scholarly journals Recent variations and trends in pan evaporation over India

MAUSAM ◽  
2021 ◽  
Vol 59 (3) ◽  
pp. 347-356
Author(s):  
I. J. VERMA ◽  
V. N. JADHAV
Keyword(s):  
Time Series Data ◽  
Linear Trend ◽  
Series Data ◽  
Pan Evaporation ◽  
Statistical Parameters ◽  
Class A ◽  
Linear Relationships ◽  
Analysis Technique ◽  
Annual Evaporation ◽  
The Mean

Thirty years pan evaporation time series data (1971-2000) recorded from US class-A evaporation pans for twenty well distributed locations in India, have been utilized in the present study. For all the locations, basic statistical parameters of annual evaporation [minimum, maximum, range, mean, standard deviation (S.D.) and coefficient of variation (C.V.)] have been computed. Annual, seasonal and monthly trends have been studied using linear trend analysis technique. Suitable graphs have been plotted to study the variations and changes in pan evaporation trends and to identify the specific periods as and when significant changes occur.   The mean annual pan evaporation was found to be lowest (1107 mm) at Buralikson and highest (3004 mm) at Rajkot. The highest C.V. of nearly 11% was observed at Rajamundry, Jodhpur, Buralikson and Nellore. The lowest C.V. of nearly 2% was observed at Ambikapur. Out of twenty locations, significant decreasing trend in annual pan evaporation was observed at fifteen locations and no significant trend at five locations. The annual dE/dt values varied from -6.27 (Canning) to -29.30 (Jodhpur) mm/year. The average annual dE/dt over India was found to be -14.90 mm/year. Linear relationship was obtained to quantitatively estimate annual dE/dt, at a given location, using pan evaporation range. On an average, over India, the contribution of seasonal dE/dt towards annual dE/dt (mm/year) is highest -5.63 (37.8 %) in Season-2 (March-April-May) and lowest -2.07 (13.9 %) in Season-1(January- February). On an average, over India, the contribution of monthly dE/dt towards annual dE/dt (mm/year) is highest - 2.08 (14.0 %) in May and lowest -0.77 (5.2 %) in August. Non linear relationships were obtained (a) to quantitatively estimate the average monthly dE/dt values over India, in any particular month (b) to quantitatively estimate the average cumulative dE/dt values over India (mm/year) upto any particular month and (c) to quantitatively estimate the contribution (per cent) towards average annual dE/dt over India, upto any particular month.

scholarly journals Recent variations and trends in potential evapotranspiration (PET) over India

MAUSAM ◽  
2021 ◽  
Vol 59 (1) ◽  
pp. 119-128
Author(s):  
I. J. VERMA ◽  
V. N. JADHAV ◽  
ERANDE R. S.
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Potential Evapotranspiration ◽  
Linear Trend ◽  
Series Data ◽  
Linear Relationships ◽  
Analysis Technique ◽  
Linear Trend Analysis ◽  
The Mean ◽  
Monteith Equation

Thirty years meteorological time series data (1971-2000), for twenty two well distributed locations in India, have been utilized to compute potential evapotranspiration using FAO recommended Penman-Monteith equation. Annual, seasonal and monthly PET trends have been studied using linear trend analysis technique. Suitable graphs have been plotted to study the variations and changes in PET trends and to identify the specific periods as and when significant changes occur.                 The mean annual PET has been found to be lowest (1100 mm) at Buralikson and highest (2109 mm) at Bellary. Out of twenty two locations, significant decreasing trend in annual PET has been observed at seventeen locations and no significant trend at five locations. The mean annual dEo/dt over India has been found to be -9.36 mm/year. Linear relationship has been obtained to quantitatively estimate annual dEo/dt, at a given location, using annual PET range. Non linear relationships have been obtained (a) to quantitatively estimate the mean monthly dEo/dt values over India, (b) to quantitatively estimate the average cumulative dEo/dt values over India (mm/year) up to any particular month and (c) to quantitatively estimate the contribution (percent) towards average annual dEo/dt over India, up to any particular month.

scholarly journals A study on recent changes in weekly evaporation at selected locations in India

MAUSAM ◽  
2021 ◽  
Vol 59 (2) ◽  
pp. 211-218
Author(s):  
I. J. VERMA ◽  
H. P. DAS ◽  
V. N. JADHAV
Keyword(s):  
Time Series ◽  
Coefficient Of Variation ◽  
Time Series Data ◽  
Linear Trend ◽  
Series Data ◽  
Evaporation Time ◽  
Statistical Parameters ◽  
Analysis Technique ◽  
Minimum Number ◽  
Almost All

Thirty years evaporation time series data (1971-2000) recorded from US class-A evaporation pans for ten well distributed locations in India, have been utilized in the present study. For these locations, basic statistical parameters of weekly evaporation [minimum, maximum, range, mean, standard deviation (S.D.) and coefficient of variation (C.V.)] have been computed. Variations in average weekly evaporation in different weeks and at different locations have been plotted and discussed. Changes in weekly evaporation have been studied using linear trend analysis technique on weekly evaporation time series data for standard meteorological weeks (1 to 52). Graphs have been plotted, for all ten different locations, to study week wise distribution of changes in weekly evaporation trends and to identify the specific periods when significant changes occur.   The highest average weekly evaporation of 107.5 mm has been observed at Jodhpur in standard week                    21(21 – 27 May). The lowest average weekly evaporation of 14.5 mm has been observed at Karimganj in standard week 3 (15 – 21 January). The peak in average weekly evaporation, at most of these locations is achieved around standard week   20 (14 – 20 May). The coefficient of variation (C.V.) at these locations varied between 18.7 and 51.8 percent. The highest C.V. of 51.8 % has been observed at Bikramganj, whereas the lowest C.V. of 18.7 % has been observed at Rajamundry. Out of 52 weeks, Pune and Rajamundry have shown significant decreasing trend in weekly evaporation in maximum number of weeks (37) and Bhubaneshwar has shown significant decreasing trend in weekly evaporation in minimum number of weeks (10). At six locations (Bikramganj, Hissar, Jodhpur, Pattambi, Pune and Rajamundry), the number of weeks showing significant decreasing trend in weekly evaporation have been found to be more than 23 weeks. At more than five locations significant decreasing trend in weekly evaporation occur, in almost all weeks, between standard weeks 1 to 19 (1 January - 13 May) and also between standard weeks 40 to 52 (1 October - 31 December). In almost all the locations, significant decreasing trend in weekly evaporation occur, in standard week numbers 1-2, 9-10, 13 and 15.

ANALISA PREDIKSI INDEKS HARGA SAHAM GABUNGAN MENGGUNAKAN METODE ARIMA

2020 ◽  
Vol 2 (2) ◽  
pp. 454
Author(s):  
Julkifli Purnama ◽  
Ahmad Juliana
Keyword(s):  
Capital Market ◽  
Stock Price ◽  
Time Series Data ◽  
Arima Model ◽  
Secondary Data ◽  
Actual Data ◽  
Series Data ◽  
Analysis Technique ◽  
The Right ◽  
The Capital Market

Investment in the capital market every manager needs to analyze to make decisions so that the right target to produce profits in accordance with what is expected. For that, we need a way to predict the decisions that will be taken in the future. The research objective is to find the best model and forecasting of the composite stock price index (CSPI). Data analysis technique The ARIMA Model time series data from historical data is the basis for forecasting. Secondary data is the closing price of the JCI on July 16 2018 to July 16 2019 to see how accurate the forecasting is done on the actual data at that time. The results of the study that the best Arima model is Arima 2.1.2 with an R-squared value of 0.014500, Schwarz criterion 10.83497 and Akaike info criterion of 10.77973. Results of forecasting actual data are 6394,609, dynamic forecast 6387,551 selisish -7,05799, statistics forecas 6400,653 difference of 6,043909. For investors or the public can use the ARIMA method to be able to predict or predict the capital market that will occur in the next period.

scholarly journals Distance variable improvement of time-series big data stream evaluation

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Ari Wibisono ◽  
Petrus Mursanto ◽  
Jihan Adibah ◽  
Wendy D. W. T. Bayu ◽  
May Iffah Rizki ◽  
...  
Keyword(s):  
Time Series ◽  
Standard Deviation ◽  
Standard Method ◽  
Time Series Data ◽  
Series Data ◽  
Percentage Error ◽  
Traffic Demand ◽  
Incremental Model ◽  
Chernoff Bound ◽  
The Mean

Abstract Real-time information mining of a big dataset consisting of time series data is a very challenging task. For this purpose, we propose using the mean distance and the standard deviation to enhance the accuracy of the existing fast incremental model tree with the drift detection (FIMT-DD) algorithm. The standard FIMT-DD algorithm uses the Hoeffding bound as its splitting criterion. We propose the further use of the mean distance and standard deviation, which are used to split a tree more accurately than the standard method. We verify our proposed method using the large Traffic Demand Dataset, which consists of 4,000,000 instances; Tennet’s big wind power plant dataset, which consists of 435,268 instances; and a road weather dataset, which consists of 30,000,000 instances. The results show that our proposed FIMT-DD algorithm improves the accuracy compared to the standard method and Chernoff bound approach. The measured errors demonstrate that our approach results in a lower Mean Absolute Percentage Error (MAPE) in every stage of learning by approximately 2.49% compared with the Chernoff Bound method and 19.65% compared with the standard method.

scholarly journals THE EFFECT OF CAPITAL EXPENDITURE AND ECONOMIC GROWTH ON THE HUMAN DEVELOPMENT INDEX OF THE DISTRICT CITY IN NORTH SUMATERA

2020 ◽  
Vol 7 (1) ◽  
pp. 585-594
Author(s):  
Muammar Rinaldi ◽  
Zainal Arifin ◽  
Indra Maipita ◽  
Saidun Hutasuhut
Keyword(s):  
Economic Growth ◽  
Human Development ◽  
Human Development Index ◽  
Time Series Data ◽  
Capital Expenditure ◽  
Numerical Data ◽  
Series Data ◽  
Development Index ◽  
Analysis Technique ◽  
North Sumatra

This study aims to analyze the effect of capital expenditure and economic growth simultaneously on the Human Development Index (HDI) in districts/cities in North Sumatra. This type of research is a descriptive-quantitative approach that suppresses its analysis of numerical data that is processed by the statistical method. Sources of data in this study were taken from the Central Bureau of Statistics of North Sumatra for the HDI data. The sample in this study is all districts/cities in North Sumatra for the period 2013-2017. The data analysis technique used in this study uses panel data regression with Eviews 7 because, in this study, there are characteristics of cross-section and time-series data simultaneously. The results of this study indicate that capital expenditure partially has a positive and significant effect on the Human Development Index in districts/cities in North Sumatra. Economic growth partially has a positive and significant effect on the HDI in districts/cities in North Sumatra, and capital expenditure and economic growth have a positive and significant effect simultaneously on the Human Development Index in districts/cities in North Sumatra.

Pengaruh Nilai Tukar (Kurs) dan Inflasi terhadap Tingkat  Non Performing  Financing (NPF) Pada Bank Syariah

2019 ◽  
Vol 2 (2) ◽  
pp. 214-224
Author(s):  
Muhammad Arfan Harahap ◽  
Anjur Perkasa Alam ◽  
Muspita Pradila
Keyword(s):  
Exchange Rate ◽  
Time Series Data ◽  
Sampling Technique ◽  
Series Data ◽  
Islamic Banks ◽  
Exogenous Variables ◽  
Independent Variables ◽  
Analysis Technique ◽  
The Exchange Rate ◽  
Conventional Banks

The level of Non-Performing Financing at Islamic Banks is higher than conventional banks, while in terms of total assets, Islamic Banks are smaller. This study aims to analyze the effect of exogenous variables consisting of the level of inflation and the exchange rate of the rupiah against non Performing Financing in Islamic banks. This study uses time series data from 2016-2018. The population in this study consisted of Islamic Commercial Banks. The sampling technique used in this research is purposive sampling technique. The data analysis technique used is Multiple Linear Regression. The results showed that the inflation variable had a positive and significant effect on Non Performing Financing. While the Exchange Rate has no effect on Non Performing Finance. Meanwhile, simultaneously the two independent variables affect the independent variables.

A New Imputation Method for Treating Missing Precipitation Records

2014 ◽  
Vol 635-637 ◽  
pp. 1488-1495
Author(s):  
Yu Liu ◽  
Feng Rui Chen
Keyword(s):  
Time Series Data ◽  
Absolute Error ◽  
Imputation Method ◽  
Temporal Information ◽  
Series Data ◽  
Precipitation Trend ◽  
Monthly Total Precipitation ◽  
The Mean ◽  
Spatio Temporal ◽  
Precipitation Records

This study aims to present a new imputation method for missing precipitation records by fusing its spatio-temporal information. On the basis of extending simple kriging model, a nonstationary kriging method which assumes that the mean or trend is known and varies in whole study area was proposed. It obtains precipitation trend of each station at a given time by analyzing its time series data, and then performs geostatistical analysis on the residual between the trend and measured values. Finally, these spatio-temporal information is integrated into a unified imputation model. This method was illustrated using monthly total precipitation data from 671 meteorological stations of China in April, spanning the period of 2001-2010. Four different methods, including moving average, mean ratio, expectation maximization and ordinary kriging were introduced to compare with. The results show that: Among these methods, the mean absolute error, mean relative error and root mean square error of the proposed method are the smallest, so it produces the best imputation result. That is because: (1) It fully takes into account the spatio-temporal information of precipitation. (2) It assumes that the mean varies in whole study area, which is more in line with the actual situation for rainfall.

scholarly journals Using artificial neural network (ANN) for prediction of sediment loads, application to the Mellah catchment, northeast Algeria

2017 ◽  
Vol 33 (1) ◽  
pp. 47-55 ◽  
Author(s):  
Housseyn Bouzeria ◽  
Abderrahmane N. Ghenim ◽  
Kamel Khanchoul
Keyword(s):  
Suspended Sediment ◽  
Time Series Data ◽  
Water Discharge ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Series Data ◽  
Sediment Discharge ◽  
Statistical Parameters ◽  
Discharge Data ◽  
Sediment Loads

AbstractIn this study, we present the performances of the best training algorithm in Multilayer Perceptron (MLP) neural networks for prediction of suspended sediment discharges in Mellah catchment. Time series data of daily suspended sediment discharge and water discharge from the gauging station of Bouchegouf were used for training and testing the networks. A number of statistical parameters, i.e. root mean square error (RMSE), mean absolute error (MAE), coefficient of efficiency (CE) and coefficient of determination (R2) were used for performance evaluation of the model. The model produced satisfactory results and showed a very good agreement between the predicted and observed data. The results also showed that the performance of the MLP model was capable to capture the exact pattern of the sediment discharge data in the Mellah catchment.

scholarly journals Moderation of Non Performing Financing on Natural Uncertainty Contracts To The Profitability of Islamic Commercial Bank In Indonesia

2020 ◽  
Vol 4 (02) ◽  
Author(s):  
Taudlikhul Afkar ◽  
Grahita Chandrarin ◽  
Lilik Pirmaningsih
Keyword(s):  
Structural Equation ◽  
Quantitative Research ◽  
Time Series Data ◽  
Equation Model ◽  
Series Data ◽  
Islamic Banks ◽  
Business Managers ◽  
Quantitative Research Methods ◽  
Research Results ◽  
Analysis Technique

This study intends to provide an overview of the consistency of research results with theoretical and empirical points of view, it is done because many research results are inconsistent with the theory. Quantitative research methods are used to make generalizations using a sample of 14 Islamic Commercial Banks in Indonesia with time series data collection techniques for 5 years. The data analysis technique used is multivariate analysis using the Warp PLS structural equation model. The results showed that the level of profitability of Islamic banks is always overshadowed by the occurrence of credit risk that causes non-performing financing from financing of the type of natural uncertainty contracts because it is type of financing is a financing that does not provide certainty of results. The results of this study are consistent with agency theory that explains the existence of information asymmetry, and consistent with the theory of mixing that by providing opportunities to manage business to business managers (mudharib/mustyarik) without interference from the owner of the fund (shaibul maal) can lead to the risk of default and thus affect the ability of Islamic banks to obtain profitability.

scholarly journals Pengaruh Variabel Makroekonomi Terhadap Jumlah Penghimpunan Zakat di Indonesia

2020 ◽  
Vol 25 (2) ◽  
pp. 199
Author(s):  
Sheema Haseena Armina
Keyword(s):  
Exchange Rate ◽  
Time Series Data ◽  
Series Data ◽  
Short Term ◽  
Correction Model ◽  
Dollar Exchange Rate ◽  
Vector Error Correction ◽  
Vector Error ◽  
Analysis Technique

Purpose this study analyzes the effect of the industrial production index, the dollar exchange rate, inflation and the BI 7DRR on the amount of zakat collection from January 2015 to December 2018to identify the potential of zakat to support alleviation in Indonesia. Methodology/Approach: this study uses a quantitative approach with a Vector Error Correction Model (VECM) data analysis technique with time series data from Januari 2015 t0 December 2018. Findings: The results show that in short term causality, there is an effect between long-term and short-term between zakat as the dependent variable with inflation and the dollar exchange rate. However, there is no short-term causality effect between BI 7-DRR and IPI to the amount of zakat while the long-term causality effect, all independent variables have a significant effect to the dependent variable namely zakat. Implications: The integration of Islamic philanthropic institutions has the potential to channel aid and support to alleviate poverty. This study adds the IPI variable to interpret the GDP variable in analyzing its effect on zakat.

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