• Energy Research

Leaf Area Index (LAI; as an indicator of the health) of the mangrove ecosystems on the northern coasts of the Persian Gulf and the Gulf of Oman was measured in the field and modeled in response to observed (1986-2017) and predicted (2018-2100) drought occurrences (quantified using the Standardized Precipitation Index [SPI]). The relationship of LAI with the normalized difference vegetation index (NDVI) obtained from satellite images was quantified, the LAI between 1986 and 2017 retrospectively estimated, and a relationship between LAI and SPI developed for the same period. Long-term climate data were used as input in the RCP8.5 climate change scenario to reconstruct recent and forecast future drought intensities. Both the NDVI and the SPI were strongly related with the LAI, indicating that realistic LAI values were derived from historic satellite data to portray annual changes of LAI in response to changes in SPI. Our findings show that projected future drought intensities modeled by the RCP8.5 scenario increase more and future LAIs decreased more on the coasts of the Gulf of Oman than the coasts of the Persian Gulf in the coming decades. The year 1998 was the most significant change-point for mean annual rainfall amounts and drought occurrences as well as for LAIs and at no time between 1998 and 2017 or between 2018 and 2100 are SPI and LAI values expected to return to pre-1998 values. LAI and SPI are projected to decline sharply around 2030, reach their lowest levels between 2040 and 2070, and increase and stabilize during the late decades of the 21st century at values similar to the present time. Overall, this study provides a comprehensive picture of the responses of mangroves to fluctuating future drought conditions, facilitating the development of management plans for these vulnerable habitats in the face of future climate change.

Leaf Area Index (LAI; as an indicator of the health) of the mangrove ecosystems on the northern coasts of the Persian Gulf and the Gulf of Oman was measured in the field and modeled in response to observed (1986-2017) and predicted (2018-2100) drought occurrences (quantified using the Standardized Precipitation Index [SPI]). The relationship of LAI with the normalized difference vegetation index (NDVI) obtained from satellite images was quantified, the LAI between 1986 and 2017 retrospectively estimated, and a relationship between LAI and SPI developed for the same period. Long-term climate data were used as input in the RCP8.5 climate change scenario to reconstruct recent and forecast future drought intensities. Both the NDVI and the SPI were strongly related with the LAI, indicating that realistic LAI values were derived from historic satellite data to portray annual changes of LAI in response to changes in SPI. Our findings show that projected future drought intensities modeled by the RCP8.5 scenario increase more and future LAIs decreased more on the coasts of the Gulf of Oman than the coasts of the Persian Gulf in the coming decades. The year 1998 was the most significant change-point for mean annual rainfall amounts and drought occurrences as well as for LAIs and at no time between 1998 and 2017 or between 2018 and 2100 are SPI and LAI values expected to return to pre-1998 values. LAI and SPI are projected to decline sharply around 2030, reach their lowest levels between 2040 and 2070, and increase and stabilize during the late decades of the 21st century at values similar to the present time. Overall, this study provides a comprehensive picture of the responses of mangroves to fluctuating future drought conditions, facilitating the development of management plans for these vulnerable habitats in the face of future climate change.

Floods are some of the most destructive and catastrophic disasters worldwide. Development of management plans needs a deep understanding of the likelihood and magnitude of future flood events. The purpose of this research was to estimate flash flood susceptibility in the Tafresh watershed, Iran, using five machine learning methods, i.e., alternating decision tree (ADT), functional tree (FT), kernel logistic regression (KLR), multilayer perceptron (MLP), and quadratic discriminant analysis (QDA). A geospatial database including 320 historical flood events was constructed and eight geo-environmental variables—elevation, slope, slope aspect, distance from rivers, average annual rainfall, land use, soil type, and lithology—were used as flood influencing factors. Based on a variety of performance metrics, it is revealed that the ADT method was dominant over the other methods. The FT method was ranked as the second-best method, followed by the KLR, MLP, and QDA. Given a few differences between the goodness-of-fit and prediction success of the methods, we concluded that all these five machine-learning-based models are applicable for flood susceptibility mapping in other areas to protect societies from devastating floods.

Floods are some of the most destructive and catastrophic disasters worldwide. Development of management plans needs a deep understanding of the likelihood and magnitude of future flood events. The purpose of this research was to estimate flash flood susceptibility in the Tafresh watershed, Iran, using five machine learning methods, i.e., alternating decision tree (ADT), functional tree (FT), kernel logistic regression (KLR), multilayer perceptron (MLP), and quadratic discriminant analysis (QDA). A geospatial database including 320 historical flood events was constructed and eight geo-environmental variables—elevation, slope, slope aspect, distance from rivers, average annual rainfall, land use, soil type, and lithology—were used as flood influencing factors. Based on a variety of performance metrics, it is revealed that the ADT method was dominant over the other methods. The FT method was ranked as the second-best method, followed by the KLR, MLP, and QDA. Given a few differences between the goodness-of-fit and prediction success of the methods, we concluded that all these five machine-learning-based models are applicable for flood susceptibility mapping in other areas to protect societies from devastating floods.

Estimating the diameter increment of forests is one of the most important relationships in forest management and planning. The aim of this study was to provide insight into the application of two machine learning methods, i.e., the multilayer perceptron artificial neural network (MLP) and adaptive neuro-fuzzy inference system (ANFIS), for developing diameter increment models for the Hyrcanian forests. For this purpose, the diameters at breast height (DBH) of seven tree species were recorded during two inventory periods. The trees were divided into four broad species groups, including beech (Fagus orientalis), chestnut-leaved oak (Quercus castaneifolia), hornbeam (Carpinus betulus), and other species. For each group, a separate model was developed. The k-fold strategy was used to evaluate these models. The Pearson correlation coefficient (r), coefficient of determination (R2), root mean square error (RMSE), Akaike information criterion (AIC), and Bayesian information criterion (BIC) were utilized to evaluate the models. RMSE and R2 of the MLP and ANFIS models were estimated for the four groups of beech ((1.61 and 0.23) and (1.57 and 0.26)), hornbeam ((1.42 and 0.13) and (1.49 and 0.10)), chestnut-leaved oak ((1.55 and 0.28) and (1.47 and 0.39)), and other species ((1.44 and 0.32) and (1.5 and 0.24)), respectively. Despite the low coefficient of determination, the correlation test in both techniques was significant at a 0.01 level for all four groups. In this study, we also determined optimal network parameters such as number of nodes of one or multiple hidden layers and the type of membership functions for modeling the diameter increment in the Hyrcanian forests. Comparison of the results of the two techniques showed that for the groups of beech and chestnut-leaved oak, the ANFIS technique performed better and that the modeling techniques have a deep relationship with the nature of the tree species.

Estimating the diameter increment of forests is one of the most important relationships in forest management and planning. The aim of this study was to provide insight into the application of two machine learning methods, i.e., the multilayer perceptron artificial neural network (MLP) and adaptive neuro-fuzzy inference system (ANFIS), for developing diameter increment models for the Hyrcanian forests. For this purpose, the diameters at breast height (DBH) of seven tree species were recorded during two inventory periods. The trees were divided into four broad species groups, including beech (Fagus orientalis), chestnut-leaved oak (Quercus castaneifolia), hornbeam (Carpinus betulus), and other species. For each group, a separate model was developed. The k-fold strategy was used to evaluate these models. The Pearson correlation coefficient (r), coefficient of determination (R2), root mean square error (RMSE), Akaike information criterion (AIC), and Bayesian information criterion (BIC) were utilized to evaluate the models. RMSE and R2 of the MLP and ANFIS models were estimated for the four groups of beech ((1.61 and 0.23) and (1.57 and 0.26)), hornbeam ((1.42 and 0.13) and (1.49 and 0.10)), chestnut-leaved oak ((1.55 and 0.28) and (1.47 and 0.39)), and other species ((1.44 and 0.32) and (1.5 and 0.24)), respectively. Despite the low coefficient of determination, the correlation test in both techniques was significant at a 0.01 level for all four groups. In this study, we also determined optimal network parameters such as number of nodes of one or multiple hidden layers and the type of membership functions for modeling the diameter increment in the Hyrcanian forests. Comparison of the results of the two techniques showed that for the groups of beech and chestnut-leaved oak, the ANFIS technique performed better and that the modeling techniques have a deep relationship with the nature of the tree species.

Forest fires are becoming a serious concern in Central European countries such as Austria (AT) and the Czech Republic (CZ). Mapping fire ignition probabilities across countries can be a useful tool for fire risk mitigation. This study was conducted to: (i) evaluate the contribution of the variables obtained from open-source datasets (i.e., MODIS, OpenStreetMap, and WorldClim) for modeling fire ignition probability at the country level; and (ii) investigate how well the Random Forest (RF) method performs from one country to another. The importance of the predictors was evaluated using the Gini impurity method, and RF was evaluated using the ROC-AUC and confusion matrix. The most important variables were the topographic wetness index in the AT model and slope in the CZ model. The AUC values in the validation sets were 0.848 (AT model) and 0.717 (CZ model). When the respective models were applied to the entire dataset, they achieved 82.5% (AT model) and 66.4% (CZ model) accuracy. Cross-comparison revealed that the CZ model may be successfully applied to the AT dataset (AUC = 0.808, Acc = 82.5%), while the AT model showed poor explanatory power when applied to the CZ dataset (AUC = 0.582, Acc = 13.6%). Our study provides insights into the effect of the accuracy and completeness of open-source data on the reliability of national-level forest fire probability assessment.