scholarly journals When the river talks to its people: Local knowledge-based flood forecasting in Gandak River basin, India

2019 ◽  
Vol 31 ◽  
pp. 55-67 ◽  
Author(s):  
Amitangshu Acharya ◽  
Anjal Prakash
Keyword(s):  
River Basin ◽  
Local Knowledge ◽  
Flood Forecasting ◽  
Knowledge Based ◽  
Gandak River

scholarly journals Projections of Local Knowledge-Based Adaptation Strategies of Mexican Coffee Farmers

Climate ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 60
Author(s):  
Patricia Ruiz-García ◽  
Cecilia Conde-Álvarez ◽  
Jesús David Gómez-Díaz ◽  
Alejandro Ismael Monterroso-Rivas
Keyword(s):  
Climate Change ◽  
Extreme Events ◽  
Local Knowledge ◽  
Production Systems ◽  
Agroforestry Systems ◽  
Southern Mexico ◽  
Knowledge Based ◽  
Coffee Farmers ◽  
Extreme Hydrological Events ◽  
Adaptation Policies

Local knowledge can be a strategy for coping with extreme events and adapting to climate change. In Mexico, extreme events and climate change projections suggest the urgency of promoting local adaptation policies and strategies. This paper provides an assessment of adaptation actions based on the local knowledge of coffee farmers in southern Mexico. The strategies include collective and individual adaptation actions that farmers have established. To determine their viability and impacts, carbon stocks and fluxes in the system’s aboveground biomass were projected, along with water balance variables. Stored carbon contents are projected to increase by more than 90%, while maintaining agroforestry systems will also help serve to protect against extreme hydrological events. Finally, the integration of local knowledge into national climate change adaptation plans is discussed and suggested with a local focus. We conclude that local knowledge can be successful in conserving agroecological coffee production systems.

scholarly journals Prior knowledge-based approach for associating contaminants with biological effects: A case study in the St. Croix River basin, MN, WI, USA

2017 ◽  
Vol 221 ◽  
pp. 427-436 ◽  
Author(s):  
Anthony L. Schroeder ◽  
Dalma Martinović-Weigelt ◽  
Gerald T. Ankley ◽  
Kathy E. Lee ◽  
Natalia Garcia-Reyero ◽  
...  
Keyword(s):  
Prior Knowledge ◽  
River Basin ◽  
Biological Effects ◽  
Knowledge Based

Flood forecasting and alert system for Arda River basin

2016 ◽  
Vol 541 ◽  
pp. 457-470 ◽  
Author(s):  
Eram Artinyan ◽  
Beatrice Vincendon ◽  
Kamelia Kroumova ◽  
Nikolai Nedkov ◽  
Petko Tsarev ◽  
...  
Keyword(s):  
River Basin ◽  
Flood Forecasting ◽  
Alert System

scholarly journals Application of Long Short-Term Memory (LSTM) Neural Network for Flood Forecasting

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1387 ◽  
Author(s):  
Le ◽  
Ho ◽  
Lee ◽  
Jung
Keyword(s):  
Neural Network ◽  
River Basin ◽  
Input Data ◽  
Short Term Memory ◽  
Flood Forecasting ◽  
Data Sets ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Hoa Binh

Flood forecasting is an essential requirement in integrated water resource management. This paper suggests a Long Short-Term Memory (LSTM) neural network model for flood forecasting, where the daily discharge and rainfall were used as input data. Moreover, characteristics of the data sets which may influence the model performance were also of interest. As a result, the Da River basin in Vietnam was chosen and two different combinations of input data sets from before 1985 (when the Hoa Binh dam was built) were used for one-day, two-day, and three-day flowrate forecasting ahead at Hoa Binh Station. The predictive ability of the model is quite impressive: The Nash–Sutcliffe efficiency (NSE) reached 99%, 95%, and 87% corresponding to three forecasting cases, respectively. The findings of this study suggest a viable option for flood forecasting on the Da River in Vietnam, where the river basin stretches between many countries and downstream flows (Vietnam) may fluctuate suddenly due to flood discharge from upstream hydroelectric reservoirs.

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