Month: September 2024

The role of personality traits and online behavior in belief in fake news

Latest Results

Abstract

The current study examines how careless online behavior and personality traits are related to the detection of fake news. We tested the relationships among accurately distinguishing between fake and real news headlines, careless online behavioral tendencies, and the HEXACO and dark triad personality traits. Poorer discernment between fake and real news headlines was associated with greater careless behavior online (i.e., greater online disinhibition, greater risky online behavior, greater engagement with strangers online, and less suspicion of others’ intentions online), as well as lower Conscientiousness, Openness, and Honesty-Humility, and greater dark triad traits. Implications for the literature as well as potential interventions to reduce susceptibility to misinformation and fake news are discussed.

The role of personality traits and online behavior in belief in fake news

Latest Results

Abstract

The current study examines how careless online behavior and personality traits are related to the detection of fake news. We tested the relationships among accurately distinguishing between fake and real news headlines, careless online behavioral tendencies, and the HEXACO and dark triad personality traits. Poorer discernment between fake and real news headlines was associated with greater careless behavior online (i.e., greater online disinhibition, greater risky online behavior, greater engagement with strangers online, and less suspicion of others’ intentions online), as well as lower Conscientiousness, Openness, and Honesty-Humility, and greater dark triad traits. Implications for the literature as well as potential interventions to reduce susceptibility to misinformation and fake news are discussed.

Assessing future changes in flood susceptibility under projections from the sixth coupled model intercomparison project: case study of Algiers City (Algeria)

Latest Results

Abstract

This research investigates the changes in flash flood susceptibility in Algiers, Northern Algeria between current and future climatic conditions based on two Shared Socio-economic Pathways (SSP2-4.5 and SSP3-7.0) from the CMIP6 dataset. Three machine-learning models, namely the Generalized Linear Model (GLM), Random Forest (RF) and Gradient Boosting Machine (GBM), were employed to assess flash flood susceptibility by capturing the relationships between a set of predictive variables and historical flash flood events in the study area. The validity of the used models was assessed using the receiver operating characteristic (ROC) model and its area under the curve (AUC). This yielded excellent performance for all models with a slight superiority to GBM (AUC = 96.4%) compared to RF (AUC = 96.1%) and GLM (AUC = 93.9%). With respct to the year 2018, SSP 2–4.5 revealed a future evolution of high to very high flash flood susceptibility of + 2.9% by the year 2040, + 1.6% by 2060 and + 5.1% by 2080. Under SSP3-7.0, the spatial coverage of high and very high susceptibility classes showed more significant increase of 3.6% by 2040, + 4.9% by 2060, and + 4.7% by 2080. Overall, this research provided insights into the changes in flash flood susceptibility between current and two future climate change scenarios. This can help decision makers and urban planners in Algiers in developing adequate strategies to improve resilience against future flash floods.

Robust future projections of global spatial distribution of major tropical cyclones and sea level pressure gradients

Latest Results

Abstract

Despite the profound societal impacts of intense tropical cyclones (TCs), prediction of future changes in their regional occurrence remains challenging owing to climate model limitations and to the infrequent occurrence of such TCs. Here we reveal projected changes in the frequency of major TC occurrence (i.e., maximum sustained wind speed: ≥ 50 m s−1) on the regional scale. Two independent high-resolution climate models projected similar changes in major TC occurrence. Their spatial patterns highlight an increase in the Central Pacific and a reduction in occurrence in the Southern Hemisphere—likely attributable to anthropogenic climate change. Furthermore, this study suggests that major TCs can modify large-scale sea-level pressure fields, potentially leading to the abrupt onset of strong wind speeds even when the storm centers are thousands of kilometers away. This study highlights the amplified risk of storm-related hazards, specifically in the Central Pacific, even when major TCs are far from the populated regions.

Toward robust pattern similarity metric for distributed model evaluation

Latest Results

Abstract

SPAtial EFficiency (SPAEF) metric is one of the most thoroughly used metrics in hydrologic community. In this study, our aim is to improve SPAEF by replacing the histogram match component with other statistical indices, i.e. kurtosis and earth mover’s distance, or by adding a fourth or fifth component such as kurtosis and skewness. The existing spatial metrics i.e. SPAtial efficiency (SPAEF), structural similarity (SSIM) and spatial pattern efficiency metric (SPEM) were compared with newly proposed metrics to assess their converging performance. The mesoscale hydrologic model (mHM) of the Moselle River is used to simulate streamflow (Q) and actual evapotranspiration (AET). The two-source energy balance AET during the growing season is used as monthly reference maps to calculate the spatial performance of the model. The moderate resolution imaging spectroradiometer based leaf area index is utilized by the mHM via pedo-transfer functions and multi-scale parameter regionalization approach to scale the potential ET. In addition to the real monthly AET maps, we also tested these metrics using a synthetic true AET map simulated with a known parameter set for a randomly selected day. The results demonstrate that the newly developed four-component metric i.e. SPAtial Hybrid 4 (SPAH4) slightly outperforms conventional three-component metric i.e. SPAEF (3% better). However, SPAH4 significantly outperforms the other existing metrics i.e. 40% better than SSIM and 50% better than SPEM. We believe that other fields such as remote sensing, change detection, function space optimization and image processing can also benefit from SPAH4.

Association of precipitation extremes and crops production and projecting future extremes using machine learning approaches with CMIP6 data

Latest Results

Abstract

Precipitation extremes have surged in frequency and duration in recent decades, significantly impacting various sectors, including agriculture, water resources, energy, and public health worldwide. Pakistan, being highly susceptible to climate change and extremes, has experienced adverse events in recent times, emphasizing the need for a comprehensive investigation into the relationship between precipitation extremes and crops production. This study focuses on assessing the association between precipitation extremes on crops production, with a particular emphasis on the Punjab province, a crucial region for the country’s food production. The initial phase of the study involved exploring the associations between precipitation extremes and crops production for the duration of 1980–2014. Notably, certain precipitation extremes, such as maximum CDDs (consecutive dry days), R99p (extreme precipitation events), PRCPTOT (precipitation total) and SDII (simple daily intensity index) exhibited strong correlations with the production of key crops like wheat, rice, garlic, dates, moong, and masoor. In the subsequent step, four machine learning (ML) algorithms were trained and tested using observed daily climate data (including maximum and minimum temperatures and precipitation) alongside model reference data (1985–2014) as predictors. Gradient boosting machine (GBM) was selected for its superior performance and employed to project precipitation extremes for three distinct future periods (F1: 2025–2049, F2: 2050–2074, F3: 2075–2099) under the SSP2-4.5 and SSP5-8.5 derived from the CMIP6 (Coupled Model Intercomparison Project Phase 6) archive. The projection results indicated an increasing and decreasing trend in CWDs (maximum consecutive wet days) and CDDs, respectively, at various meteorological stations. Furthermore, R10mm (the number of days with precipitation equal to or exceeding 10 mm) and R25mm displayed an overall increasing trend at most of the stations, though some exhibited a decreasing trend. These trends in precipitation extremes have potential consequences, including the risk of flash floods and damage to agriculture and infrastructure. However, the study emphasizes that with proper planning, adaptation measures, and mitigation strategies, the potential losses and damages can be significantly minimized in the future.

Association of precipitation extremes and crops production and projecting future extremes using machine learning approaches with CMIP6 data

Latest Results

Abstract

Precipitation extremes have surged in frequency and duration in recent decades, significantly impacting various sectors, including agriculture, water resources, energy, and public health worldwide. Pakistan, being highly susceptible to climate change and extremes, has experienced adverse events in recent times, emphasizing the need for a comprehensive investigation into the relationship between precipitation extremes and crops production. This study focuses on assessing the association between precipitation extremes on crops production, with a particular emphasis on the Punjab province, a crucial region for the country’s food production. The initial phase of the study involved exploring the associations between precipitation extremes and crops production for the duration of 1980–2014. Notably, certain precipitation extremes, such as maximum CDDs (consecutive dry days), R99p (extreme precipitation events), PRCPTOT (precipitation total) and SDII (simple daily intensity index) exhibited strong correlations with the production of key crops like wheat, rice, garlic, dates, moong, and masoor. In the subsequent step, four machine learning (ML) algorithms were trained and tested using observed daily climate data (including maximum and minimum temperatures and precipitation) alongside model reference data (1985–2014) as predictors. Gradient boosting machine (GBM) was selected for its superior performance and employed to project precipitation extremes for three distinct future periods (F1: 2025–2049, F2: 2050–2074, F3: 2075–2099) under the SSP2-4.5 and SSP5-8.5 derived from the CMIP6 (Coupled Model Intercomparison Project Phase 6) archive. The projection results indicated an increasing and decreasing trend in CWDs (maximum consecutive wet days) and CDDs, respectively, at various meteorological stations. Furthermore, R10mm (the number of days with precipitation equal to or exceeding 10 mm) and R25mm displayed an overall increasing trend at most of the stations, though some exhibited a decreasing trend. These trends in precipitation extremes have potential consequences, including the risk of flash floods and damage to agriculture and infrastructure. However, the study emphasizes that with proper planning, adaptation measures, and mitigation strategies, the potential losses and damages can be significantly minimized in the future.