scholarly journals Machine Learning-Based Water Level Prediction in Lake Erie

Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2654
Author(s):  
Qi Wang ◽  
Song Wang
Keyword(s):  
Machine Learning ◽  
Water Level ◽  
Lake Erie ◽  
Water Resource Management ◽  
Predictive Accuracy ◽  
Meteorological Data ◽  
Water Levels ◽  
Paper Machine ◽  
K Nearest Neighbor ◽  
Time Saving

Predicting water levels of Lake Erie is important in water resource management as well as navigation since water level significantly impacts cargo transport options as well as personal choices of recreational activities. In this paper, machine learning (ML) algorithms including Gaussian process (GP), multiple linear regression (MLR), multilayer perceptron (MLP), M5P model tree, random forest (RF), and k-nearest neighbor (KNN) are applied to predict the water level in Lake Erie. From 2002 to 2014, meteorological data and one-day-ahead observed water level are the independent variables, and the daily water level is the dependent variable. The predictive results show that MLR and M5P have the highest accuracy regarding root mean square error (RMSE)  and mean absolute error (MAE). The performance of ML models has also been compared against the performance of the process-based advanced hydrologic prediction system (AHPS), and the results indicate that ML models are superior in predictive accuracy compared to AHPS. Together with their time-saving advantage, this study shows that ML models, especially MLR and M5P, can be used for forecasting Lake Erie water levels and informing future water resources management.

scholarly journals An automatic lake-model application using near real-time data forcing: Development of an operational forecast model for Lake Erie

2021 ◽  
Author(s):  
Shuqi Lin ◽  
Leon Boegman ◽  
Shiliang Shan ◽  
Ryan Mulligan
Keyword(s):  
Open Access ◽  
Real Time ◽  
Public Safety ◽  
Lake Erie ◽  
Water Resource Management ◽  
Computational Models ◽  
Fishery Management ◽  
Water Levels ◽  
Time Data ◽  
Forecast System

Abstract. For enhanced public safety and water resource management, a three-dimensional operational lake hydrodynamic forecast system called COASTLINES (Canadian cOASTal and Lake forecastINg modEl System) was developed. The modelling system is built upon the Aquatic Ecosystem Model (AEM3D) model, with predictive simulation capabilities developed and tested for a large lake (i.e., Lake Erie). The open-access web-based platform derives model forcing, code execution, post-processing and visualization of the model outputs, including water level elevations and temperature, is in near real-time. COASTLINES currently generates 240-h predictions using atmospheric forcing from 15 km and 25 km horizontal-resolution operational meteorological products from the Environment Canada Global Deterministic Forecast System (GDPS). Simulated water levels were validated against observations from 6 gauge stations, with model error increasing for longer forecast times. Satellite images and lake buoys were applied to validate forecast lake surface temperature (LST) and the water column thermal stratification. The forecast LST is as accurate as hindcasts, with a root-mean-square-deviation < 2 ℃. COASTLINES predicts storm-surge events and up-/down-welling events that are important for flood water and drinking water/fishery management, respectively. Model forecasts are available in real-time at https://coastlines.engineering.queensu.ca/. This study provides an example of the successful development of an operational forecasting system, entirely driven by open-access data, that may be easily adapted to simulate aquatic systems or to drive other computational models, as required for management and public safety.

scholarly journals Kabul River Flow Prediction Using Automated ARIMA Forecasting: A Machine Learning Approach

2021 ◽  
Vol 13 (19) ◽  
pp. 10720
Author(s):  
Muhammad Ali Musarat ◽  
Wesam Salah Alaloul ◽  
Muhammad Babar Ali Rabbani ◽  
Mujahid Ali ◽  
Muhammad Altaf ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Level ◽  
River Flow ◽  
Early Stage ◽  
Moving Average ◽  
Weather Prediction ◽  
Maximum Level ◽  
Information Criterion ◽  
Water Levels ◽  
Kabul River

The water level in a river defines the nature of flow and is fundamental to flood analysis. Extreme fluctuation in water levels in rivers, such as floods and droughts, are catastrophic in every manner; therefore, forecasting at an early stage would prevent possible disasters and relief efforts could be set up on time. This study aims to digitally model the water level in the Kabul River to prevent and alleviate the effects of any change in water level in this river downstream. This study used a machine learning tool known as the automatic autoregressive integrated moving average for statistical methodological analysis for forecasting the river flow. Based on the hydrological data collected from the water level of Kabul River in Swat, the water levels from 2011–2030 were forecasted, which were based on the lowest value of Akaike Information Criterion as 9.216. It was concluded that the water flow started to increase from the year 2011 till it reached its peak value in the year 2019–2020, and then the water level will maintain its maximum level to 250 cumecs and minimum level to 10 cumecs till 2030. The need for this research is justified as it could prove helpful in establishing guidelines for hydrological designers, the planning and management of water, hydropower engineering projects, as an indicator for weather prediction, and for the people who are greatly dependent on the Kabul River for their survival.

scholarly journals EXTREME WATER LEVEL EXCEEDANCE PROBABILITIES AROUND AUSTRALIA

2012 ◽  
Vol 1 (33) ◽  
pp. 53
Author(s):  
Leigh MacPherson ◽  
Ivan David Haigh ◽  
Matthew Mason ◽  
Sarath Wijeratne ◽  
Charitha Pattiaratchi ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
Water Level ◽  
Return Period ◽  
Meteorological Data ◽  
Storm Surges ◽  
Value Theory ◽  
Water Levels ◽  
Sea Levels ◽  
Extreme Water Level ◽  
Extreme Water Levels

The potential impacts of extreme water level events on our coasts are increasing as populations grow and sea levels rise. To better prepare for the future, coastal engineers and managers need accurate estimates of average exceedance probabilities for extreme water levels. In this paper, we estimate present day probabilities of extreme water levels around the entire coastline of Australia. Tides and storm surges generated by extra-tropical storms were included by creating a 61-year (1949-2009) hindcast of water levels using a high resolution depth averaged hydrodynamic model driven with meteorological data from a global reanalysis. Tropical cyclone-induced surges were included through numerical modelling of a database of synthetic tropical cyclones equivalent to 10,000 years of cyclone activity around Australia. Predicted water level data was analysed using extreme value theory to construct return period curves for both the water level hindcast and synthetic tropical cyclone modelling. These return period curves were then combined by taking the highest water level at each return period.

scholarly journals Predicting Fine Particulate Matter (PM2.5) in the Greater London Area: An Ensemble Approach using Machine Learning Methods

2020 ◽  
Vol 12 (6) ◽  
pp. 914 ◽  
Author(s):  
Mahdieh Danesh Yazdi ◽  
Zheng Kuang ◽  
Konstantina Dimakopoulou ◽  
Benjamin Barratt ◽  
Esra Suel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Nearest Neighbor ◽  
Meteorological Data ◽  
Fine Particulate Matter ◽  
Gradient Boosting ◽  
K Nearest Neighbor ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Technological Advances

Estimating air pollution exposure has long been a challenge for environmental health researchers. Technological advances and novel machine learning methods have allowed us to increase the geographic range and accuracy of exposure models, making them a valuable tool in conducting health studies and identifying hotspots of pollution. Here, we have created a prediction model for daily PM2.5 levels in the Greater London area from 1st January 2005 to 31st December 2013 using an ensemble machine learning approach incorporating satellite aerosol optical depth (AOD), land use, and meteorological data. The predictions were made on a 1 km × 1 km scale over 3960 grid cells. The ensemble included predictions from three different machine learners: a random forest (RF), a gradient boosting machine (GBM), and a k-nearest neighbor (KNN) approach. Our ensemble model performed very well, with a ten-fold cross-validated R2 of 0.828. Of the three machine learners, the random forest outperformed the GBM and KNN. Our model was particularly adept at predicting day-to-day changes in PM2.5 levels with an out-of-sample temporal R2 of 0.882. However, its ability to predict spatial variability was weaker, with a R2 of 0.396. We believe this to be due to the smaller spatial variation in pollutant levels in this area.

scholarly journals Accuracy Enhancement for Forecasting Water Levels of Reservoirs and River Streams Using a Multiple-Input-Pattern Fuzzification Approach

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Nariman Valizadeh ◽  
Ahmed El-Shafie ◽  
Majid Mirzaei ◽  
Hadi Galavi ◽  
Muhammad Mukhlisin ◽  
...  
Keyword(s):  
Water Level ◽  
Case Studies ◽  
Water Resource Management ◽  
Water Levels ◽  
Anfis Model ◽  
New Approach ◽  
Multiple Input ◽  
Different Types ◽  
Output Pattern ◽  
And Performance

Water level forecasting is an essential topic in water management affecting reservoir operations and decision making. Recently, modern methods utilizing artificial intelligence, fuzzy logic, and combinations of these techniques have been used in hydrological applications because of their considerable ability to map an input-output pattern without requiring prior knowledge of the criteria influencing the forecasting procedure. The artificial neurofuzzy interface system (ANFIS) is one of the most accurate models used in water resource management. Because the membership functions (MFs) possess the characteristics of smoothness and mathematical components, each set of input data is able to yield the best result using a certain type of MF in the ANFIS models. The objective of this study is to define the different ANFIS model by applying different types of MFs for each type of input to forecast the water level in two case studies, the Klang Gates Dam and Rantau Panjang station on the Johor river in Malaysia, to compare the traditional ANFIS model with the new introduced one in two different situations, reservoir and stream, showing the new approach outweigh rather than the traditional one in both case studies. This objective is accomplished by evaluating the model fitness and performance in daily forecasting.

Adaptive forecast of multi-month lake level elevations

1994 ◽  
Vol 21 (5) ◽  
pp. 778-788 ◽  
Author(s):  
Saad Bennis ◽  
Gabriel J. Assaf
Keyword(s):  
Water Level ◽  
Lake Erie ◽  
Flood Control ◽  
Ordinary Least Squares ◽  
Water Levels ◽  
Control Measures ◽  
Type Model ◽  
Model Parameters ◽  
Time Step ◽  
Kalman Predictor

The early and precise prediction of the water levels in lakes is a major concern for public authorities. Such predictions describe the evolution of the water levels and are essential for appropriate flood control measures. In this paper, a new ARMAX-type model is developed to predict, months in advance, the monthly fluctuations of the water level of Lake Erie. The predictive variables used in the model are the past monthly water levels of Lakes Erie, Superior, and Michigan–Huron along with the estimated response times between water flow entries and exits. Two scenarios are compared. The first scenario is based on the ordinary least squares (OLS) technique in order to identify the parameters of the ARMAX-type model, to filter measurement and model noises, using the ARMAX Kalman predictor (AKP), and to optimize predictions. In this scenario, the model parameters remain unchanged throughout the simulation. The second scenario is based on the mutually interactive state parameter (MISP) technique in order to readjust the parameters of the model at each time step and to filter measurement and modelling noises through the Kalman predictor. In this scenario, the parameters of the model change with time. The analysis shows that the MISP–AKP framework has a slightly higher Nash coefficient than the OLS–AKP framework for the first month. In subsequent months, however, the quality of the predictions based on the OLS–AKP technique improves significantly. This observation also applies to the persistence and extrapolation coefficients as well as to the sample autocorrelation functions for the residuals of the Lake Erie water level forecast. It was therefore decided to apply the MISP–AKP technique to obtain the first prediction of the Lake Erie level, and the OLS–AKP technique to compute subsequent predictions. Key words: adaptive, forecast, Kalman's filter, lake levels, MISP algorithm, Great Lakes.

Water Level Prediction of Rainwater Pipe Network Using an SVM-Based Machine Learning Method

2019 ◽  
Vol 34 (02) ◽  
pp. 2051002 ◽  
Author(s):  
Hao Wang ◽  
Lixiang Song
Keyword(s):  
Machine Learning ◽  
Water Level ◽  
Water Levels ◽  
Machine Learning Method ◽  
Learning Method ◽  
Pipe Network ◽  
Hydrodynamic Models ◽  
Time Step ◽  
Running Speed ◽  
Machine Learning Model

Model accuracy and running speed are the two key issues for flood warning in urban areas. Traditional hydrodynamic models, which have a rigorous physical mechanism for flood routine, have been widely adopted for water level prediction of rainwater pipe network. However, with the amount of pipes increasing, both the running speed and data availability of hydrodynamic models would be decreased rapidly. To achieve a real-time prediction for the water level of the rainwater pipe network, a new framework based on a machine learning method was proposed in this paper. The spatial and temporal autocorrelation of water levels for adjacent manholes was revealed through theoretical analysis, and then a support vector machine (SVM)-based machine learning model was developed, in which the water levels of adjacent manholes and rivers-near-by-outlets at the last time step were chosen as the independent variables, and then the water levels at the current time step can be computed by the proposed machine learning model with calibrated parameters. The proposed framework was applied in Fuzhou city, China. It turns out that the proposed machine learning method can forecast the water level of the rainwater pipe network with good accuracy and running speed.

scholarly journals Evapotranspiration Partitioning and Response to Abnormally Low Water Levels in a Floodplain Wetland in China

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Xiaosong Zhao ◽  
Yuanbo Liu
Keyword(s):  
Water Level ◽  
Land Surface ◽  
Water Budget ◽  
Poyang Lake ◽  
Meteorological Data ◽  
Partition Ratio ◽  
Water Levels ◽  
Water Evaporation ◽  
Floodplain Wetland ◽  
Wetland Water

Evapotranspiration (ET) is an important component of the wetland water budget. Water level declines in Poyang Lake, the largest freshwater lake in China, have caused concerns, especially during low water levels. However, how wetland ET and its partitioning respond to abnormally low water levels is unclear. In this study, wetland ET was estimated with MODIS data and meteorological data. The wetland ET partitioning and its relationship with abnormally low water levels were analyzed for 2000–2013. The results showed that the water evaporation rate (Ewater) was larger than the land ET rate (ETland); theETland/Ewaterranged from 0.77 to 0.99. When the water level was below 12.8 m, the ET partition ratio was larger than 1, which indicates that wetland ET comes from land surface ET more than water evaporation. The negative standardized water level index (SWI) was used to represent an abnormally low water level in the wetland. Although the monthly wetland ET decreased as the negative SWI decreased,ETlandwas higher than the average under negative SWI conditions from September to December, when the water level decreased. The abnormally low water level induced more water loss from the land surface, especially when the water level decreased, which reduced the available water resources along the wetland shore.

scholarly journals Towards Improving Offline Signature Verification based Authentication Using Machine Learning Classifiers

2019 ◽  
Vol 8 (11) ◽  
pp. 3393-3401
Keyword(s):  
Machine Learning ◽  
Sample Size ◽  
Nearest Neighbor ◽  
Turnaround Time ◽  
Signature Verification ◽  
Support Vector ◽  
Paper Machine ◽  
K Nearest Neighbor ◽  
Machine Learning Classifiers ◽  
Learning Classifiers

Signatures have been accepted in commercial transactions as a method of authentication. Digitizing credentials reduce the storage space requisite for the same information from a few cubic inches to so many bytes on a server. The most frequent use of offline signature authentication is to reduce the turnaround time for cheque clearance. In this paper, machine learning classifiers are used to verify the signature using four image based features. BHsig260 dataset (Bangla and Hindi) has been used. We used signatures of 55 users of Hindi and Bangla each. .Six classifier i.e. Boosted Tree, Random forest classifier (RFC), K-nearest neighbor, Multilayer Perceptron, Support Vector Machine (SVM) and Naive Bayes classifier are used in the work. In the paper, the results of Writer independent model show that accuracy of Hindi off-line signature verification is 72.3 % using MLP with the signature sample size of 20 and that of Bangla is 79 % using RFC with the signature sample size of 23.In user dependent model, for some users, we achieved accuracy of more than 92 % using KNN and SVM.

scholarly journals Remote sensing applications for reservoir water level monitoring, sustainable water surface management, and environmental risks in Quang Nam province, Vietnam

2021 ◽  
Author(s):  
Dinh Nhat Quang ◽  
Nguyen Khanh Linh ◽  
Ho Sy Tam ◽  
Nguyen Trung Viet
Keyword(s):  
Water Management ◽  
Remote Sensing ◽  
Surface Water ◽  
Water Level ◽  
Water Resource Management ◽  
Measurement Data ◽  
Water Levels ◽  
Sensing Applications ◽  
Volume Curve

Abstract Monitoring surface water provides vital information in water management; however, limited data is a fundamental challenge for most developing countries, such as Vietnam. Based on advanced remote sensing technologies, the authors proposed a methodology to process satellite images and use their outcomes to extract surface water in water resource management of Quang Nam province. Results of the proposed study show good agreement with in situ measurement data when the obtained Overall Accuracy and Kappa Coefficient were greater than 90% and 0.99, respectively. Three potential applications based on the surface water results are selected to discuss sustainable water management in Quang Nam province. Firstly, reservoir operating processes can be examined, enhanced, and even developed through long-term extracted water levels, which are the interpolation results between the extracted surface water area and the water level–area–volume curve. Secondly, the long-term morphological change for the Truong Giang river case between 1990 and 2019 can also be detected from the Water Frequency Index performance and provided additional information regarding permanent and seasonal water changes. Lastly, the flood inundation extent was extracted and separated from permanent water to assess the damage of the Mirinae typhoon on 2 November 2009 in terms of population and crop aspects.

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