scholarly journals Wavelet-ANN versus ANN-Based Model for Hydrometeorological Drought Forecasting

Water ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 998 ◽  
Author(s):  
Md Khan ◽  
Nur Muhammad ◽  
Ahmed El-Shafie
Keyword(s):  
Neural Network ◽  
River Basin ◽  
Water Resource Management ◽  
Correlation Coefficients ◽  
Peninsular Malaysia ◽  
Drought Forecasting ◽  
Level Data ◽  
Water Level Data ◽  
Langat River ◽  
Ann Models

Malaysia is one of the countries that has been experiencing droughts caused by a warming climate. This study considered the Standard Index of Annual Precipitation (SIAP) and Standardized Water Storage Index (SWSI) to represent meteorological and hydrological drought, respectively. The study area is the Langat River Basin, located in the central part of peninsular Malaysia. The analysis was done using rainfall and water level data over 30 years, from 1986 to 2016. Both of the indices were calculated in monthly scale, and two neural network-based models and two wavelet-based artificial neural network (W-ANN) models were developed for monthly droughts. The performance of the SIAP and SWSI models, in terms of the correlation coefficient (R), was 0.899 and 0.968, respectively. The application of a wavelet for preprocessing the raw data in the developed W-ANN models achieved higher correlation coefficients for most of the scenarios. This proves that the created model can predict meteorological and hydrological droughts very close to the observed values. Overall, this study helps us to understand the history of drought conditions over the past 30 years in the Langat River Basin. It further helps us to forecast drought and to assist in water resource management.

Prediction of Water Level Using Time Series, Wavelet and Neural Network Approaches

2022 ◽  
pp. 1077-1097
Author(s):  
Nguyen Quang Dat ◽  
Ngoc Anh Nguyen Thi ◽  
Vijender Kumar Solanki ◽  
Ngo Le An
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Water Level ◽  
Project Work ◽  
Level Data ◽  
Water Level Data ◽  
Ann Models ◽  
Input Variables ◽  
Network Approaches ◽  
Short Time

To control water resources in many domains such as agriculture, flood forecasting, and hydro-electrical dams, forecasting water level needs to predict. In this article, a new computational approach using a data driven model and time series is proposed to calculate the forecast water level in short time. Concretely, wavelet-artificial neural network (WAANN) and time series (TS) are combined together called WAANN-TS that encourages the advantage of each model. For this real time project work, Yen Bai station, Northwest Vietnam was chosen as an experimental case study to apply the proposed model. Input variables into the Wavelet-ANN structure is water level data. Time series and ANN models are built, and their performances are compared. The results indicate the greater accuracy of the proposed models at Hanoi station. The final proposal WAANN−TS for water level forecasting shows good performance with root mean square error (RMSE) from 10−10 to 10−11.

Prediction of Water Level Using Time Series, Wavelet and Neural Network Approaches

2020 ◽  
Vol 10 (3) ◽  
pp. 1-19
Author(s):  
Nguyen Quang Dat ◽  
Ngoc Anh Nguyen Thi ◽  
Vijender Kumar Solanki ◽  
Ngo Le An
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Water Level ◽  
Project Work ◽  
Level Data ◽  
Water Level Data ◽  
Ann Models ◽  
Input Variables ◽  
Network Approaches ◽  
Short Time

To control water resources in many domains such as agriculture, flood forecasting, and hydro-electrical dams, forecasting water level needs to predict. In this article, a new computational approach using a data driven model and time series is proposed to calculate the forecast water level in short time. Concretely, wavelet-artificial neural network (WAANN) and time series (TS) are combined together called WAANN-TS that encourages the advantage of each model. For this real time project work, Yen Bai station, Northwest Vietnam was chosen as an experimental case study to apply the proposed model. Input variables into the Wavelet-ANN structure is water level data. Time series and ANN models are built, and their performances are compared. The results indicate the greater accuracy of the proposed models at Hanoi station. The final proposal WAANN−TS for water level forecasting shows good performance with root mean square error (RMSE) from 10−10 to 10−11.

scholarly journals Modeling of hourly river water temperatures using artificial neural networks

2014 ◽  
Vol 49 (2) ◽  
pp. 144-162 ◽  
Author(s):  
Cindie Hebert ◽  
Daniel Caissie ◽  
Mysore G. Satish ◽  
Nassir El-Jabi
Keyword(s):  
Water Temperature ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Air Temperatures ◽  
Level Data ◽  
Water Level Data ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
The Many

Water temperature is an important component for water quality and biotic conditions in rivers. A good knowledge of river thermal regime is critical for the management of aquatic resources and environmental impact studies. The objective of the present study was to develop a water temperature model as a function of air temperatures, water temperatures and water level data using artificial neural network (ANN) techniques for two thermally different streams. This model was applied on an hourly basis. The results showed that ANN models are an effective modeling tool with overall root-mean-square-error of 0.94 and 1.23 °C, coefficient of determination (R2) of 0.967 and 0.962 and bias of −0.13 and 0.02 °C, for Catamaran Brook and the Little Southwest Miramichi River, respectively. The ANN model performed best in summer and autumn and showed a poorer performance in spring. Results of the present study showed similar or better results to those of deterministic and stochastic models. The present study shows that the predicted hourly water temperatures can also be used to estimate the mean and maximum daily water temperatures. The many advantages of ANN models are their simplicity, low data requirements, their capability of modeling long-term time series as well as having an overall good performance.

scholarly journals Improvement of SVR-Based Drought Forecasting Models using Wavelet Pre-Processing Technique

2018 ◽  
Vol 65 ◽  
pp. 07007 ◽  
Author(s):  
Kit Fai Fung ◽  
Yuk Feng Huang ◽  
Chai Hoon Koo
Keyword(s):  
Support Vector Regression ◽  
River Basin ◽  
Absolute Error ◽  
Processing Technique ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Drought Risk ◽  
Drought Forecasting ◽  
Drought Prediction ◽  
Langat River

Drought is a damaging natural hazard due to the lack of precipitation from the expected amount for a period of time. Mitigations are required to reduced its impact. Due to the difficulty in determining the onset and offset of droughts, accurate drought forecasting approaches are required for drought risk management. Given the growing use of machine learning in the field, Wavelet-Boosting Support Vector Regression (W-BS-SVR) was proposed for drought forecasting at Langat River Basin, Malaysia. Monthly rainfall, mean temperature and evapotranspiration for years 1976 - 2015 were used to compute Standardized Precipitation Evapotranspiration Index (SPEI) in this study, producing SPEI-1, SPEI-3 and SPEI-6. The 1-month lead time SPEIs forecasting capability of W-BS-SVR model was compared with the Support Vector Regression (SVR) and Boosting-Support Vector Regression (BS-SVR) models using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), coefficient of determination (R2) and Adjusted R2. The results demonstrated that W-BS-SVR provides higher accuracy for drought prediction in Langat River Basin.

scholarly journals Drought Forecasting Using SPI and EDI under RCP-8.5 Climate Change Scenarios for Langat River Basin, Malaysia

2016 ◽  
Vol 154 ◽  
pp. 710-717 ◽  
Author(s):  
Yuk Feng Huang ◽  
Jong Tat Ang ◽  
Yong Jie Tiong ◽  
Majid Mirzaei ◽  
Mohd Zaki Mat Amin
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Climate Change Scenarios ◽  
Drought Forecasting ◽  
Langat River ◽  
Rcp 8.5

scholarly journals Drought forecasting using new machine learning methods / Prognozowanie suszy z wykorzystaniem automatycznych samouczących się metod

2013 ◽  
Vol 18 (9) ◽  
pp. 3-12 ◽  
Author(s):  
Anteneh Belayneh ◽  
Jan Adamowski
Keyword(s):  
Machine Learning ◽  
River Basin ◽  
Standardized Precipitation Index ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Lead Times ◽  
Drought Forecasting ◽  
Awash River Basin ◽  
Ann Models ◽  
The Standardized Precipitation Index

Abstract In order to have effective agricultural production the impacts of drought must be mitigated. An important aspect of mitigating the impacts of drought is an effective method of forecasting future drought events. In this study, three methods of forecasting short-term drought for short lead times are explored in the Awash River Basin of Ethiopia. The Standardized Precipitation Index (SPI) was the drought index chosen to represent drought in the basin. The following machine learning techniques were explored in this study: artificial neural networks (ANNs), support vector regression (SVR), and coupled wavelet-ANNs, which pre-process input data using wavelet analysis (WA). The forecast results of all three methods were compared using two performance measures (RMSE and R2). The forecast results of this study indicate that the coupled wavelet neural network (WA-ANN) models were the most accurate models for forecasting SPI 3 (3-month SPI) and SPI 6 (6-month SPI) values over lead times of 1 and 3 months in the Awash River Basin in Ethiopia.

POTENSI SUMBERDAYA AIR DAS SERAYU

2018 ◽  
Vol 6 (3) ◽  
Author(s):  
Setyawan Purnama
Keyword(s):  
Water Balance ◽  
River Basin ◽  
Central Java ◽  
Level Data ◽  
Water Level Data ◽  
Average Rainfall ◽  
Method Base ◽  
Result Show ◽  
Hydrogeological Map ◽  
Central Java Province

Serayu River Basin is one of critical river basin in Java. This problems become seriously because the river basin cover wide area, that almost cover most of the south of Central Java Province. This research is aimed to calculate and analyse the resources potency of rainfall, river, groundwater and springs. Rainfall potency is calculated by isohyetal method, river potency is determined base onhydograph dan water level data. Groundwater and springs is determined qualitatifly base on Hydrogeological Map, whereas quantitatifly is calculated by meteorogical method base on water balance concept in river basin. As a result, show that the average rainfall in Serayu River Basin is 3,577 m3/year, whereasthe rainfall potency is 13,481.00 x 106 m3/year. The average of monthly maximum discharge in Serayu is 60 m3/second, with monthly minimum discharge 11 m3/second. Meteorologically, groundwater storage in Serayu River Basin is 105,981,890 m3/year. Beside that, in Serayu River Basin also have 93 springs, that is 10 springs with discharge more than 500 litre/second, 15 springs with discharge 50-100 litre/second, 20 springs with discharge 10-50 litre/second and 24 springs with discharge less than 10 litre/second.Keywords : Water resources potency, Serayu River Basin

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