scholarly journals Prediction of river water temperature using machine learning algorithms: a tropical river system of India

2021 ◽  
Author(s):  
M. Rajesh ◽  
S. Rehana
Keyword(s):  
Machine Learning ◽  
Sensitivity Analysis ◽  
Water Temperature ◽  
River Water ◽  
River System ◽  
Support Vector ◽  
Tropical River ◽  
Global Sensitivity ◽  
Model Complex ◽  
River Water Temperature

Abstract Machine learning (ML) has been increasingly adopted due to its ability to model complex and non-linearities between river water temperature (RWT) and its predictors (e.g., Air Temperature, AT). Most of these ML approaches have been applied using average AT without any detailed sensitivity analysis of other forms of AT (e.g., maximum and minimum). The present study demonstrates how new ML approaches, such as ridge regression (RR), K-nearest neighbors (KNN) regressor, random forest (RF) regressor, and support vector regression (SVR), can be coupled with Sobol’ global sensitivity analysis (GSA) to predict accurate RWT estimates with the most appropriate form of AT. Furthermore, the proposed ML approaches have been combined with the Ensemble Kalman Filter (EnKF), a data assimilation (DA) technique to improve the predicted values based on the measured data. The proposed modelling framework's effectiveness is demonstrated with a tropical river system of India, Tunga-Bhadra River, as a case study. The SVR has been noted as the most robust ML model to predict RWT at a monthly time scale compared with daily and seasonal. The study demonstrates how ML methods can be coupled with a global sensitivity algorithm and DA techniques to generate accurate RWT predictions in river water quality modelling.

scholarly journals Assessing the performance of a suite of machine learning models for daily river water temperature prediction

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7065 ◽  
Author(s):  
Senlin Zhu ◽  
Emmanuel Karlo Nyarko ◽  
Marijana Hadzima-Nyarko ◽  
Salim Heddam ◽  
Shiqiang Wu
Keyword(s):  
Machine Learning ◽  
Water Temperature ◽  
River Water ◽  
Minor Role ◽  
Learning Models ◽  
Temperature Prediction ◽  
Flow Discharge ◽  
River Water Temperature ◽  
Machine Learning Models

In this study, different versions of feedforward neural network (FFNN), Gaussian process regression (GPR), and decision tree (DT) models were developed to estimate daily river water temperature using air temperature (Ta), flow discharge (Q), and the day of year (DOY) as predictors. The proposed models were assessed using observed data from eight river stations, and modelling results were compared with the air2stream model. Model performances were evaluated using four indicators in this study: the coefficient of correlation (R), the Willmott index of agreement (d), the root mean squared error (RMSE), and the mean absolute error (MAE). Results indicated that the three machine learning models had similar performance when only Ta was used as the predictor. When the day of year was included as model input, the performances of the three machine learning models dramatically improved. Including flow discharge instead of day of year, as an additional predictor, provided a lower gain in model accuracy, thereby showing the relatively minor role of flow discharge in river water temperature prediction. However, an increase in the relative importance of flow discharge was noticed for stations with high altitude catchments (Rhône, Dischmabach and Cedar) which are influenced by cold water releases from hydropower or snow melting, suggesting the dependence of the role of flow discharge on the hydrological characteristics of such rivers. The air2stream model outperformed the three machine learning models for most of the studied rivers except for the cases where including flow discharge as a predictor provided the highest benefits. The DT model outperformed the FFNN and GPR models in the calibration phase, however in the validation phase, its performance slightly decreased. In general, the FFNN model performed slightly better than GPR model. In summary, the overall modelling results showed that the three machine learning models performed well for river water temperature modelling.

Efficient modelling of water temperature patterns in river systems – benchmarking a set of machine learning approaches

2020 ◽  
Author(s):  
Mathew Herrnegger ◽  
Moritz Feigl ◽  
Katharina Lebiedzinski ◽  
Karsten Schulz
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Water Temperature ◽  
River Water ◽  
Large Scale ◽  
Data Availability ◽  
Learning Approaches ◽  
River Temperature ◽  
River Water Temperature ◽  
Process Based Models

<p>Many approaches for modelling river water temperature are available, but not one exist that can be applied without restrictions. The applied method depends on data availability, dominant processes, scales and transferability. Process-based models are currently the best way to evaluate detailed management scenarios on reach scale and to understand underlying processes.  Due to limitations of data availability, however, more simplified approaches are frequently applied, where different meteorological or hydrological time series are statistically related to water temperature (or in the simplest case only using air temperature). Here, machine learning methods could help bridging a gap by allowing for more complex relationships without setting prior assumptions. They are thus integrating reasonable processes and dynamics within the catchment by learning from given data. However, up-to-date machine learning approaches have rarely been used in this field until now.</p><p>This contribution analyses a set of machine learning approaches for large-scale river temperature modelling. Deep learning methods, random forests and boosting methods are compared with the performance of commonly used simple and multiple regression models. These approaches are tested on 10 catchments with different characteristics, human impacts (e.g. hydropower, river regulation) and time series lengths (10 to 39 years). They are situated in the Austrian Alps or flatlands with areas ranging from 200 to 96.000 km². Observed data including daily means of river water temperature, air temperature, discharge, precipitation and global radiation are grouped to simple and advanced sets of input variables to analyse possible data dependencies. </p><p>In summary, we compare up-to-date machine learning approaches for their applicability in river water temperature prediction. By implementing necessary data preprocessing steps and machine learning routines in a R package, we aim to make these findings easily accessible and reproducible for the community. This tool provides an attractive approach for large-scale river temperature modelling, where the requirements for using process-based models are not able to be met. Future applications can include e.g. short and long term forecasting of river water temperature to find management options for balancing environmental requirements. </p>

scholarly journals Development of a Multilayer Deep Neural Network Model for Predicting Hourly River Water Temperature From Meteorological Data

2021 ◽  
Vol 9 ◽  
Author(s):  
Reza Abdi ◽  
Ashley Rust ◽  
Terri S. Hogue
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Water Temperature ◽  
River Water ◽  
Air Temperature ◽  
Deep Neural Network ◽  
Single Layer ◽  
Machine Learning Algorithms ◽  
Ecosystem Functions ◽  
River Water Temperature

Water temperature is a vital attribute of physical riverine habitat and one of the focal objectives of river engineering and management. However, in most rivers, there are not enough water temperature measurements to characterize thermal regimes and evaluate its effect on ecosystem functions such as fish migration. To aid in river restoration, machine learning-based algorithms were developed to predict hourly river water temperature. We trained, validated, and tested single-layer and multilayer linear regression (LR) and deep neural network (DNN) algorithms to predict water temperature in the Los Angeles River in southern CA, United States. For the single-layer models, we considered air temperature as the predictive feature, and for the multilayer models, relative humidity, wind speed, and barometric pressure were included in addition to air temperature as the considered features. We trained the LR and DNN algorithms on Google’s TensorFlow model using Keras artificial neural network library on Python. Results showed that multilayer predictions performed better compared to single-layer models by producing mean absolute errors (MAEs), that were 20% smaller (1.05°C), on average, compared to the single-layer models (1.3°C). The multilayer DNN algorithm outperformed the other model where the model’s coefficient of determination was 26 and 12% higher compared to the single-layer LR (the base model) and multilayer LR model, respectively. The multilayer machine learning algorithms, under proper data preparation protocols, may be considered useful tools for predicting water temperatures in sampled and unsampled rivers for current conditions and future estimations affected by different stressors such as climate and land-use change. River temperature predictions from the developed models provide valuable information for evaluating sustainability of river ecosystems and biota.

STUDY ON CHANGING IN RIVER WATER TEMPERATURE IN KYUSYU REGION

2018 ◽  
Vol 74 (5) ◽  
pp. I_79-I_84
Author(s):  
Yoji NODA ◽  
Tomoko MINAGAWA ◽  
Hidetaka ICHIYANAGI ◽  
Akihiko KOYAMA
Keyword(s):  
Water Temperature ◽  
River Water ◽  
River Water Temperature

scholarly journals Exploring and Quantifying River Thermal Response to Heatwaves

Water ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 1098 ◽  
Author(s):  
Sebastiano Piccolroaz ◽  
Marco Toffolon ◽  
Christopher Robinson ◽  
Annunziato Siviglia
Keyword(s):  
Water Temperature ◽  
River Water ◽  
Thermal Response ◽  
Hydrological Regime ◽  
Management Policy ◽  
Extreme Weather Events ◽  
Thermal Dynamics ◽  
Depth Analysis ◽  
River Water Temperature

Most of the existing literature on river water temperature focuseds on river thermal sensitivity to long-term trends of climate variables, whereas how river water temperature responds to extreme weather events, such as heatwaves, still requires in-depth analysis. Research in this direction is particularly relevant in that heatwaves are expected to increase in intensity, frequency, and duration in the coming decades, with likely consequences on river thermal regimes and ecology. In this study we analyzed the long-term temperature and streamflow series of 19 Swiss rivers with different hydrological regime (regulated, low-land, and snow-fed), and characterized how concurrent changes in air temperature and streamflow concurred to affect their thermal dynamics. We focused on quantifying the thermal response to the three most significant heatwave events that occurred in Central Europe since 1950 (July–August 2003, July 2006, and July 2015). We found that the thermal response of the analyzed rivers contrasted strongly depending on the river hydrological regime, confirming the behavior observed under typical weather conditions. Low-land rivers were extremely sensitive to heatwaves. In sharp contrast, high-altitude snow-fed rivers and regulated rivers receiving cold water from higher altitude hydropower reservoirs or diversions showed a damped thermal response. The results presented in this study suggest that water resource managers should be aware of the multiple consequences of heatwave events on river water temperature and incorporate expected thermal responses in adaptive management policy. In this respect, additional efforts and dedicated studies are required to deepen our knowledge on how extreme heatwave events can affect river ecosystems.

scholarly journals Controls on Arctic glacier-fed river water temperature

2016 ◽  
Vol 62 (4) ◽  
pp. 499-514 ◽  
Author(s):  
Christopher J. Mellor ◽  
Stephen J. Dugdale ◽  
Grace Garner ◽  
Alexander M. Milner ◽  
David M. Hannah
Keyword(s):  
Water Temperature ◽  
River Water ◽  
Arctic Glacier ◽  
River Water Temperature
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