Survey on River Water Level Measuring Technologies: Case Study for Flood Management Purposes of the C2-SENSE Project

2016 ◽  
pp. 610-623 ◽  
Author(s):  
Anna Bączyk ◽  
Jan Piwiński ◽  
Rafał Kłoda ◽  
Mateusz Grygoruk
Keyword(s):  
Water Level ◽  
River Water ◽  
Flood Management

River Water Level Prediction Using Passive Microwave Signatures—A Case Study: The Bermejo Basin

2014 ◽  
Vol 7 (9) ◽  
pp. 3903-3914 ◽  
Author(s):  
Cristina Vittucci ◽  
Leila Guerriero ◽  
Paolo Ferrazzoli ◽  
Rachid Rahmoune ◽  
Veronica Barraza ◽  
...  
Keyword(s):  
Water Level ◽  
River Water ◽  
Passive Microwave ◽  
Bermejo Basin ◽  
Microwave Signatures ◽  
Water Level Prediction

REAL-TIME ANORMALY DETECTION OF RIVER WATER LEVEL OBSERVATION BASED ON PROBABILITY DISTRIBUTION OF WATER LEVEL ESTIMATION ERROR

2019 ◽  
Vol 75 (2) ◽  
pp. I_193-I_198
Author(s):  
Masayuki HITOKOTO ◽  
Noriko KAWAGOE ◽  
Hajime HASHIDA ◽  
Yuichi SEI ◽  
Kazutomo FUSAMAE
Keyword(s):  
Probability Distribution ◽  
Water Level ◽  
Real Time ◽  
River Water ◽  
Estimation Error

River Water Level Prediction in Coastal Catchment using hybridized relevance vector machine model with improved grasshopper optimization

2021 ◽  
pp. 126477
Author(s):  
Hai Tao ◽  
Najah Kadhim Al-Bedyry ◽  
Khaled Mohamed Khedher ◽  
Shamsuddin Shahid ◽  
Zaher Mundher Yaseen
Keyword(s):  
Water Level ◽  
River Water ◽  
Relevance Vector Machine ◽  
Machine Model ◽  
Grasshopper Optimization ◽  
Water Level Prediction

Flood Water Level Modeling and Prediction Using Radial Basis Function Neural Network: Case Study Kedah

2017 ◽  
pp. 225-234
Author(s):  
Mohd Anuar Abu Bakar ◽  
Fathrul Azarshah Abdul Aziz ◽  
Shamsul Faisal Mohd Hussein ◽  
Shahrum Shah Abdullah ◽  
Fauzan Ahmad
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Water Level ◽  
Basis Function ◽  
Flood Water ◽  
Modeling And Prediction ◽  
Radial Basis

Estimating the ratio of pond size to irrigated soybean land in Mississippi: a case study

2016 ◽  
Vol 16 (6) ◽  
pp. 1639-1647 ◽  
Author(s):  
Ying Ouyang ◽  
Gary Feng ◽  
John J. Read ◽  
Theodor D. Leininger ◽  
Johnie N. Jenkins
Keyword(s):  
Water Level ◽  
Groundwater Resources ◽  
Pond Water ◽  
Crop Land ◽  
Experimental Learning ◽  
Storage Pond ◽  
Pond Size ◽  
Stella Model ◽  
On Farm

Although more on-farm storage ponds have been constructed in recent years to mitigate groundwater resources depletion in Mississippi, little effort has been devoted to estimating the ratio of on-farm water storage pond size to irrigated crop land based on pond metrics and its hydrogeological conditions. In this study, two simulation scenarios were chosen to determine such a ratio as well as to investigate pond hydrological processes using a Structural Thinking, Experimental Learning Laboratory with Animation (STELLA) model, one scenario with and the other without using pond water for irrigation for a typical pond that represented the average conditions in East Mississippi. Simulation results showed that pond water level changed moderately for conditions without using its water for irrigation, whereas pond water level changed dramatically for conditions with using its water for irrigation. A reasonable ratio of pond size to irrigated soybeans land was 1:18 if the irrigation rate was 2.54 cm/d (or 1 inch/d) and the low limit of the pond water level was drawn to near zero (0.08 m). For the ratio of 1:18, our simulations further revealed that a 1-ha soybeans land could save about 542 m3 groundwater each year. This study suggests that the STELLA model is a useful tool for estimating the ratio of pond size to irrigated crop land.

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