basin, using artificial neural network (ANN) and stochastic models. This area has an unpredictable and complicated monthly rainfall pattern due to impact of several different precipitation systems of other surrounding regions. SARIMA time series models and Time Delay Neural Network (TDNN) are used in monthly precipitation forecasting. Monthly time series of rainfall during 1351-52 to 1387-88 in selected station were used in this study. Stations selection was based on Geographical distribution and data quality. Comparing the results of models of forecasting showed that TDNN model is superior to SARIMA time series model due to different rainfall systems and very sporadic precipitation in this area.

Recognition of the carbon dioxide (CO2) concentration variations over time is critical for tracing the future changes in climate both globally and regionally. In this study, a time series analysis of atmospheric CO2 concentration and its relationship with precipitation, relative humidity (RH), and vegetation is investigated over Oman. The daily XCO2 data from OCO-2 satellite was obtained from September 2014 to March 2019. The daily RH and precipitation data were also collected from the ground weather stations, and the Normalized Difference Vegetation Index was obtained from MODIS. Oman was studied in four distinct regions where the main emphasis was on the Monsoon Region in the far south. The CO2 concentration time series indicated a significant upward trend over different regions for the study period, with annual cycles being the same for all regions except the Monsoon Region. This is indicative of RH, precipitation, and consequently vegetation cover impact on atmospheric CO2 concentration, resulting in an overall lower annual growth in the Monsoon Region. Simple and multiple correlation analyses of CO2 concentration with mentioned parameters were performed in zero to three-month lags over Oman. They showed high correlations mainly during the rainfall period in the Monsoon Region.

The growing demand and exigency for groundwater resources warrant the demarcation of groundwater spring potential zones (GSPZ) for effective sustainable strategy in groundwater identification, conservation, and management. Here we utilized a novel data mining (DM) ensemble to generate groundwater spring potential maps (GSPMs) by combining RF-BRT (random forest-boosted regression tree), MARS-SVM (multivariate adaptive regression spline-support vector machine) and, FDA-GLM-MDA (functional data analysis-generalized linear model-mixture discriminant analysis). Initially, an aggregate of 1726 groundwater spring locations was collected from the regional water company of Tehran Province and field investigation, in which 1208 springs (70%) were taken for training purposes and the remaining 518 (30%) springs were applied for the validation process. Twelve conditioning factors including DEM (digital elevation model)/elevation, fault density, aspect, rainfall, distance from rivers, distance from faults, slope, MRVBF (multiresolution index of valley bottom flatness), TWI (topographic wetness index), lithology, land use/land cover, and permeability were utilized for mapping process and their importance in predicting the groundwater spring potential. The variable importance (VI) analysis using SVM (support vector machine) reveals that the most significant conditioning factors in the prediction process are rainfall, TWI, DEM-elevation, distance from rivers, slope, distance from faults, and MRVBF. The GSPMs generated from novel data-mining (DM) ensembles were validated using the cut-off reliant (recall, fallout, F-measure, accuracy, precision, specificity, TSS: true skill statistic, Cohen’s kappa, fourfold plot, CCI: corrected classified instances) and cut-off independent (ROC-AUC: receiver operating characteristic-area under the curve) measures. The outcome of the validation measures shows that RF-BRT has the superior values of recall, F-measure, overall accuracy, precision, specificity, TSS, Cohen’s kappa, fourfold plot, CCI followed by MARS-SVM, and FDA-GLM-MDA whereas the AUC value of RF-BRT (0.955), MARS-SVM (0.934), and FDA-GLM-MDA (0.914) also display similar result. The GSPMs generated using the novel DM ensemble models in our study can be utilized by policymakers in implementing the strategies for effective land use planning and sustainable groundwater management.

This study investigates the accuracy of the ISCCP FD and AIRS surface air and skin temperatures, as well as the ISCCP FD and MODIS surface albedos and aerosol optical depths. The comparisons showed that the AIRS air and skin temperatures were slightly more accurate compared to the surface measurements than those in ISCCP FD. The MODIS surface albedos differed from the ISCCP FD values by no more than 0.02–0.07, but because these differences are mostly at longer wavelengths, they did not change the net solar radiation very much. Therefore, to obtain the best estimate of surface net radiation with the best combination of spatial and temporal resolution, we developed a method to adjust the ISCCP FD surface longwave fluxes using the AIRS surface air and skin temperatures to obtain the higher spatial resolution of the latter, while retaining the 3-hr time intervals of the former.