Hybrid Modeling Framework for Simulating Compound Floods in a Coastal City

Author(s):  
Ahad Hasan Tanim ◽  
Erfan Goharian
2021 ◽  
Author(s):  
Anna Maria De Girolamo ◽  
Youssef Brouziyne ◽  
Lahcen Benaabidate ◽  
Aziz Aboubdillah ◽  
Ali El Bilali ◽  
...  

<p>The non-perennial streams and rivers are predominant in the Mediterranean region and play an important ecological role in the ecosystem diversity in this region. This class of streams is particularly vulnerable to climate change effects that are expected to amplify further under most climatic projections. Understanding the potential response of the hydrologic regime attributes to climatic stress helps in planning better conservation and management strategies. Bouregreg watershed (BW) in Morocco, is a strategic watershed for the region with a developed non-perennial stream network, and with typical assets and challenges of most Mediterranean watersheds. In this study, a hybrid modeling approach, based on the Soil and Water Assessment Tool (SWAT) model and Indicator of Hydrologic Alteration (IHA) program, was used to simulate the response of BW's stream network to climate change during the period: 2035-2050. Downscaled daily climate data from the global circulation model CNRM-CM5 were used to force the hybrid modeling framework over the study area. Results showed that, under the changing climate, the magnitude of the alteration will be different across the stream network; however, almost the entire flow regime attributes will be affected. Under the RCP8.5 scenario, the average number of zero-flow days will rise up from 3 to 17.5 days per year in some streams, the timing of the maximum flow was calculated to occur earlier by 17 days than in baseline, and the timing of the minimal flow should occur later by 170 days in some streams. The used modeling approach in this study contributed in identifying the most vulnerable streams in the BW to climate change for potential prioritization in conservation plans.</p>


2020 ◽  
Author(s):  
Linnea Österberg ◽  
Iván Domenzain ◽  
Julia Münch ◽  
Jens Nielsen ◽  
Stefan Hohmann ◽  
...  

AbstractThe interplay between nutrient-induced signaling and metabolism plays an important role in maintaining homeostasis and its malfunction has been implicated in many different human diseases such as obesity, type 2 diabetes, cancer and neurological disorders. Therefore, unravelling the role of nutrients as signaling molecules and metabolites as well as their interconnectivity may provide a deeper understanding of how these conditions occur. Both signalling and metabolism have been extensively studied using various systems biology approaches. However, they are mainly studied individually and in addition current models lack both the complexity of the dynamics and the effects of the crosstalk in the signaling system. To gain a better understanding of the interconnectivity between nutrient signaling and metabolism, we developed a hybrid model, combining Boolean model, describing the signalling layer and the enzyme constraint model accounting for metabolism using a regulatory network as a link. The model was capable of reproducing the regulatory effects that are associated with the Crabtree effect and glucose repression. We show that using this methodology one can investigat intrinsically different systems, such as signaling and metabolism, in the same model and gain insight into how the interplay between them can have non-trivial effects by showing a connection between Snf1 signaling and chronological lifespan by the regulation of NDE and NDI usage in respiring conditions. In addition, the model showed that during fermentation, enzyme utilization is the more important factor governing the protein allocation, while in low glucose conditions robustness and control is prioritized.Author summaryElucidating the complex relationship between nutrient-induced signaling and metabolism represents a key in understanding the onset of many different human diseases like obesity, type 3 diabetes, cancer and many neurological disorders. In this work we proposed a hybrid modeling approach, combining Boolean representation of singaling pathways, like Snf11, TORC1 and PKA with the enzyme constrained model of metabolism linking them via the regulatory network. This allowed us to improve individual model predictions and elucidate how single components in the dynamic signaling layer affect the steady-state metabolism. The model has been tested under respiration and fermentation, reveling novel connections and further reproducing the regulatory effects that are associated with the Crabtree effect and glucose repression. Finally, we show a connection between Snf1 signaling and chronological lifespan by the regulation of NDE and NDI usage in respiring conditions.


2021 ◽  
Author(s):  
Harini Narayanan ◽  
M. Nicolas Cruz Bournazou ◽  
Gonzalo Guillen-Gosalbez ◽  
Alessandro Butte

Mathematical models used for the representation of (bio)-chemical processes can be grouped into two broad paradigms: white-box or mechanistic models, completely based on knowledge or black-box data-driven models based on patterns observed in data. However, in the past two-decade, hybrid modeling that explores the synergy between the two paradigms has emerged as a pragmatic compromise. The data-driven part of these have been largely based on conventional machine learning algorithm (e.g., artificial neural network, support vector regression), which prevents interpretability of the finally learnt model by the domain-experts. In this work we present a novel hybrid modeling framework, the Functional-Hybrid model, that uses the ranked domain-specific functional beliefs together with symbolic regression to develop dynamic models. We demonstrate the successful implementation of these hybrid models for four benchmark systems and a microbial fermentation reactor, all of which are systems of (bio)chemical relevance. We also demonstrate that compared to a similar implementation with the conventional ANN, the performance of Functional-Hybrid model is at least two times better in interpolation and extrapolation. Additionally, the proposed framework can learn the dynamics in 50% lower number of experiments. This improved performance can be attributed to the structure imposed by the functional transformations introduced in the Functional-Hybrid model.


2021 ◽  
Author(s):  
Shubhankar Sengupta ◽  
Jürgen Scheffran ◽  
Dmitry Kovalevsky

<p>We develop a single-agent-based model in Netlogo of a coastal city facing climate change, using the VIABLE framework. The coastal city is threatened by damages from sea level rise and subsequent extreme sea level events. The agent, representing an urban planner, uses capital generated by the city to mitigate these damages by investing into one of two adaptation options available to it- developing coastal defenses or relocating the vulnerable coastal territories of the city inland. As the simulation progresses, gradually rising sea levels and randomly occurring extreme sea level events incur damages, and the agent alters its investments to optimize its value, resulting in dynamic reactive behavior. We track the response of this agent to the changing system through its investment patterns.</p>


Processes ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1939
Author(s):  
Maria João Regufe ◽  
Vinicius V. Santana ◽  
Alexandre F. P. Ferreira ◽  
Ana M. Ribeiro ◽  
José M. Loureiro ◽  
...  

This study proposed a hybrid modeling framework for membrane separation processes where lithium from batteries is recovered. This is a pertinent problem nowadays as lithium batteries are popularized in hybrid and electric vehicles. The hybrid model is based on an artificial intelligence (AI) structure to model the mass transfer resistance of several experimental separations found in the literature. It is also based on a phenomenological model to represent the transient system regime. An optimization framework was designed to perform the AI model training and simultaneously solve the Ordinary Differential Equation (ODE) system representing the phenomenological model. The results demonstrate that the hybrid model can better represent the experimental validation sets than the phenomenological model alone. This strategy opens doors for further investigations of this system.


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