Monitoring Long-term Mangrove Shoreline Changes along the Northern Coasts of the Persian Gulf and the Oman Sea

Generally, investigating changes in mangrove shorelines is an important step to evaluate whether mangrove ecosystems are expanding or contracting. In this study, the rates of change of mangrove boundaries were investigated along the coasts of the Persian Gulf and the Oman Sea, over a 30-year period. Seaward edges of mangrove forests were extracted from Landsat images of the years 1986, 2000 and 2016 and the Digital Shoreline Analysis System (DSAS) Software was used to implement the Linear Regression Rate (LRR) method to quantify the rates of boundary changes. On average, areas that experienced boundary expansion progressed by 2.55 m yr-1 and those that experienced contraction regressed by -0.38 m yr-1. The maximum rate of expansion was 25.91 m yr-1 and the maximum rate of contraction was -22.45 m yr-1. Mangroves located on the coasts of the Persian Gulf exhibited differential rates of progression and regression at their borders, with expansion rates increasing in an eastward direction toward the coasts of the Oman Sea. However, on the eastern coasts of the Oman Sea, mangroves are characterized by contraction and erosion

Evaluation of mangrove rehabilitation and afforestation in the southern coasts of Iran

The increasing multiple ecosystem services of mangrove forests, especially in the coastal regions have highlighted a need for conservation and afforestation of these forests. However, economic development and activities on the coasts have generated severe pollution issues that caused irreparable damages to the areas and quality of mangrove forests. As a result, rehabilitating the affected areas and forest planting are increasingly important, whereby some form of an assessment is needed to determine their sustainable performance and effectives. This study has used the indicators of forest resource sustainability, and the sustainability of planting sites to evaluate mangrove plantings in Iran’s southern coast. Findings showed that there was a total of 47 mangrove planting sites on the coasts of the three provinces studied with an area of 9584.5 ha. There were 26 afforestation practice sites with an area of 5724 ha, and 21 combined rehabilitation and afforestation practice sites with an area of 3860.5 ha identified in this study. Approximately 76.6% of planting sites had been lost and the remaining areas had experienced an average density drop of 44%. Results of the stability class analysis revealed that 23 planting sites were in an extremely unsustainable state, 15 sites were considered as highly unsustainable, six sites were in a state of tendency to be unsustainable, whereas only three sites were regarded as sustainable. Findings from this study can assist managers and decision makers to review the site selection processes and pattern of successful planting sites, to facilitate better site selection and enhance the monitoring of mangrove rehabilitation or afforestation

Swarm intelligence optimization of the group method of data handling using the cuckoo search and whale optimization algorithms to model and predict landslides

The robustness of landslide prediction models has become a major focus of researchers worldwide. We developed two novel hybrid predictive models that combine the self-organizing, deep-learning group method of data handling (GMDH) with two swarm intelligence optimization algorithms, i.e., cuckoo search algorithm (CSA) and whale optimization algorithm (WOA) for spatially explicit prediction of landslide susceptibility. Eleven landslide-causing factors and 334 historic landslides in a 31,340 km2 landslide-prone area in Iran were used to produce geospatial training and validation datasets. The GMDH model was employed to develop a basic predictive model that was then restructured and its parameters were optimized using the CSA and WOA algorithms, yielding the novel hybrid GMDH-CSA and GMDH-WOA models. The hybrid models were validated and compared to the standalone GMDH model by calculating the area under the receiver operating characteristic (AUC) curve and root mean square error (RMSE). The results demonstrated that the hybrid models overcame the computational shortcomings of the basic GMDH model and significantly improved landslide susceptibility prediction (GMDH-CSA, AUC = 0.909 and RMSE = 0.089; GMDH-WOA, AUC = 0.902 and RMSE = 0.129; standalone GMDH, AUC = 0.791 and RMSE = 0.226). Further, the hybrid models were more robust than the standalone GMDH model, showing consistently excellent performance when the training and validation datasets were changed. Overall, the swarm intelligence-optimized models, but not the standalone model, identified the best trade-offs among objectives, accuracy, and robustness

Kinetic and Isotherm Study of Lead Adsorption from Synthetic Effluent by Eucalyptus Sawdust

Lead is a heavy metal which has many applications in different industries. Due to toxicity of lead, discharging industrial effluents which contain these ions will bring irreversible risks to the environment and living ecosystems. The objective of this study is to analyse the use of Eucalyptus sawdust as a cheap adsorbent for lead removal from effluent. The experiments were conducted in batch system and the effect of pH, the amount of adsorbent, contact time and the initial concentration of lead were examined. Noticing the results, the maximum efficiency of lead adsorption is 96.25% which was obtained in pH of 7 and contact time of 30 minutes and 10 g/L of adsorbent. By increasing the initial concentration of lead, the adsorbed metal and removal percentage also increased. Achieved data from this study indicated a good compatibility with Langmuir adsorption isotherm. Kinetic analysis indicated that lead adsorption matches with the second-order kinetic adsorption model (R2=0.998). Noticing the high efficiency of lead removal by Eucalyptus sawdust, this method could be used as an effective and cheap adsorbent for lead removal

Wildfire hazard mapping using an ensemble method of frequency ratio with Shannon’s entropy

This study investigates the capability of frequency ratio and an ensemble method of frequency ratio with Shannon’s entropy to produce a reliable map of wildfire susceptibility for Chaharmahal and Bakhtiari province, Iran. At first, the fire locations were identified in the study area from historical archives and field surveys. Ninety two cases (70%) out of 132 detected fire locations were randomly selected for modeling, and the remaining 40 (30 %) cases were used for the validation. Thirteen fire conditioning factors representing topography, climate, and human activities of the study area were extracted from the spatial database. Using the frequency ratio and the ensemble model, the relationship between the conditioning factors and fire locations were explored. The results were then used to produce distribution maps of wildfire hazard. The verification analysis using Receiver Operating Characteristic (ROC) curves and the Areas Under the Curve (AUC) revealed that the ensemble model with the capability of computing the weights of factors and their categories is more efficient than frequency ratio. The success and prediction rates for the frequency ratio and ensemble model were found to be 79.2 and 75.72%, and 85.16 and 82.92%, respectively. Further, the results suggest that more than one-third of the study area falls into the high and very high hazard classes, and the conditioning factors of land use, soil types, and distance from roads play major roles in fire occurrence and distribution in the study area