Leila Rahimi - Academia.edu (original) (raw)
Papers by Leila Rahimi
Scientific Reports, Mar 4, 2021
<p>In a network of binarized precipitation (i.e., w... more <p>In a network of binarized precipitation (i.e., wet or dry value), the connection or dependence between each pair of nodes can occur following one or more of the following conditions: wet‐wet, dry‐dry, wet‐dry, or dry‐wet. Here, we firstly investigate the different types of dependence, year by year, within a precipitation network of binarized variables. We compare the sample estimate of the probability of co‐occurrence (or occurrence with a lag time within ±3 days) of each of the four possible combinations with respect to the correspondent confidence interval in hypothesis of independence. We develop a procedure to efficiently assess the dependence behavior of all couples of nodes within the network and apply the methodology to a network of rain gauges covering Europe and north Africa.</p>
This research examines the ability of soft computing approaches (i.e. Linear Regression (LR), Gau... more This research examines the ability of soft computing approaches (i.e. Linear Regression (LR), Gaussian Process regression (GP), Adaptive neuro-fuzzy inference system (ANFIS),Support Vector Machine (SVM) and deep neural network (DNN)) to predict the undrained shear strength (SU) of soil mixed waste crushed tires. Data set consisting of 72 different samples were used and obtained from the laboratory experiments. Out of 72 experimental observations randomly separated 50 observations were selected for model development whereas residual 22 were selected for the validation of the developed models. Input data set consist of vertical stress, percentage of the crushed tire, percentage of clay, size of clay, specific gravity of tires, Liquid limit, Plastic limit and Specific gravity of clay samples were considered as inputs whereas undrained shear strength of stabilized soil using waste crushed tires material (SU) was considered as output. Five most popular goodness fit assessment parameters ...
Compound weather events may lead to extreme impacts that can affect many aspects of society inclu... more Compound weather events may lead to extreme impacts that can affect many aspects of society including agriculture. Identifying the underlying mechanisms that cause extreme impacts, such as crop failure, is of crucial importance to improve their understanding and forecasting. In this study, we investigate whether key meteorological drivers of extreme impacts can be identified using the least absolute shrinkage and selection operator (LASSO) in a model environment, a method that allows for automated variable selection and is able to handle collinearity between variables. As an example of an extreme impact, we investigate crop failure using annual wheat yield as simulated by the Agricultural Production Systems sIMulator (APSIM) crop model driven by 1600 years of daily weather data from a global climate model (EC-Earth) under present-day conditions for the Northern Hemisphere. We then apply LASSO logistic regression to determine which weather conditions during the growing season lead to crop failure. We obtain good model performance in central Europe and the eastern half of the United States, while crop failure years in regions in Asia and the western half of the United States are less accurately predicted. Model performance correlates strongly with annual mean and variability of crop yields; that is, model performance is highest in regions with relatively large annual crop yield mean and variability. Overall, for nearly all grid points, the inclusion of temperature, precipitation and vapour pressure deficit is key to predict crop failure. In addition, meteorological predictors during all seasons are required for a good prediction. These results illustrate the omnipresence of compounding effects of both meteorological drivers and different periods of the growing season for creating crop failure events. Especially vapour pressure deficit and climate extreme indicators such as diurnal temperature range and the number of frost days are selected by the statistical model as relevant predictors for crop failure at most grid points, underlining their overarching relevance. We conclude that the LASSO regression model is a useful tool to automatically detect compound drivers of extreme impacts and could be applied to other weather impacts such as wildfires or floods. As the detected relationships are of Published by Copernicus Publications on behalf of the European Geosciences Union. 152 J. Vogel et al.: Identifying meteorological drivers of extreme impacts: an application to simulated crop yields purely correlative nature, more detailed analyses are required to establish the causal structure between drivers and impacts.
<p&amp... more <p>Compound weather events may lead to extreme impacts that can affect many aspects of society including agriculture. The identification of the underlying mechanisms that cause extreme impacts, such as crop failure, is of crucial importance to improve their understanding and forecasting. Here we investigate whether key meteorological drivers of extreme yield loss can be identified using Least Absolute Shrinkage and Selection Operator (Lasso) in a model environment. <br>We use yearly wheat yields as simulated by the APSIM crop model driven by 1600 years of daily weather data from a global climate model (EC-Earth v2.3) under present-day conditions for the Northern Hemisphere. We define extreme yield loss as years with yield below the 5th percentile. We apply logistic Lasso regression to predict whether weather conditions during the growing season lead to crop failure. Lasso selects the most relevant variables from a large set of predictors that best explain the target variable via regularization. Our input variables include monthly averaged values of maximum temperature, vapour pressure deficit and precipitation as well as established extreme event indicators such as maximum and minimum temperature during the growing season, diurnal temperature range, total number of frost days, and maximum five-day precipitation sum.<br>We obtain good model performance in Central Europe and the American Corn Belt, while yield losses in Asian and African regions are less accurately predicted. Model performance and mean wheat yield strongly correlate, i.e. model performance is highest in regions with relatively large mean yield. Based on the selected predictors, we identify regions where crop loss is predominantly influenced by a single variable and regions where it is driven by the interplay of several variables, i.e. compound events. Especially in the Midwest and Eastern regions of the USA, several variables are required to correctly predict yield losses. This illustrates the importance of accounting for the interplay of various weather conditions over the course of the growing season to be able to determine crop yield losses more precisely.<br>We conclude that the Lasso regression is a useful tool to detect the compound drivers of extreme impacts, which can be applied for other impact variables such as fires or floods. As the detected relationships are of purely correlative nature, more detailed analyses are required to establish the causal structure between drivers and impacts. Furthermore, using the same model environment, the robustness of the identified relationships will be tested in a climate change context.</p>
Environmental Research Letters, 2021
Compound events, like compound floods, have rapidly aroused interest due to the strong impacts as... more Compound events, like compound floods, have rapidly aroused interest due to the strong impacts associated with them. The spatial dependence has a fundamental role in the dynamics of these events, and causative investigations of their origins could contribute to elucidate their dynamics. Here, we addressed the pairwise spatial dependence between annual maximum (instantaneous) discharges occurring in river stations located in the United Kingdom. First, we tested the hypothesis that the dependence comes from the co-occurrence of annual maxima using Kendall’s tau measure of association and its conditional version, calculated from the non-co-occurrent values. This hypothesis, commonly accepted in literature, would attribute to the co-occurrence of the origin of the spatial dependence between extreme floods. The analysis showed how there is also dependence between annual maxima pertaining to catchments located very far from one another, and where the co-occurrence of annual maxima is smal...
Scientific Reports, Mar 4, 2021
<p>In a network of binarized precipitation (i.e., w... more <p>In a network of binarized precipitation (i.e., wet or dry value), the connection or dependence between each pair of nodes can occur following one or more of the following conditions: wet‐wet, dry‐dry, wet‐dry, or dry‐wet. Here, we firstly investigate the different types of dependence, year by year, within a precipitation network of binarized variables. We compare the sample estimate of the probability of co‐occurrence (or occurrence with a lag time within ±3 days) of each of the four possible combinations with respect to the correspondent confidence interval in hypothesis of independence. We develop a procedure to efficiently assess the dependence behavior of all couples of nodes within the network and apply the methodology to a network of rain gauges covering Europe and north Africa.</p>
This research examines the ability of soft computing approaches (i.e. Linear Regression (LR), Gau... more This research examines the ability of soft computing approaches (i.e. Linear Regression (LR), Gaussian Process regression (GP), Adaptive neuro-fuzzy inference system (ANFIS),Support Vector Machine (SVM) and deep neural network (DNN)) to predict the undrained shear strength (SU) of soil mixed waste crushed tires. Data set consisting of 72 different samples were used and obtained from the laboratory experiments. Out of 72 experimental observations randomly separated 50 observations were selected for model development whereas residual 22 were selected for the validation of the developed models. Input data set consist of vertical stress, percentage of the crushed tire, percentage of clay, size of clay, specific gravity of tires, Liquid limit, Plastic limit and Specific gravity of clay samples were considered as inputs whereas undrained shear strength of stabilized soil using waste crushed tires material (SU) was considered as output. Five most popular goodness fit assessment parameters ...
Compound weather events may lead to extreme impacts that can affect many aspects of society inclu... more Compound weather events may lead to extreme impacts that can affect many aspects of society including agriculture. Identifying the underlying mechanisms that cause extreme impacts, such as crop failure, is of crucial importance to improve their understanding and forecasting. In this study, we investigate whether key meteorological drivers of extreme impacts can be identified using the least absolute shrinkage and selection operator (LASSO) in a model environment, a method that allows for automated variable selection and is able to handle collinearity between variables. As an example of an extreme impact, we investigate crop failure using annual wheat yield as simulated by the Agricultural Production Systems sIMulator (APSIM) crop model driven by 1600 years of daily weather data from a global climate model (EC-Earth) under present-day conditions for the Northern Hemisphere. We then apply LASSO logistic regression to determine which weather conditions during the growing season lead to crop failure. We obtain good model performance in central Europe and the eastern half of the United States, while crop failure years in regions in Asia and the western half of the United States are less accurately predicted. Model performance correlates strongly with annual mean and variability of crop yields; that is, model performance is highest in regions with relatively large annual crop yield mean and variability. Overall, for nearly all grid points, the inclusion of temperature, precipitation and vapour pressure deficit is key to predict crop failure. In addition, meteorological predictors during all seasons are required for a good prediction. These results illustrate the omnipresence of compounding effects of both meteorological drivers and different periods of the growing season for creating crop failure events. Especially vapour pressure deficit and climate extreme indicators such as diurnal temperature range and the number of frost days are selected by the statistical model as relevant predictors for crop failure at most grid points, underlining their overarching relevance. We conclude that the LASSO regression model is a useful tool to automatically detect compound drivers of extreme impacts and could be applied to other weather impacts such as wildfires or floods. As the detected relationships are of Published by Copernicus Publications on behalf of the European Geosciences Union. 152 J. Vogel et al.: Identifying meteorological drivers of extreme impacts: an application to simulated crop yields purely correlative nature, more detailed analyses are required to establish the causal structure between drivers and impacts.
<p&amp... more <p>Compound weather events may lead to extreme impacts that can affect many aspects of society including agriculture. The identification of the underlying mechanisms that cause extreme impacts, such as crop failure, is of crucial importance to improve their understanding and forecasting. Here we investigate whether key meteorological drivers of extreme yield loss can be identified using Least Absolute Shrinkage and Selection Operator (Lasso) in a model environment. <br>We use yearly wheat yields as simulated by the APSIM crop model driven by 1600 years of daily weather data from a global climate model (EC-Earth v2.3) under present-day conditions for the Northern Hemisphere. We define extreme yield loss as years with yield below the 5th percentile. We apply logistic Lasso regression to predict whether weather conditions during the growing season lead to crop failure. Lasso selects the most relevant variables from a large set of predictors that best explain the target variable via regularization. Our input variables include monthly averaged values of maximum temperature, vapour pressure deficit and precipitation as well as established extreme event indicators such as maximum and minimum temperature during the growing season, diurnal temperature range, total number of frost days, and maximum five-day precipitation sum.<br>We obtain good model performance in Central Europe and the American Corn Belt, while yield losses in Asian and African regions are less accurately predicted. Model performance and mean wheat yield strongly correlate, i.e. model performance is highest in regions with relatively large mean yield. Based on the selected predictors, we identify regions where crop loss is predominantly influenced by a single variable and regions where it is driven by the interplay of several variables, i.e. compound events. Especially in the Midwest and Eastern regions of the USA, several variables are required to correctly predict yield losses. This illustrates the importance of accounting for the interplay of various weather conditions over the course of the growing season to be able to determine crop yield losses more precisely.<br>We conclude that the Lasso regression is a useful tool to detect the compound drivers of extreme impacts, which can be applied for other impact variables such as fires or floods. As the detected relationships are of purely correlative nature, more detailed analyses are required to establish the causal structure between drivers and impacts. Furthermore, using the same model environment, the robustness of the identified relationships will be tested in a climate change context.</p>
Environmental Research Letters, 2021
Compound events, like compound floods, have rapidly aroused interest due to the strong impacts as... more Compound events, like compound floods, have rapidly aroused interest due to the strong impacts associated with them. The spatial dependence has a fundamental role in the dynamics of these events, and causative investigations of their origins could contribute to elucidate their dynamics. Here, we addressed the pairwise spatial dependence between annual maximum (instantaneous) discharges occurring in river stations located in the United Kingdom. First, we tested the hypothesis that the dependence comes from the co-occurrence of annual maxima using Kendall’s tau measure of association and its conditional version, calculated from the non-co-occurrent values. This hypothesis, commonly accepted in literature, would attribute to the co-occurrence of the origin of the spatial dependence between extreme floods. The analysis showed how there is also dependence between annual maxima pertaining to catchments located very far from one another, and where the co-occurrence of annual maxima is smal...