Modeling the Changes in Water Balance Components of Highly Irrigated Western Part of Bangladesh

Abstract. The objectives of the study are to explore the changes in water balance components (WBC) by co-utilizing discrete wavelet transformation (DWT) and different forms of Mann–Kendal (MK) test; and to develop wavelet autoregressive moving average (ARIMA) models for forecasting the WBC. Trend test results reveal that the most of the trends (about 73 %) identified in potential evapotranspiration (PET) show decreasing tendency during the hydrological year 1981–82 to 2012–13 in the western part of Bangladesh, however most of the changes (about 82 %) are insignificant at 5 % significant level. Actual evapotranspiration (AET), annual deficit and annual surplus also show the almost similar tendency. Rainfall and temperature show increasing trends, but WBC show inverse of this tendency and suggesting that traditional concept of changes in PET associated with changes in temperature cannot explain the changes in WBC. Moreover, it is found that generally 8-years (D3) to 16-years (D4) periodic components are the effective components and are responsible for trends found in original data of WBC in western part of Bangladesh. Wavelet denoising of WBC time series has been done to improve the performance of models as actual data affected by noise and show unsatisfactory performances. The quality of denoised data has been ensured by relevant statistical analysis. Performance of wavelet ARIMA models have been assessed by Nash–Sutcliffe Efficiency (NSE) coefficient and coefficient of determination (R2). The obtained results indicate that performances of wavelet ARIMA models of WBC are acceptable to very good and clearly demonstrate the advantages of denoising over actual data. The models validation results reveal that the forecasted values are very close to actual values with acceptable mean percentage error and residuals also follow normally distribution. Performances and validation results indicate that models can be used for short term forecasting of WBC. Further studies on different combinations of wavelet analysis would be facilitated to develop better models for WBC in context of climate change and findings of study can be used to improve water resources management in highly irrigated western part of Bangladesh.

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Modeling the changes in water balance components of the highly irrigated western part of Bangladesh

Hydrology and Earth System Sciences ◽

10.5194/hess-22-4213-2018 ◽

2018 ◽

Vol 22 (8) ◽

pp. 4213-4228 ◽

Cited By ~ 6

Author(s):

A. T. M. Sakiur Rahman ◽

M. Shakil Ahmed ◽

Hasnat Mohammad Adnan ◽

Mohammad Kamruzzaman ◽

M. Abdul Khalek ◽

...

Keyword(s):

Time Series ◽

Water Balance ◽

Water Resource Management ◽

Time Series Data ◽

Arima Model ◽

Series Data ◽

Discrete Wavelet ◽

Water Balance Components ◽

Mk Test ◽

Periodic Components

Abstract. The objectives of the present study were to explore the changes in the water balance components (WBCs) by co-utilizing the discrete wavelet transform (DWT) and different forms of the Mann–Kendall (MK) test and develop a wavelet denoise autoregressive integrated moving average (WD-ARIMA) model for forecasting the WBCs. The results revealed that most of the potential evapotranspiration (PET) trends (approximately 73 %) had a decreasing tendency from 1981–1982 to 2012–2013 in the western part of Bangladesh. However, most of the trends (approximately 82 %) were not statistically significant at a 5 % significance level. The actual evapotranspiration (AET), annual deficit, and annual surplus also exhibited a similar tendency. The rainfall and temperature exhibited increasing trends. However, the WBCs exhibited an inverse trend, which suggested that the PET changes associated with temperature changes could not explain the change in the WBCs. Moreover, the 8-year (D3) and 16-year (D4) periodic components were generally responsible for the trends found in the original WBC data for the study area. The actual data was affected by noise, which resulted in the ARIMA model exhibiting an unsatisfactory performance. Therefore, wavelet denoising of the WBC time series was conducted to improve the performance of the ARIMA model. The quality of the denoising time series data was ensured using relevant statistical analysis. The performance of the WD-ARIMA model was assessed using the Nash–Sutcliffe efficiency (NSE) coefficient and coefficient of determination (R2). The WD-ARIMA model exhibited very good performance, which clearly demonstrated the advantages of denoising the time series data for forecasting the WBCs. The validation results of the model revealed that the forecasted values were very close to actual values, with an acceptable mean percentage error. The residuals also followed a normal distribution. The performance and validation results indicated that models can be used for the short-term forecasting of WBCs. Further studies on different combinations of wavelet analysis are required to develop a superior model for the hydrological forecasting in the context of climate change. The findings of this study can be used to improve water resource management in the highly irrigated western part of Bangladesh.

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A Catchment Scale Assessment of Water Balance Components: A Case Study of Chittar Catchment in South India

10.21203/rs.3.rs-954410/v1 ◽

2021 ◽

Author(s):

Dinagarapandi Pandi ◽

Saravanan Kothandaraman ◽

K S Kasiviswanathan ◽

Mohan Kuppusamy

Keyword(s):

Water Balance ◽

Assessment Tool ◽

Moving Average ◽

Swat Model ◽

Meteorological Variables ◽

Coefficient Of Determination ◽

Time Interval ◽

Catchment Scale ◽

Water Balance Components ◽

Management And Development

Abstract Analyzing the Water Balance Components (WBCs) of catchment help in assessing the water resources for their sustainable management and development. This paper used Soil and Water Assessment Tool (SWAT) model mainly to analyze the variation in the WBCs through the change in the Land Use and Land Cover (LULC) and meteorological variables. For this purpose, the model used the inputs of LULC and meteorological variables between the year 2001-2020 at five year and daily time interval respectively from the Chittar river catchment. The developed models were evaluated using SWAT-CUP split-up procedure (pre-calibration and post-calibration). The model was found to be good in calibration and validation, yielding the coefficient of determination (R2) of 0.94 and 0.81 respectively. Furthermore, WBCs of the catchment were estimated for the near future (2021 - 2030) at monthly and annual scale. For this endeavour, LULC was forecasted for the year 2021 and 2026 using Celluar Automata (CA)-ANN and for the same period meteorological variables were also forecasted using the smoothing moving average method from the historical data.

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Evaluation of the CRU TS3.1, APHRODITE_V1101, and CFSR Datasets in Assessing Water Balance Components in the Upper Vakhsh River Basin in Central Asia

Atmosphere ◽

10.3390/atmos12101334 ◽

2021 ◽

Vol 12 (10) ◽

pp. 1334

Author(s):

Aminjon Gulakhmadov ◽

Xi Chen ◽

Manuchekhr Gulakhmadov ◽

Zainalobudin Kobuliev ◽

Nekruz Gulahmadov ◽

...

Keyword(s):

Water Balance ◽

Central Asia ◽

Observational Data ◽

River Basin ◽

Hydrological Modeling ◽

Swat Model ◽

Coefficient Of Determination ◽

Climatic Research Unit ◽

Water Balance Components ◽

Monthly Streamflow

In this study, the applicability of three gridded datasets was evaluated (Climatic Research Unit (CRU) Time Series (TS) 3.1, “Asian Precipitation—Highly Resolved Observational Data Integration Toward the Evaluation of Water Resources” (APHRODITE)_V1101, and the climate forecast system reanalysis dataset (CFSR)) in different combinations against observational data for predicting the hydrology of the Upper Vakhsh River Basin (UVRB) in Central Asia. Water balance components were computed, the results calibrated with the SUFI-2 approach using the calibration of soil and water assessment tool models (SWAT–CUP) program, and the performance of the model was evaluated. Streamflow simulation using the SWAT model in the UVRB was more sensitive to five parameters (ALPHA_BF, SOL_BD, CN2, CH_K2, and RCHRG_DP). The simulation for calibration, validation, and overall scales showed an acceptable correlation between the observed and simulated monthly streamflow for all combination datasets. The coefficient of determination (R2) and Nash–Sutcliffe efficiency (NSE) showed “excellent” and “good” values for all datasets. Based on the R2 and NSE from the “excellent” down to “good” datasets, the values were 0.91 and 0.92 using the observational datasets, CRU TS3.1 (0.90 and 0.90), APHRODITE_V1101+CRU TS3.1 (0.74 and 0.76), APHRODITE_V1101+CFSR (0.72 and 0.78), and CFSR (0.67 and 0.74) for the overall scale (1982–2006). The mean annual evapotranspiration values from the UVRB were about 9.93% (APHRODITE_V1101+CFSR), 25.52% (APHRODITE_V1101+CRU TS3.1), 2.9% (CFSR), 21.08% (CRU TS3.1), and 27.28% (observational datasets) of annual precipitation (186.3 mm, 315.7 mm, 72.1 mm, 256.4 mm, and 299.7 mm, out of 1875.9 mm, 1236.9 mm, 2479 mm, 1215.9 mm, and 1098.5 mm). The contributions of the snowmelt to annual runoff were about 81.06% (APHRODITE_V1101+CFSR), 63.12% (APHRODITE_V1101+CRU TS3.1), 82.79% (CFSR), 81.66% (CRU TS3.1), and 67.67% (observational datasets), and the contributions of rain to the annual flow were about 18.94%, 36.88%, 17.21%, 18.34%, and 32.33%, respectively, for the overall scale. We found that gridded climate datasets can be used as an alternative source for hydrological modeling in the Upper Vakhsh River Basin in Central Asia, especially in scarce-observation regions. Water balance components, simulated by the SWAT model, provided a baseline understanding of the hydrological processes through which water management issues can be dealt with in the basin.

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Predicting Ink Transfer Rate of 3D Additive Printing Using EGBO Optimized Least Squares Support Vector Machine Model

Mathematical Problems in Engineering ◽

10.1155/2020/8642430 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Shengpu Li ◽

Yize Sun

Keyword(s):

Support Vector Machine ◽

Prediction Model ◽

Least Squares ◽

Transfer Rate ◽

Coefficient Of Determination ◽

Percentage Error ◽

Support Vector ◽

Experimental Sample ◽

Machine Model ◽

Ink Transfer

Ink transfer rate (ITR) is a reference index to measure the quality of 3D additive printing. In this study, an ink transfer rate prediction model is proposed by applying the least squares support vector machine (LSSVM). In addition, enhanced garden balsam optimization (EGBO) is used for selection and optimization of hyperparameters that are embedded in the LSSVM model. 102 sets of experimental sample data have been collected from the production line to train and test the hybrid prediction model. Experimental results show that the coefficient of determination (R2) for the introduced model is equal to 0.8476, the root-mean-square error (RMSE) is 6.6 × 10 (−3), and the mean absolute percentage error (MAPE) is 1.6502 × 10 (−3) for the ink transfer rate of 3D additive printing.

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How to improve infectious disease prediction by integrating environmental data: an application of a novel ensemble analysis strategy to predict HFMD

Epidemiology and Infection ◽

10.1017/s0950268821000091 ◽

2021 ◽

Vol 149 ◽

Author(s):

Junwen Tao ◽

Yue Ma ◽

Xuefei Zhuang ◽

Qiang Lv ◽

Yaqiong Liu ◽

...

Keyword(s):

Variable Selection ◽

Mean Squared Error ◽

Moving Average ◽

Dynamic Bayesian Networks ◽

Environmental Data ◽

Control Measures ◽

Coefficient Of Determination ◽

Percentage Error ◽

Analysis Strategy ◽

Ensemble Analysis

Abstract This study proposed a novel ensemble analysis strategy to improve hand, foot and mouth disease (HFMD) prediction by integrating environmental data. The approach began by establishing a vector autoregressive model (VAR). Then, a dynamic Bayesian networks (DBN) model was used for variable selection of environmental factors. Finally, a VAR model with constraints (CVAR) was established for predicting the incidence of HFMD in Chengdu city from 2011 to 2017. DBN showed that temperature was related to HFMD at lags 1 and 2. Humidity, wind speed, sunshine, PM10, SO2 and NO2 were related to HFMD at lag 2. Compared with the autoregressive integrated moving average model with external variables (ARIMAX), the CVAR model had a higher coefficient of determination (R2, average difference: + 2.11%; t = 6.2051, P = 0.0003 < 0.05), a lower root mean-squared error (−24.88%; t = −5.2898, P = 0.0007 < 0.05) and a lower mean absolute percentage error (−16.69%; t = −4.3647, P = 0.0024 < 0.05). The accuracy of predicting the time-series shape was 88.16% for the CVAR model and 86.41% for ARIMAX. The CVAR model performed better in terms of variable selection, model interpretation and prediction. Therefore, it could be used by health authorities to identify potential HFMD outbreaks and develop disease control measures.

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Modeling of Lake Malombe Annual Fish Landings and Catch per Unit Effort (CPUE)

Forecasting ◽

10.3390/forecast3010004 ◽

2021 ◽

Vol 3 (1) ◽

pp. 39-55

Author(s):

Rodgers Makwinja ◽

Seyoum Mengistou ◽

Emmanuel Kaunda ◽

Tena Alemiew ◽

Titus Bandulo Phiri ◽

...

Keyword(s):

Time Series ◽

Information Criterion ◽

Coefficient Of Determination ◽

Series Data ◽

Efficiency Coefficient ◽

Arima Models ◽

Catch Per Unit Effort ◽

Annual Fish ◽

Unit Effort ◽

The Mean

Forecasting, using time series data, has become the most relevant and effective tool for fisheries stock assessment. Autoregressive integrated moving average (ARIMA) modeling has been commonly used to predict the general trend for fish landings with increased reliability and precision. In this paper, ARIMA models were applied to predict Lake Malombe annual fish landings and catch per unit effort (CPUE). The annual fish landings and CPUE trends were first observed and both were non-stationary. The first-order differencing was applied to transform the non-stationary data into stationary. Autocorrelation functions (AC), partial autocorrelation function (PAC), Akaike information criterion (AIC), Bayesian information criterion (BIC), square root of the mean square error (RMSE), the mean absolute error (MAE), percentage standard error of prediction (SEP), average relative variance (ARV), Gaussian maximum likelihood estimation (GMLE) algorithm, efficiency coefficient (E2), coefficient of determination (R2), and persistent index (PI) were estimated, which led to the identification and construction of ARIMA models, suitable in explaining the time series and forecasting. According to the measures of forecasting accuracy, the best forecasting models for fish landings and CPUE were ARIMA (0,1,1) and ARIMA (0,1,0). These models had the lowest values AIC, BIC, RMSE, MAE, SEP, ARV. The models further displayed the highest values of GMLE, PI, R2, and E2. The “auto. arima ()” command in R version 3.6.3 further displayed ARIMA (0,1,1) and ARIMA (0,1,0) as the best. The selected models satisfactorily forecasted the fish landings of 2725.243 metric tons and CPUE of 0.097 kg/h by 2024.

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Machine Learning for Sensorless Temperature Estimation of a BLDC Motor

Sensors ◽

10.3390/s21144655 ◽

2021 ◽

Vol 21 (14) ◽

pp. 4655

Author(s):

Dariusz Czerwinski ◽

Jakub Gęca ◽

Krzysztof Kolano

Keyword(s):

Machine Learning ◽

Temperature Measurement ◽

Stochastic Gradient Descent ◽

Estimation Accuracy ◽

Coefficient Of Determination ◽

Percentage Error ◽

Support Vector ◽

Bldc Motor ◽

Temperature Estimation ◽

Motor Operation

In this article, the authors propose two models for BLDC motor winding temperature estimation using machine learning methods. For the purposes of the research, measurements were made for over 160 h of motor operation, and then, they were preprocessed. The algorithms of linear regression, ElasticNet, stochastic gradient descent regressor, support vector machines, decision trees, and AdaBoost were used for predictive modeling. The ability of the models to generalize was achieved by hyperparameter tuning with the use of cross-validation. The conducted research led to promising results of the winding temperature estimation accuracy. In the case of sensorless temperature prediction (model 1), the mean absolute percentage error MAPE was below 4.5% and the coefficient of determination R2 was above 0.909. In addition, the extension of the model with the temperature measurement on the casing (model 2) allowed reducing the error value to about 1% and increasing R2 to 0.990. The results obtained for the first proposed model show that the overheating protection of the motor can be ensured without direct temperature measurement. In addition, the introduction of a simple casing temperature measurement system allows for an estimation with accuracy suitable for compensating the motor output torque changes related to temperature.

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Afforestation of Degraded Croplands as a Water-Saving Option in Irrigated Region of the Aral Sea Basin

Water ◽

10.3390/w13101433 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1433

Author(s):

Navneet Kumar ◽

Asia Khamzina ◽

Patrick Knöfel ◽

John P. A. Lamers ◽

Bernhard Tischbein

Keyword(s):

Land Use ◽

Water Balance ◽

Water Supply ◽

Water Availability ◽

Irrigation Water ◽

Water Saving ◽

Aral Sea ◽

Water Balance Components ◽

Study Region ◽

Trade Offs

Climate change is likely to decrease surface water availability in Central Asia, thereby necessitating land use adaptations in irrigated regions. The introduction of trees to marginally productive croplands with shallow groundwater was suggested for irrigation water-saving and improving the land’s productivity. Considering the possible trade-offs with water availability in large-scale afforestation, our study predicted the impacts on water balance components in the lower reaches of the Amudarya River to facilitate afforestation planning using the Soil and Water Assessment Tool (SWAT). The land-use scenarios used for modeling analysis considered the afforestation of 62% and 100% of marginally productive croplands under average and low irrigation water supply identified from historical land-use maps. The results indicate a dramatic decrease in the examined water balance components in all afforestation scenarios based largely on the reduced irrigation demand of trees compared to the main crops. Specifically, replacing current crops (mostly cotton) with trees on all marginal land (approximately 663 km2) in the study region with an average water availability would save 1037 mln m3 of gross irrigation input within the study region and lower the annual drainage discharge by 504 mln m3. These effects have a considerable potential to support irrigation water management and enhance drainage functions in adapting to future water supply limitations.

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