Forecasting stream water temperature using regression analysis, artificial neural network, and chaotic non-linear dynamic models

Sahoo, G. B.; Schladow, S. Geoffrey; Reuter, John E.

doi:10.1016/j.jhydrol.2009.09.037

Cited by 159 publications

(89 citation statements)

References 51 publications

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“…Figure 7 indicates that the lake clarity model simulates lake dynamics well. Detailed calibration and validation results are also available in Roberts and Reuter (2007) and Sahoo et al (2007b). The calibrated and validated LCM is used to examine the Lake Tahoe's future (i.e.…”

Section: Lake Clarity Modelmentioning

confidence: 99%

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Effects of climate change on thermal properties of lakes and reservoirs, and possible implications

Sahoo

Schladow

Reuter

et al. 2010

Stoch Environ Res Risk Assess

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Meteorologic-driven processes exert large and diverse impacts on lakes' internal heating, cooling, and mixing. Thus, continued global warming and climate change will affect lakes' thermal properties, dynamics, and ecosystem. The impact of climate change on Lake Tahoe (in the states of California and Nevada in the United States) is investigated here, as a case study of climate change effects on the physical processes occurring within a lake. In the Tahoe basin, air temperature data show upward trends and streamflow trends indicate earlier snowmelt. Precipitation in the basin is shifting from snow to rain, and the frequency of intense rainfall events is increasing. In-lake water temperature records of the past 38 years show that Lake Tahoe is warming at an average rate of 0.013°C/year. The future trends of weather variables, such as air temperature, precipitation, longwave radiation, downward shortwave radiation, and wind speed are estimated from predictions of three General Circulation Models (GCMs) for the period 2001-2100. Future trends of weather variables of each GCM are found to be different to those of the other GCMs. A series of simulation years into the future (2000-2040) is established using streamflows and associated loadings, and meteorologic data sets for the period 1994-2004. Future simulation years and trends of weather variables are selected so that: (1) future simulated warming trend would be consistent with the observed warming trend (0.013°C/year); and (2) future mixing pattern frequency would closely match with the historical mixing pattern frequency. Results of 40-year simulations show that the lake continues to become warmer and more stable, and mixing is reduced. Continued warming in the Tahoe has important implications for efforts towards managing biodiversity and maintaining clarity of the lake.

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Section: Lake Clarity Modelmentioning

confidence: 99%

“…Finally, fine (\20 lm) inorganic sediment has been shown to play an important role in reducing the clarity of the lake (Swift et al 2006;Sahoo et al 2007b). This impact is greatest in years following heavy stream runoff, and is prolonged by an absence of deep-water mixing events (Jassby et al 1999).…”

Section: Possible Effects On Lake Due To Warming Trendmentioning

confidence: 99%

Effects of climate change on thermal properties of lakes and reservoirs, and possible implications

Sahoo

Schladow

Reuter

et al. 2010

Stoch Environ Res Risk Assess

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show abstract

“…In order to predict and study the strength of the dependent and independent variables among them, the Multiple Linear Regression (MLR) method is used. The Multiple Linear Regression (MLR) method is commonly used techniques to obtain a linear input output model for a given dataset [11] [12]. However, this model will face some difficulties, especially when the independent variables are following certain distribution.…”

Section: Introductionmentioning

confidence: 99%

Improved the Prediction of Multiple Linear Regression Model Performance Using the Hybrid Approach: A Case Study of Chlorophyll-a at the Offshore Kuala Terengganu, Terengganu

Lola¹,

Ramlee²,

Gunalan³

et al. 2016

OJS

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Efficiency and precision in prediction of Chlorophyll-a using this model is still a pandemic among researchers, due to the natural conditions in ocean water systems itself, which involved chemical, biological and physical processes and interaction among them may affect the model performance drastically. Thus, to overcome this problem as well as to improve the strength of MLR, we proposed a hybrid approach, i.e., an Artificial Neural Network to the MLR coins as Artificial Neural NetworkMultiple Linear Regression (ANN-MLR). To investigate the performance of the proposed model, we compared Multiple Linear Regression (MLR), Artificial Neural Network (ANN) and proposed hybrid Artificial Neural Network and Multiple Linear Regression (ANN-MLR) in the prediction of chlorophyll-a (chl-a) concentration by statistical measurement which are MSE and MAE. Achieving our objectives of study, we used 4 parameters, i.e. temperature (˚C), pH, salinity (ppt), DO (ppm) at the Offshore Kuala Terengganu, Terengganu, Malaysia. The results showed that our proposed model can improve the performance of the model as compared to ANN and MLR due to small errors generated, error reduced, and increased the correlation coefficient for all parameters in both MSE and MAE, respectively. Thus, this result indicated that our proposed model is efficient, precise and almost perfect correlation as compared to ANN and MLR.

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“…Lack of knowledge about the potential for sunlight to affect Tw has led to inconsistency in sensor deployment [8][9][10][11][12][13][14][15]. For example, some researchers shade sensors with plastic tubes [8] whereas others do not [9], or fail to mention this detail [10].…”

Section: Introductionmentioning

confidence: 99%

Shield or not to Shield: Effects of Solar Radiation on Water Temperature Sensor Accuracy

Johnson

Wilby

2013

Water

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Temperature sensors are potentially susceptible to errors due to heating by solar radiation. Although this is well known for air temperature (Ta), significance to continuous water temperature (Tw) monitoring is relatively untested. This paper assesses radiative errors by comparing measurements of exposed and shielded Tinytag sensors under indirect and direct solar radiation, and in laboratory experiments under controlled, artificial light. In shallow, still-water and under direct solar radiation, measurement discrepancies between exposed and shielded sensors averaged 0.4 °C but can reach 1.6 °C. Around 0.3 °C of this inconsistency is explained by variance in measurement accuracy between sensors; the remainder is attributed to solar radiation. Discrepancies were found to increase with light intensity, but to attain Tw differences in excess of 0.5 °C requires direct, bright solar radiation (>400 W m −2 in the total spectrum). Under laboratory conditions, radiative errors are an order of magnitude lower when thermistors are placed in flowing water (even at velocities as low as 0.1 m s −1). Radiative errors were also modest relative to the discrepancy between different thermistor manufacturers. Based on these controlled experiments, a set of guidelines are recommended for deploying thermistor arrays in water bodies.

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Forecasting stream water temperature using regression analysis, artificial neural network, and chaotic non-linear dynamic models

Cited by 159 publications

References 51 publications

Effects of climate change on thermal properties of lakes and reservoirs, and possible implications

Effects of climate change on thermal properties of lakes and reservoirs, and possible implications

Improved the Prediction of Multiple Linear Regression Model Performance Using the Hybrid Approach: A Case Study of Chlorophyll-a at the Offshore Kuala Terengganu, Terengganu

Shield or not to Shield: Effects of Solar Radiation on Water Temperature Sensor Accuracy

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