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Freshwater scarcity is an ever-increasing problem throughout the arid and semi-arid countries, and it often results in poverty. Thus, it is necessary to enhance understanding of freshwater resources availability, particularly for groundwater, and to be able to implement functional water resources plans. This study introduces a novel statistical approach combined with a data-mining ensemble model, through implementing evidential belief function and boosted regression tree (EBF-BRT) algorithms for groundwater potential mapping of the Lordegan aquifer in central Iran. To do so, spring locations are determined and partitioned into two groups for training and validating the individual and ensemble methods. In the next step, 12 groundwater-conditioning factors (GCFs), including topographical and hydrogeological factors, are prepared for the modeling process. The mentioned factors are employed in the application of the EBF model. Then, the EBF values of the GCFs are implemented as input to the BRT algorithm. The results of the modeling process are plotted to produce spring (groundwater) potential maps. To verify the results, the receiver operating characteristics (ROC) test is applied to the model's output. The findings of the test indicated that the areas under the ROC curves are 75 and 82% for the EBF and EBF-BRT models, respectively. Therefore, it can be inferred that the combination of the two techniques could increase the efficacy of these methods in groundwater potential mapping.

In this study, 39 generalized height-diameter prediction models were developed for Oriental beech (Fagus orientalis Lipsky) in the Hyrcanian forest in Iran. Data were collected from 75 permanent sample plots in uneven-aged stands of F. orientalis. A total of 1,067 individual tree height-diameter measurements were available for this study. For model testing a 10-fold cross validation method was used. The goodness of fit of the models was evaluated using six statistical measures including Akaike information criteria, Bayesian information criterion, root mean square error (RMSE), mean error, R 2 and R 2 adj . Results showed that the R 2 ranged from 0.62 to 0.78 and RMSE from 3.3 to 4.7 in the validation phase. Considering all the performance criteria, a model which uses DBH, dominant height, basal area per hectare and number of trees per hectare was found to be the best model to predict the height of Oriental beech from these data.

Predicting landslide occurrences can be difficult. However, failure to do so can be catastrophic, causing unwanted tragedies such as property damage, community displacement, and human casualties. Research into landslide susceptibility mapping (LSM) attempts to alleviate such catastrophes through the identification of landslide prone areas. Computational modelling techniques have been successful in related disaster scenarios, which motivate this work to explore such modelling for LSM. In this research, the potential of supervised machine learning and ensemble learning is investigated. Firstly, the Flexible Discriminant Analysis (FDA) supervised learning algorithm is trained for LSM and compared against other algorithms that have been widely used for the same purpose, namely Generalized Logistic Models (GLM), Boosted Regression Trees (BRT or GBM), and Random Forest (RF). Next, an ensemble model consisting of all four algorithms is implemented to examine possible performance improvements. The dataset used to train and test all the algorithms consists of a landslide inventory map of 227 landslide locations. From these sources, 13 conditioning factors are extracted to be used in the models. Experimental evaluations are made based on True Skill Statistic (TSS), the Receiver Operation characteristic (ROC) curve and kappa index. The results show that the best TSS (0.6986), ROC (0.904) and kappa (0.6915) were obtained by the ensemble model. FDA on its own seems effective at modelling landslide susceptibility from multiple data sources, with performance comparable to GLM. However, it slightly underperforms when compared to GBM (BRT) and RF. RF seems most capable compared to GBM, GLM, and FDA, when dealing with all conditioning factors.

This study predicts forest fire susceptibility in Chaloos Rood watershed in Iran using three machine learning (ML) models—multivariate adaptive regression splines (MARS), support vector machine (SVM), and boosted regression tree (BRT). The study utilizes 14 set of fire predictors derived from vegetation indices, climatic variables, environmental factors, and topographical features. To assess the suitability of the models and estimating the variance and bias of estimation, the training dataset obtained from the Natural Resources Directorate of Mazandaran province was subjected to resampling using cross validation (CV), bootstrap, and optimism bootstrap techniques. Using variance inflation factor (VIF), weight indicating the strength of the spatial relationship of the predictors to fire occurrence was assigned to each contributing variable. Subsequently, the models were trained and validated using the receiver operating characteristics (ROC) area under the curve (AUC) curve. Results of the model validation based on the resampling techniques (non, 5- and 10-fold CV, bootstrap and optimism bootstrap) produced AUC values of 0.78, 0.88, 0.90, 0.86 and 0.83 for the MARS model; 0.82, 0.82, 0.89, 0.87, 0.84 for the SVM and 0.87, 0.90, 0.90, 0.90, 0.91 for the BRT model. Across the individual model, the 10-fold CV performed best in MARS and SVM with AUC values of 0.90 and 0.89. Overall, the BRT outperformed the other models in all ramification with highest AUC value of 0.91 using optimism bootstrap resampling algorithm. Generally, the resampling process enhanced the prediction performance of all the models.

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In arid and semi-arid lands using industrial wastewater for irrigating tree plantations offers a great opportunity to fulfill the purpose of Clean Development Mechanism by sequestering carbon in living tissues as well as in soil. Selection of tree for plantation has a great effect on the goal achievements, especially when the managers deal with afforestation projects rather than reforestation projects. The objective of this study was to quantify the above-and below-ground biomass accumulation and carbon storages of the 17-year-old monoculture plantations of mulberry (Morus alba L.), black locust (Robinia pseudoacacia L.), Eldar pine (Pinus eldarica Medw.) and Arizona cypress (Cupressus arizonica Greene) planted in central Iran. To assess the potential carbon storage, we destructively measured individual above-and below-ground tree biomass and developed and scaled models at stand level. Furthermore, carbon content at three soil depths (0-15, 15-30, 30-45 cm), the litter and the understory were assessed in sample plots. The results showed that the total amount of carbon stored by Eldar pine (36.8 Mg/hm 2) was higher than those stored by the trees in the other three plantations, which were 23.7, 10.0, and 9.6 Mg/hm 2 for Arizona cypress, mulberry and black locust plantations, respectively. For all the species, the above-ground biomass accumulations were higher than those of the below-ground. The root mass fractions of the deciduous were larger than those of the coniferous. Accordingly, the results indicate that the potential carbon storages of the coniferous were higher than those of the deciduous in arid regions.