scholarly journals Evaluation of the BMA probabilistic inflow forecasts using TIGGE numeric precipitation predictions based on artificial neural network

2018 ◽  
Vol 49 (5) ◽  
pp. 1417-1433 ◽  
Author(s):  
Yixuan Zhong ◽  
Shenglian Guo ◽  
Huanhuan Ba ◽  
Feng Xiong ◽  
Fi-John Chang ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Bayesian Model Averaging ◽  
Weather Prediction ◽  
Lead Times ◽  
Hydrologic Modelling ◽  
Probabilistic Forecasts ◽  
Inflow Forecasting ◽  
Artificial Neural ◽  
Reservoir Basin

Abstract Reservoir inflow forecasting is a crucial task for reservoir management. Without considering precipitation predictions, the lead time for inflow is subject to the concentration time of precipitation in the basin. With the development of numeric weather prediction (NWP) techniques, it is possible to forecast inflows with long lead times. Since larger uncertainty usually occurs during the forecasting process, much attention has been paid to probabilistic forecasts, which uses a probabilistic distribution function instead of a deterministic value to predict the future status. In this study, we aim at establishing a probabilistic inflow forecasting scheme in the Danjiangkou reservoir basin based on NWP data retrieved from the Interactive Grand Global Ensemble (TIGGE) database by using the Bayesian model averaging (BMA) method, and evaluating the skills of the probabilistic inflow forecasts. An artificial neural network (ANN) is used to implement hydrologic modelling. Results show that the corrected TIGGE NWP data can be applied sufficiently to inflow forecasting at 1–3 d lead times. Despite the fact that the raw ensemble inflow forecasts are unreliable, the BMA probabilistic inflow forecasts perform much better than the raw ensemble forecasts in terms of probabilistic style and deterministic style, indicating the established scheme can offer a useful approach to probabilistic inflow forecasting.

Using Artificial Neural Networks for Generating Probabilistic Subseasonal Precipitation Forecasts over California

2020 ◽  
Vol 148 (8) ◽  
pp. 3489-3506
Author(s):  
Michael Scheuerer ◽  
Matthew B. Switanek ◽  
Rochelle P. Worsnop ◽  
Thomas M. Hamill
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Signal To Noise Ratio ◽  
Weather Prediction ◽  
Forecast Skill ◽  
Lead Times ◽  
Probabilistic Forecasts ◽  
Medium Range ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Abstract Forecast skill of numerical weather prediction (NWP) models for precipitation accumulations over California is rather limited at subseasonal time scales, and the low signal-to-noise ratio makes it challenging to extract information that provides reliable probabilistic forecasts. A statistical postprocessing framework is proposed that uses an artificial neural network (ANN) to establish relationships between NWP ensemble forecast and gridded observed 7-day precipitation accumulations, and to model the increase or decrease of the probabilities for different precipitation categories relative to their climatological frequencies. Adding predictors with geographic information and location-specific normalization of forecast information permits the use of a single ANN for the entire forecast domain and thus reduces the risk of overfitting. In addition, a convolutional neural network (CNN) framework is proposed that extends the basic ANN and takes images of large-scale predictors as inputs that inform local increase or decrease of precipitation probabilities relative to climatology. Both methods are demonstrated with ECMWF ensemble reforecasts over California for lead times up to 4 weeks. They compare favorably with a state-of-the-art postprocessing technique developed for medium-range ensemble precipitation forecasts, and their forecast skill relative to climatology is positive everywhere within the domain. The magnitude of skill, however, is low for week-3 and week-4, and suggests that additional sources of predictability need to be explored.

scholarly journals Rainfall Forecasting Using Various Artificial Neural Network Techniques - A Review

2021 ◽  
pp. 506-526
Author(s):  
Nisha Thakur ◽  
Sanjeev Karmakar ◽  
Sunita Soni
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Moving Average ◽  
Weather Prediction ◽  
Optimization Technique ◽  
Back Propagation ◽  
Rainfall Forecasting ◽  
Rainfall Prediction ◽  
Artificial Neural ◽  
Review Reports

The present review reports the work done by the various authors towards rainfall forecasting using the different techniques within Artificial Neural Network concepts. Back-Propagation, Auto-Regressive Moving Average (ARIMA), ANN , K- Nearest Neighbourhood (K-NN), Hybrid model (Wavelet-ANN), Hybrid Wavelet-NARX model, Rainfall-runoff models, (Two-stage optimization technique), Adaptive Basis Function Neural Network (ABFNN), Multilayer perceptron, etc., algorithms/technologies were reviewed. A tabular representation was used to compare the above-mentioned technologies for rainfall predictions. In most of the articles, training and testing, accuracy was found more than 95%. The rainfall prediction done using the ANN techniques was found much superior to the other techniques like Numerical Weather Prediction (NWP) and Statistical Method because of the non-linear and complex physical conditions affecting the occurrence of rainfall.

scholarly journals A simulation of the rainfall-runoff process using artificial neural network and HEC-HMS model in forest lands

2021 ◽  
Vol 67 (No. 4) ◽  
pp. 165-174
Author(s):  
Vahid Gholami ◽  
Mohammad Reza Khaleghi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Rainfall Runoff ◽  
Runoff Generation ◽  
Soil Infiltration ◽  
Hydrologic Modelling ◽  
Moisture Condition ◽  
Simulation Performance ◽  
Artificial Neural ◽  
Forest Lands

Simulation of the runoff-rainfall process in forest lands is essential for forest land management. In this research, a hydrologic modelling system (HEC-HMS) and artificial neural network (ANN) were applied to simulate the rainfall-runoff process (RRP) in forest lands of Kasilian watershed with an area of 68 square kilometres. The HMS model was performed using the secondary data of rainfall and discharge at the climatology and hydrometric stations, the Soil Conservation Service (SCS) for simulating a flow hydrograph, the curve number (CN) method for runoff estimation, and lag time method for flow routing. Further, a multilayer perceptron (MLP) network was used for simulating the rainfall-runoff process. HEC-HMS model was used to optimize the initial loss (IL) values in the rainfall-runoff process as an input. IL reflects the conditions of vegetation, soil infiltration, and antecedent moisture condition (AMC) in soil. Then, IL values and also incremental rainfall were applied as inputs into ANN to simulate the runoff values. The comparison of the results of simulating the RRP in two scenarios, using IL and without IL, showed that the IL parameter has a high effect in increasing the simulation performance of the rainfall-runoff process. Moreover, ANN predictions were more precise in comparison with those of the HMS model. Further, forest lands can significantly increase IL values and decrease runoff generation.

Monthly Inflow Forecasting using Autoregressive Artificial Neural Network

2012 ◽  
Vol 12 (20) ◽  
pp. 2139-2147 ◽  
Author(s):  
M. Valipour ◽  
M.E. Banihabib ◽  
S.M.R. Behbahani
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Inflow Forecasting ◽  
Artificial Neural

scholarly journals IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORK FOR MONTHLY RAIN ATTACKS IN SOUTH KALIMANTAN

2019 ◽  
Vol 4 (1) ◽  
pp. 35-42
Author(s):  
Nur Arminarahmah ◽  
Miftahul Munir
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Numerical Weather Prediction ◽  
Empirical Orthogonal Function ◽  
Weather Prediction ◽  
Numerical Weather ◽  
Orthogonal Function ◽  
Artificial Neural

Prakiraan hujan bulanan bisa digunakan untuk antisipasi banjir dan manajemen sumber daya air, keselamatan jiwa dan harta benda, serta keberlangsungan aktivitas ekonomi. Penggunaan Jaringan Syaraf Tiruan sebagai bagian dari Machine Learning adalah teknik yang sering digunakan selain numerical weather prediction dan metode statistik. Menggunakan peru-bah data bulan dan data empirical orthogonal function anomali suhu muka laut bulanan pada 12 lokasi menghasilkan nilai korelasi yang baik saat pembuatan model, tetapi hasil verifikasi menunjukkan akurasi yang baik didapatkan saat periode musim kemarau dan skill terjelek saat peralihan musim kemarau ke musim hujan.

scholarly journals Estimation of Daily Potential Evapotranspiration in Real-Time from GK2A/AMI Data Using Artificial Neural Network for the Korean Peninsula

Hydrology ◽  
2021 ◽  
Vol 8 (3) ◽  
pp. 129
Author(s):  
Jae-Cheol Jang ◽  
Eun-Ha Sohn ◽  
Ki-Hong Park ◽  
Soobong Lee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Real Time ◽  
Korean Peninsula ◽  
Potential Evapotranspiration ◽  
Weather Prediction ◽  
Spatiotemporal Variability ◽  
Artificial Neural ◽  
Using Data

Evapotranspiration (ET) is a fundamental factor in energy and hydrologic cycles. Although highly precise in-situ ET monitoring is possible, such data are not always available due to the high spatiotemporal variability in ET. This study estimates daily potential ET (PET) in real-time for the Korean Peninsula, via an artificial neural network (ANN), using data from the GEO-KOMPSAT 2A satellite, which is equipped with an Advanced Meteorological Imager (GK2A/AMI). We also used passive microwave data, numerical weather prediction (NWP) model data, and static data. The ANN-based PET model was trained using data for the period 25 July 2019 to 24 July 2020, and was tested by comparing with in-situ PET for the period 25 July 2020 to 31 July 2021. In terms of accuracy, the PET model performed well, with root-mean-square error (RMSE), bias, and Pearson’s correlation coefficient (R) of 0.649 mm day−1, −0.134 mm day−1, and 0.954, respectively. To examine the efficiency of the GK2A/AMI-derived PET data, we compared it with in-situ ET measured at flux towers and with MODIS PET data. The accuracy of the GK2A/AMI-derived PET, in comparison with the flux tower-measured ET, showed RMSE, bias, and Pearson’s R of 1.730 mm day−1, 1.212 mm day−1, and 0.809, respectively. In comparison with the in-situ PET, the ANN model produced more accurate estimates than the MODIS data, indicating that it is more locally optimized for the Korean Peninsula than MODIS. This study advances the field by applying an ANN approach using GK2A/AMI data and could play an important role in examining hydrologic energy for air-land interactions.

A Novel Soil Moisture Predicting Method Based on Artificial Neural Network and Xinanjiang Model

2010 ◽  
Vol 121-122 ◽  
pp. 1028-1032 ◽  
Author(s):  
Jing Wen Xu ◽  
Jun Fang Zhao ◽  
Wan Chang Zhang ◽  
Xiao Xun Xu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soil Moisture ◽  
Prediction Models ◽  
Weather Prediction ◽  
Eastern China ◽  
Agricultural Drought ◽  
Xinanjiang Model ◽  
Predicting Model ◽  
Artificial Neural

Soil moisture plays an important role in agricultural drought predicting, therefore there is an increasing demand for detailed predictions of soil moisture, especially at basin scales. However, so far soil moisture predictions are usually obtained as a by-product of climate and weather prediction models coupled with a land surface parameterization scheme, and there has been little dedicated work to meet this urgent need at basin scales. In order to improve the basin hydrological models’ performance in the soil moisture forecasting, an integrated soil moisture predicting model based on Artificial Neural Network (ANN) and Xinanjiang model is proposed and presented in this paper. The performance of the new integrated soil moisture predicting model was tested in the Linyi watershed with a drainage area of 10040 km2, located in the semi-arid area of the eastern China. The results suggest that the soil moisture simulated by the integrated ANN-Xinanjiang model is more agree with the observed ones than that simulated by Xinanjiang, and that the simulated soil moisture by both the models has the similar trend and temporal change pattern with the observed one.

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