Comparing lazy and eager learning models for water level forecasting in river-reservoir basins of inundation regions

Wei, Chih-Chiang

doi:10.1016/j.envsoft.2014.09.026

Cited by 32 publications

(14 citation statements)

References 74 publications

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“…The main advantages of kNN include simplicity or lazy learning (no explicit quantification between inputs and outputs) and nonparameter (no assumption on the dataset distribution). The kNN has been applied to open water hydrological studies [13][14][15][16] and received much attention recently [17][18][19][20][21][22][23][24][25][26]. The kNN is very suitable for river ice forecasting because the difficulty in direct quantification of complex river ice mechanisms described by scarce data can be avoided.…”

Section: Introductionmentioning

confidence: 99%

Modeling River Ice Breakup Dates by k-Nearest Neighbor Ensemble

Sun

Liu

et al. 2020

Water

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Forecasting of river ice breakup timing is directly related to the local ice-caused flooding management. However, river ice forecasting using k-nearest neighbor (kNN) algorithms is limited. Thus, a kNN stacking ensemble learning (KSEL) method was developed and applied to forecasting breakup dates (BDs) for the Athabasca River at Fort McMurray in Canada. The kNN base models with diverse inputs and distance functions were developed and their outputs were further combined. The performance of these models was examined using the leave-one-out cross validation method based on the historical BDs and corresponding climate and river conditions in 1980–2015. The results indicated that the kNN with the Chebychev distance functions generally outperformed other kNN base models. Through the simple average methods, the ensemble kNN models using multiple-type (Mahalanobis and Chebychev) distance functions had the overall optimal performance among all models. The improved performance indicates that the kNN ensemble is a promising tool for river ice forecasting. The structure of optimal models also implies that the breakup timing is mainly linked with temperature and water flow conditions before breakup as well as during and just after freeze up.

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Section: Introductionmentioning

confidence: 99%

Modeling River Ice Breakup Dates by k-Nearest Neighbor Ensemble

Sun

Liu

et al. 2020

Water

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“…In addition to the numerous physical models currently available, the development of the field of machine learning (ML) has also increased the frequency with which data-driven models, defined as computer programming in which program statements describe data to be matched and the processing required, are used [21][22][23]. The ML model and statistical methods are based on myriad historical data not considering the meteorological conditions.…”

Section: Introductionmentioning

confidence: 99%

Study on Wind Simulations Using Deep Learning Techniques during Typhoons: A Case Study of Northern Taiwan

Wei

2019

Atmosphere

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A scheme for wind-speed simulation during typhoons in Taiwan is highly desirable, considering the effects of the powerful winds accompanying the severe typhoons. The developed combination of deep learning (DL) algorithms with a weather-forecasting numerical model can be used to determine wind speed in a rapid simulation process. Here, the Weather Research and Forecasting (WRF) numerical model was employed as the numerical simulation-based model for precomputing solutions to determine the wind velocity at arbitrary positions where the wind cannot be measured. The deep neural network (DNN) was used for constructing the DL-based wind-velocity simulation model. The experimental area of Northern Taiwan was used for the simulation. Regarding the complex typhoon system, the collected data comprised the typhoon tracks, FNL (Final) Operational Global Analysis Data for the WRF model, typhoon characteristics, and ground weather data. This study included 47 typhoon events that occurred over 2000–2017. Three measures were used to analyze the models for identifying optimal performance levels: Mean absolute error, root mean squared error, and correlation coefficient. This study compared observations with the WRF numerical model and DNN model. The results revealed that (1) simulations by using the WRF-based models were satisfactorily consistent with the observed data and (2) simulations by using the DNN model were considerably consistent with those of the WRF-based model. Consequently, the proposed DNN combined with WRF model can be effectively used in simulations of wind velocity at arbitrary positions of study area.

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“…Table 1 presents the per-unit descriptive statistics of all the parameters, namely their maximum value, minimum value, mean value, and standard deviation. algorithms to achieve computational tractability [24,25]. In addition, LR was selected to compare with the aforementioned models because LR is a traditional regression analysis modeling technique.…”

Section: Study Site and Datamentioning

confidence: 99%

“…In this paper, surface solar irradiation forecasting models (SSIFMs) were constructed good performance [23]. Regarding NNS, the typical kNN method involves using neighbor search algorithms to achieve computational tractability [24,25]. In addition, LR was selected to compare with the aforementioned models because LR is a traditional regression analysis modeling technique.…”

Section: Introductionmentioning

confidence: 99%

“…Solar energy provides an environmentally friendly solution to generating electricity. Located at the latitude [22][23][24][25] • N, Taiwan receives a long duration of sunshine and stands at a small sunlight deflection angle. Currently the world's second largest producer of silicon-wafer solar cells, it is well-positioned to develop solar-energy technologies.…”

Section: Introductionmentioning

confidence: 99%

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Predictions of Surface Solar Radiation on Tilted Solar Panels using Machine Learning Models: A Case Study of Tainan City, Taiwan

Wei

2017

Energies

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Abstract:In this paper, forecasting models were constructed to estimate surface solar radiation on an hourly basis and the solar irradiance received by solar panels at different tilt angles, to enhance the capability of photovoltaic systems by estimating the amount of electricity they generate, thereby improving the reliability of the power they supply. The study site was Tainan in southern Taiwan, which receives abundant sunlight because of its location at a latitude of approximately 23 • . Four forecasting models of surface solar irradiance were constructed, using the multilayer perceptron (MLP), random forests (RF), k-nearest neighbors (kNN), and linear regression (LR), algorithms, respectively. The forecast horizon ranged from 1 to 12 h. The findings are as follows: first, solar irradiance was effectively estimated when a combination of ground weather data and solar position data was applied. Second, the mean absolute error was higher in MLP than in RF and kNN, and LR had the worst predictive performance. Third, the observed total solar irradiance was 1.562 million w/m 2 per year when the solar-panel tilt angle was 0 • (i.e., the non-tilted position) and peaked at 1.655 million w/m 2 per year when the angle was 20-22 • . The level of the irradiance was almost the same when the solar-panel tilt angle was 0 • as when the angle was 41 • . In summary, the optimal solar-panel tilt angle in Tainan was 20-22 • .

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Comparing lazy and eager learning models for water level forecasting in river-reservoir basins of inundation regions

Cited by 32 publications

References 74 publications

Modeling River Ice Breakup Dates by k-Nearest Neighbor Ensemble

Modeling River Ice Breakup Dates by k-Nearest Neighbor Ensemble

Study on Wind Simulations Using Deep Learning Techniques during Typhoons: A Case Study of Northern Taiwan

Predictions of Surface Solar Radiation on Tilted Solar Panels using Machine Learning Models: A Case Study of Tainan City, Taiwan

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