scholarly journals The effect of water level in vertical aquaculture systems on production performance, biochemistry, hematology, and histology of Anguilla bicolor bicolor

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Eko Harianto ◽  
Eddy Supriyono ◽  
Tatag Budiardi ◽  
Ridwan Affandi ◽  
Yani Hadiroseyani
Keyword(s):  
Water Level ◽  
Normal Values ◽  
Stocking Density ◽  
Water Levels ◽  
Production Performance ◽  
Fish Weight ◽  
Important Study ◽  
Level Information ◽  
Gill Filaments ◽  
Skin Epidermis

AbstractThe water level in the cultivation of eel (Anguilla bicolor bicolor) is an important study in order to provide the optimal water level for cultivation. Optimizing the water level will affect the substitution of respiration energy with energy to grow. In addition, the water level information is related to the efficiency of water use for eel production in the future. Information on water level for eel production is still very limited, so this research is necessary to do. A total of 120 eel elver (initial weight 13.66 ± 0.09 g) were collected from eel companies in Bogor City, Indonesia. Fish were reared in vertical aquaculture systems with a stocking density of 10 fish per container for 60 days. The artificial feed containing 55% protein given as much as 3–5% of the biomass. Absorption and water replacement were done 20% per day. The result of this research showed that fish weight increased with an average of 33.45 ± 0.33 g. Different water levels had an impact to KKb, SGOT, ALP, and He. There was erosion of the skin epidermis and necrosis of the gill filaments due to the adaptation process. Water quality was within the optimum range for all treatments and 1.5 cm water level is recommended for maintenance (SGOT, ALP and He values were closest to normal values).

scholarly journals Estimating Real-Time Water Area of Dongting Lake Using Water Level Information

Water ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 1240
Author(s):  
Yuannan Long ◽  
Rong Tang ◽  
Changshan Wu ◽  
Changbo Jiang ◽  
Shixiong Hu
Keyword(s):  
Linear Regression ◽  
Water Level ◽  
Real Time ◽  
Yangtze River Basin ◽  
Water Area ◽  
Dongting Lake ◽  
Water Levels ◽  
Lake Area ◽  
Level Information ◽  
Maximum Water

Dongting Lake, the second largest freshwater lake in China, is an important water source for the Yangtze River Basin. The water area of Dongting Lake fluctuates significantly daily, which may cause flooding and other relevant disasters. Although remote sensing techniques may provide lake area estimates with reasonable accuracy, they are not available in real-time and may be susceptible to weather conditions. To address this issue, this paper attempted to examine the relationship between lake area and the water levels at the hydrological stations. Multi-temporal water area data were derived through analyzing Moderate Resolution Imaging Spectroradiometer (MODIS) imagery using the Automatic Water Extraction Index (AWEI). Then we analyzed the inter- and intra-annual variations in the water area of the Dongting Lake. Corresponding water level information at hydrological stations of the Dongting Lake were obtained. Simple linear regression (SLR) models and stepwise multiple linear regression (SMLR) models were constructed using water levels and water level differences from the upstream and downstream hydrological stations. We used the data from 2004 to 2012 and 2012, respectively, to build the model, and applied the data from 2013 to 2015 to evaluate the models. Results suggest that the maximum water area of the Dongting Lake during 2000–2015 has a clear decreasing trend. The variations in the water area were characterized by hydrological seasons, with the annual minimum and maximum water areas occurring in January and September, respectively. The water level at the Chengjingji station, and water level differences between upstream stations and the Chengjingji station, play a major role in estimating the water area. Further, results also show that the SMLR established in 2012 performs the best in estimating water area of the Dongting Lake, especially with high water levels.

Shiplock Multi-Channel Water Level Monitoring and Message Interaction

2013 ◽  
Vol 321-324 ◽  
pp. 586-591 ◽  
Author(s):  
Wei Tang ◽  
Peng Zhang ◽  
Yong Liu ◽  
Li Hui Wang ◽  
Lin Fei Liu
Keyword(s):  
Water Level ◽  
Real Time ◽  
High Performance ◽  
Water Levels ◽  
Channel Water ◽  
Level Information ◽  
Lock Chamber ◽  
Pressure Water ◽  
Level Monitoring ◽  
Water Level Monitoring

The real-time monitoring of water level is a key element for the security and automation of shiplock. To meet the requirement of real time measurement and information releasing of multipoint water level for a shiplock station, a multi-channel water level monitoring and SMS interaction system based on high-performance MCU W77E58 is designed. Pressure water level sensors are used to acquire multipoint real-time water levels at upstream, downstream, lock chamber points. The water level information is processed by MCU and transmitted to the remote IPC through the RS485 bus. IPC interface is designed basing on VB6.0. It completes the bi-directional interaction of water level information to cell phone users with GSM module by sending short messages. Site operation shows that overall scheme of the system is reasonable, hardware and software works well and running costs is relatively low. The system fully meets the requirements of shiplock and is worth popularizing and application.

AUTOMATIC WATER LEVEL MONITORING WITH IOT AND LPWAN

2019 ◽  
pp. 95-103
Author(s):  
Krum Videnov ◽  
Vanya Stoykova
Keyword(s):  
Water Level ◽  
Real Time ◽  
Early Warning ◽  
Water Sources ◽  
Water Levels ◽  
Water Level Fluctuations ◽  
Level Monitoring ◽  
Water Level Monitoring

Monitoring water levels of lakes, streams, rivers and other water basins is of essential importance and is a popular measurement for a number of different industries and organisations. Remote water level monitoring helps to provide an early warning feature by sending advance alerts when the water level is increased (reaches a certain threshold). The purpose of this report is to present an affordable solution for measuring water levels in water sources using IoT and LPWAN. The assembled system enables recording of water level fluctuations in real time and storing the collected data on a remote database through LoRaWAN for further processing and analysis.

Interaction between water pressure in the basal drainage system and discharge from an Alpine glacier before and during a rainfall-induced subglacial hydrological event

1997 ◽  
Vol 24 ◽  
pp. 288-292 ◽  
Author(s):  
Andrew P. Barrett ◽  
David N. Collins
Keyword(s):  
Water Level ◽  
Water Pressure ◽  
Drainage System ◽  
Water Levels ◽  
Drainage Network ◽  
Alpine Glacier ◽  
Hydraulic Conditions ◽  
Borehole Water ◽  
Progressive Growth ◽  
Resultant Increase

Combined measurements of meltwater discharge from the portal and of water level in a borehole drilled to the bed of Findelengletscher, Switzerland, were obtained during the later part of the 1993 ablation season. A severe storm, lasting from 22 through 24 September, produced at least 130 mm of precipitation over the glacier, largely as rain. The combined hydrological records indicate periods during which the basal drainage system became constricted and water storage in the glacier increased, as well as phases of channel growth. During the storm, water pressure generally increased as water backed up in the drainage network. Abrupt, temporary falls in borehole water level were accompanied by pulses in portal discharge. On 24 September, whilst borehole water level continued to rise, water started to escape under pressure with a resultant increase in discharge. As the drainage network expanded, a large amount of debris was flushed from a wide area of the bed. Progressive growth in channel capacity as discharge increased enabled stored water to drain and borehole water level to fall rapidly. Possible relationships between observed borehole water levels and water pressures in subglacial channels are influenced by hydraulic conditions at the base of the hole, distance between the hole and a channel, and the nature of the substrate.

scholarly journals Spatial distribution of water level impact to back-barrier bays

2018 ◽  
Author(s):  
Alfredo L. Aretxabaleta ◽  
Neil K. Ganju ◽  
Zafer Defne ◽  
Richard P. Signell
Keyword(s):  
Water Level ◽  
Sea Level ◽  
Water Levels ◽  
Analytical Models ◽  
Barnegat Bay ◽  
Sea Level Fluctuations ◽  
Flooding Hazard ◽  
Short Period ◽  
Inlet Geometry ◽  
Spatial Estimates

Abstract. Water level in semi-enclosed bays, landward of barrier islands, is mainly driven by offshore sea level fluctuations that are modulated by bay geometry and bathymetry, causing spatial variability in the ensuing response (transfer). Local wind setup can have a secondary role that depends on wind speed, fetch, and relative orientation of the wind direction and the bay. Inlet geometry and bathymetry primarily regulate the magnitude of the transfer between open ocean and bay. Tides and short-period offshore oscillations are more damped in the bays than longer-lasting offshore fluctuations, such as storm surge and sea level rise. We compare observed and modeled water levels at stations in a mid-Atlantic bay (Barnegat Bay) with offshore water level proxies. Observed water levels in Barnegat Bay are compared and combined with model results from the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) modeling system to evaluate the spatial structure of the water level transfer. Analytical models based on the dimensional characteristics of the bay are used to combine the observed data and the numerical model results in a physically consistent approach. Model water level transfers match observed values at locations inside the Bay in the storm frequency band (transfers ranging from 70–100 %) and tidal frequencies (10–55 %). The contribution of frequency-dependent local setup caused by wind acting along the bay is also considered. The approach provides transfer estimates for locations inside the Bay where observations were not available resulting in a complete spatial characterization. The approach allows for the study of the Bay response to alternative forcing scenarios (landscape changes, future storms, and rising sea level). Detailed spatial estimates of water level transfer can inform decisions on inlet management and contribute to the assessment of current and future flooding hazard in back-barrier bays and along mainland shorelines.

scholarly journals The effects of ENSO, climate change and human activities on the water level of Lake Toba, Indonesia: a critical literature review

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Hendri Irwandi ◽  
Mohammad Syamsu Rosid ◽  
Terry Mart
Keyword(s):  
Climate Change ◽  
Water Level ◽  
Human Activities ◽  
Southern Oscillation ◽  
Climatic Conditions ◽  
Water Levels ◽  
Dominant Factor ◽  
Climate Projections ◽  
Continuous Increase ◽  
The Future

AbstractThis research quantitatively and qualitatively analyzes the factors responsible for the water level variations in Lake Toba, North Sumatra Province, Indonesia. According to several studies carried out from 1993 to 2020, changes in the water level were associated with climate variability, climate change, and human activities. Furthermore, these studies stated that reduced rainfall during the rainy season due to the El Niño Southern Oscillation (ENSO) and the continuous increase in the maximum and average temperatures were some of the effects of climate change in the Lake Toba catchment area. Additionally, human interventions such as industrial activities, population growth, and damage to the surrounding environment of the Lake Toba watershed had significant impacts in terms of decreasing the water level. However, these studies were unable to determine the factor that had the most significant effect, although studies on other lakes worldwide have shown these factors are the main causes of fluctuations or decreases in water levels. A simulation study of Lake Toba's water balance showed the possibility of having a water surplus until the mid-twenty-first century. The input discharge was predicted to be greater than the output; therefore, Lake Toba could be optimized without affecting the future water level. However, the climate projections depicted a different situation, with scenarios predicting the possibility of extreme climate anomalies, demonstrating drier climatic conditions in the future. This review concludes that it is necessary to conduct an in-depth, comprehensive, and systematic study to identify the most dominant factor among the three that is causing the decrease in the Lake Toba water level and to describe the future projected water level.

scholarly journals Application of Neural Network Models and ANFIS for Water Level Forecasting of the Salve Faccha Dam in the Andean Zone in Northern Ecuador

Water ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 2011
Author(s):  
Pablo Páliz Larrea ◽  
Xavier Zapata Ríos ◽  
Lenin Campozano Parra
Keyword(s):  
Neural Network ◽  
Water Level ◽  
Clustering Algorithm ◽  
Network Models ◽  
Water Levels ◽  
Subtractive Clustering ◽  
Neural Network Models ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Maximum Delay

Despite the importance of dams for water distribution of various uses, adequate forecasting on a day-to-day scale is still in great need of intensive study worldwide. Machine learning models have had a wide application in water resource studies and have shown satisfactory results, including the time series forecasting of water levels and dam flows. In this study, neural network models (NN) and adaptive neuro-fuzzy inference systems (ANFIS) models were generated to forecast the water level of the Salve Faccha reservoir, which supplies water to Quito, the Capital of Ecuador. For NN, a non-linear input–output net with a maximum delay of 13 days was used with variation in the number of nodes and hidden layers. For ANFIS, after up to four days of delay, the subtractive clustering algorithm was used with a hyperparameter variation from 0.5 to 0.8. The results indicate that precipitation was not influencing input in the prediction of the reservoir water level. The best neural network and ANFIS models showed high performance, with a r > 0.95, a Nash index > 0.95, and a RMSE < 0.1. The best the neural network model was t + 4, and the best ANFIS model was model t + 6.

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