Browsing by Author "Jaafari, Abolfazl"

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    Modeling Tree Growth Responses to Climate Change: A Case Study in Natural Deciduous Mountain Forests
    Bayat, Mahmoud; Knoke, Thomas; Heidari, Sahar; Hamidi, Seyedeh Kosar; Burkhart, Harold E.; Jaafari, Abolfazl (MDPI, 2022-10-31)
    Climate change has significant effects on forest ecosystems around the world. Since tree diameter increment determines forest volume increment and ultimately forest production, an accurate estimate of this variable under future climate change is of great importance for sustainable forest management. In this study, we modeled tree diameter increment under the effects of current and expected future climate change, using multilayer perceptron (MLP) artificial neural networks and linear mixed-effect model in two sites of the Hyrcanian Forest, northern Iran. Using 573 monitoring fixed-area (0.1 ha) plots, we measured and calculated biotic and abiotic factors (i.e., diameter at breast height (DBH), basal area in the largest trees (BAL), basal area (BA), elevation, aspect, slope, precipitation, and temperature). We investigated the effect of climate change in the year 2070 under two reference scenarios; RCP 4.5 (an intermediate scenario) and RCP 8.5 (an extreme scenario) due to the uncertainty caused by the general circulation models. According to the scenarios of climate change, the amount of annual precipitation and temperature during the study period will increase by 12.18 mm and 1.77 °C, respectively. Further, the results showed that the impact of predicted climate change was not very noticeable and the growth at the end of the period decreased by only about 7% annually. The effect of precipitation and temperature on the growth rate, in fact, neutralize each other, and therefore, the growth rate does not change significantly at the end of the period compared to the beginning. Based on the models’ predictions, the MLP model performed better compared to the linear mixed-effect model in predicting tree diameter increment.
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    Prediction Success of Machine Learning Methods for Flash Flood Susceptibility Mapping in the Tafresh Watershed, Iran
    Janizadeh, Saeid; Avand, Mohammadtaghi; Jaafari, Abolfazl; Phong, Tran Van; Bayat, Mahmoud; Ahmadisharaf, Ebrahim; Prakash, Indra; Pham, Binh Thai; Lee, Saro (MDPI, 2019-09-30)
    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.