Search Results (5)

Accurate prediction of future streamflow in flood-prone regions is crucial for effective flood management and disaster mitigation. This study presents an innovative approach for streamflow projections in deep learning (DL) environment by integrating the quantitative Land-Use Land-Cover (LULC) overlaid with flow accumulation values and the various Global Climate Model (GCM) simulated data. Firstly, the Long Short Term Memory (LSTM) model was developed for the streamflow prediction of Greater Pamba River Basin (GPRB) in Kerala, India for 1985 to 2015 period, considering the climatic inputs. Then, the flow accumulation-weighted LULC integration was considered in modelling, which substantially improves the accuracy of streamflow predictions including the extremes of all the three stations, as the model accounts for the geographical variety of land cover types towards the streamflow at the sub-basin outlets. Subsequently, Reliability Ensemble Averaging (REA) technique was used to create an ensemble of three candidate GCM products to illustrate the spectrum of uncertainty associated with climate projections. Future LULC changes are accounted in regional scale based on the sub-basin approach by means of Cellular-Automata Markov Model and used for integrating with the climatic indices. The basin-scale streamflow projection is done under three climate scenarios of SSP126, SSP245 and SSP585 respectively for lowest, moderate and highest emission conditions. This work is a novel approach of integrating quantified LULC with flow accumulation and other climatic inputs in a DL environment against the conventional techniques of hydrological modelling. The DL model can adapt and account for shifting hydrological responses induced by changes in climatic and LULC inputs. The integration of flow accumulation with changes in LULC was successful in capturing the flow dynamics in long-term. It also identifies regions that are more likely to experience increased flooding in the near future under changing climate scenarios and supports decision-making for sustainable water management of the Greater Pamba Basin which was the worst affected region in Kerala during the mega floods of 2018. Full article

During the last half century, the concept of shared ledger systems that offer a single source of truth has challenged traditional bookkeeping, leading to innovations such as the resource-event-agent (REA) accounting framework, triple-entry accounting (TEA), and blockchain. Despite these advancements, the historical development of shared ledger systems remains under-researched and unclear, with the influence of REA on TEA particularly overlooked. This study aims to fill this gap by conducting a genealogical analysis of shared ledger systems, with a focus on tracing the development of TEA and its historical byproduct of the REA framework designed by McCarthy. Through a comprehensive literature review and interviews with pioneers in REA, TEA, and blockchain, we uncover the missing link between REA and TEA. Our findings suggest that the current explosion of shared ledger systems results from the convergence of three parallel research streams, occasionally interacting with each other. We correct common misconceptions, acknowledge the influence of key individuals, and map out the overlapping paths of REA, TEA, and blockchain. By elucidating the historical evolution of shared ledger systems, this study contributes to the academic debate and fosters further discourse among researchers in REA, TEA, and blockchain, thereby enhancing the potential applications within these fields. Full article

The risk associated with extreme hydrological processes (flash floods, floods) is more present than ever, taking into account the global climatic changes, the expansion of inhabited areas and the changes emerging as a result of inadequate land management. Of all the hydrological risks, slope flash floods represent the processes that have the highest impact because of the high speed of their development and their place of origin, which makes them difficult to predict. This study is performed in an area susceptible to the emergence of slope flash floods, the Valea Rea catchment area, spatially located in Northwest Romania, and exposed to western circulation, which favours the development of such processes. The entire research is based on a methodology involving the integration of spatial databases, which indicate the vulnerability of the territory in the form of a weighted average equation to highlight the major impact of the most relevant factor. A number of 15 factors have been used in raster spatial databases, obtained by conversion (land use, soil type, lithology, Hydrologic Soil Group, etc.), derived from the digital elevation model (slope, aspect, TWI, etc.) or by performing spatial analysis submodels (precipitation, slope length, etc). The integration of these databases by means of the spatial analysis equation based on the weighted average led to the vulnerability of the territory to FFPI, classified on five classes from very low to very high. The final result underlines the high and very high vulnerability (43%) of the analysed territory that may have a major impact on the human communities and the territorial infrastructure. The results obtained highlight the torrential nature of the analysed catchment area, identifying several hotspots of great risk, located mainly within the built-up areas of intensely inhabited regions; a fact which involves a major risk and significant potential material damage in the territory. The model was validated by directly comparing the results obtained with locations previously affected, where the flood effects have been identified, highlighting the fact that the model may be taken into account to be applied in practice, and also to be implemented in territories that share the same features. Full article

In Love: Accusative and Dative, Paul Mendes-Flohr explores ancient and modern Jewish engagements with the commandment to love the Re’a (neighbor) in Leviticus 19:18. Drawing on Rosenzweig’s phenomenology of divine–human love, Mendes-Flohr seeks to delineate the possibility of a humanist ethics of compassion that is not dependent, as in Rosenzweig, on hearing the divine voice. Taking Mendes-Flohr as point of departure, this paper explores the concept of fraternity (fraternité) as it figures in the thought of Yehuda Léon Askenazi (1922–1996), a North African kabbalist thinker and an important spiritual leader of Francophone Jewry in the twentieth century. Looking at two interrelated moments in Askenazi’s long career as a biblical exegete, I quarry Askenazi’s notion of fraternity for an account of alterity. Based on his discussions of the Cain and Abel story, as well as other biblical episodes, I argue that, for Askenazi, the challenge of fraternity, as figuring repeatedly in the Genesis narrative, is the preferred model to think of second-person relationships. Furthermore, I suggest, in contrast to Rosenzweig’s top-down account of revelation and human love, Askenazi’s approach represents a bottom-up model of love of one’s neighbor, which, when achieved, brings about divine revelation. Full article

Electricity from renewable energy (RE) sources gained in significance due to green-friendly governmental initiatives in the form of either direct subsidizes, tax incentives or tradable certificates. Thereby, RE generators are incentivized to maximize energy feed-in or the remuneration from governmental subsidizes, meanwhile neglecting any market interaction. Consequently, wind farms are clustered in windy regions. Along with the governmentally initiated integration of RE generation into power markets, the siting of RE generators will change. In wind power dominated power systems that fully integrate RE generators into power markets, wind farms will compete against each other and try to maximize their market value. Hence, wind speed correlations with other wind farms will become increasingly important when choosing a site in a uniform or zonal pricing system. To quantify the impact of market integration on future wind farm siting, an approach is developed that takes into account the local wind potential of a certain site, wind speed correlations to other sites and their installed capacities. An optimization that minimizes the normalized sum of wind power correlations to all other sites and their respective normalized installed wind power capacity is performed. To achieve a predefined minimum energy output, the average wind yield is considered as an additional constraint. The outcome is an optimal wind farm site in a wind energy dominated system. Running this for a given wind power expansion scenario enables decision makers to foresee the spatial development of wind farm installations. To demonstrate the model’s applicability, a case study is performed for Germany. Thereby, wind speed data for four years from the European reanalysis model COSMO-REA6 is used. The results indicate that a full market integration of RE generators will space out more evenly new wind farms. Thereby, wind farms can economically benefit from the non-simultaneity of wind speed. Full article