Improved Gridded Precipitation Data Derived from Microwave Link Attenuation

The motivation for improving gridded precipitation data lies in weather now-casting and flood forecasting. Therefore, over the past decade, Commercial Microwave Link (CML) attenuation data have been used to determine rain rates between microwave antennas, and to produce more accurate countrywide precipitation grids. CML networks offer a unique advantage for precipitation measurements due to their high density. However, these data experience uncertainty from several sources as reported in earlier research. This current work determines the reliability of rainfall measurements for each link by comparing CML-derived rain rates to adjusted weather radar rainfall at the link location, over three months. Dynamic Time Warping (DTW) is applied to the pair of CML/radar time-series data in two study areas, Israel and Netherlands. Based on the DTW amplitude and temporal distance, unreliable links are identified and flagged, and interpolated gridded precipitation data are derived in each country after filtering out those unreliable links. Correlations between CML-derived grids and rain observations from an independent set of gauges, tested over several rain events in both study areas, are higher for the reliable subset of CML than the full set. For certain storm events, the Kendall rank correlation for the set of reliable CML is almost double that of the complete set, demonstrating that improved gridded precipitation data can be obtained by removing unreliable links.

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Estimating spatial distribution of aridity and moisture indices of Mongolia using remotely sensed time series data

Proceedings of the Mongolian Academy of Sciences ◽

10.5564/pmas.v61i01.1558 ◽

2021 ◽

pp. 20-29

Author(s):

Munkhdulam Otgonbayar ◽

Erdenesukh Sumiya ◽

Renchinmyadag Tovuudorj

Keyword(s):

Spatial Distribution ◽

Time Series Data ◽

Aridity Index ◽

Meteorological Station ◽

Series Data ◽

Gobi Desert ◽

Precipitation Data ◽

Moisture Index ◽

Temperature And Precipitation ◽

Dry Steppe

The aim of this study is to estimate the spatial distribution of aridity and moisture indices using remotely sensed time-series data. For the analysis, we have chosen two simple climatic indices. One of two indices was the De Martonne aridity index, and the other one was the Mezentsev moisture index. The study area covers the total territory of Mongolia (~1.566 × 106 km2). Both indices could be estimated from meteorological station-based air temperature and precipitation. However, meteorological station-based recorded precipitation and temperature data with long coverage are only available from a limited number of stations with insufficient spatial coverage. In other words, these datasets suffer from uneven geographic coverage, with many areas of the Earth poorly represented. In this study, therefore, we have used satellite-derived temperature and precipitation data. Monthly mean air temperature has been estimated from MODIS LSTd, LSTn, and elevation using RF regression. Precipitation data has been extracted from Climate Hazards Group Infra-Red Precipitation with Station (CHIRPS) datasets. CHIRPS is gauge-satellite combined precipitation data. Based on De Martonne and Mezensev formulas, and satellite-derived meteorological data, spatial distribution maps of aridity and moisture indices in Mongolia were generated. The study result showed that aridity was observed in all areas of southern Mongolia, and some areas in the west, and grassland areas in the east, which is largely included in the dry steppe, desert-steppe, and gobi desert zones. Moisture was observed in the forest and forest-steppe areas in north, central, northeastern, and eastern Mongolia. A comparison of the aridity index and moisture index shows the following: of the total territory, aridity is 31.9:31.4 percent, humidity 14.7:13.4 percent respectively. Dry steppe, desert steppe, and Gobi desert zones are extremely sensitive to water resource variability and availability.

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Forecasting Groundwater Table in a Flood Prone Coastal City with Long Short-term Memory and Recurrent Neural Networks

Water ◽

10.3390/w11051098 ◽

2019 ◽

Vol 11 (5) ◽

pp. 1098 ◽

Cited By ~ 15

Author(s):

Benjamin D. Bowes ◽

Jeffrey M. Sadler ◽

Mohamed M. Morsy ◽

Madhur Behl ◽

Jonathan L. Goodall

Keyword(s):

Time Series ◽

Real Time ◽

Sea Level ◽

Continuous Time ◽

Time Series Data ◽

Short Term Memory ◽

Groundwater Table ◽

Series Data ◽

Storm Events ◽

Coastal City

Many coastal cities are facing frequent flooding from storm events that are made worse by sea level rise and climate change. The groundwater table level in these low relief coastal cities is an important, but often overlooked, factor in the recurrent flooding these locations face. Infiltration of stormwater and water intrusion due to tidal forcing can cause already shallow groundwater tables to quickly rise toward the land surface. This decreases available storage which increases runoff, stormwater system loads, and flooding. Groundwater table forecasts, which could help inform the modeling and management of coastal flooding, are generally unavailable. This study explores two machine learning models, Long Short-term Memory (LSTM) networks and Recurrent Neural Networks (RNN), to model and forecast groundwater table response to storm events in the flood prone coastal city of Norfolk, Virginia. To determine the effect of training data type on model accuracy, two types of datasets (i) the continuous time series and (ii) a dataset of only storm events, created from observed groundwater table, rainfall, and sea level data from 2010–2018 are used to train and test the models. Additionally, a real-time groundwater table forecasting scenario was carried out to compare the models’ abilities to predict groundwater table levels given forecast rainfall and sea level as input data. When modeling the groundwater table with observed data, LSTM networks were found to have more predictive skill than RNNs (root mean squared error (RMSE) of 0.09 m versus 0.14 m, respectively). The real-time forecast scenario showed that models trained only on storm event data outperformed models trained on the continuous time series data (RMSE of 0.07 m versus 0.66 m, respectively) and that LSTM outperformed RNN models. Because models trained with the continuous time series data had much higher RMSE values, they were not suitable for predicting the groundwater table in the real-time scenario when using forecast input data. These results demonstrate the first use of LSTM networks to create hourly forecasts of groundwater table in a coastal city and show they are well suited for creating operational forecasts in real-time. As groundwater table levels increase due to sea level rise, forecasts of groundwater table will become an increasingly valuable part of coastal flood modeling and management.

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Prediction of Salinity Based on Meteorological Data Using the Backpropagation Neural Network Method

ILMU KELAUTAN Indonesian Journal of Marine Sciences ◽

10.14710/ik.ijms.26.3.207-214 ◽

2021 ◽

Vol 26 (3) ◽

pp. 207-214

Author(s):

Anisa Nur Azizah ◽

Dian C.R. Novitasari ◽

Putroue Keumala Intan ◽

Fajar Setiawan ◽

Ghaluh Indah Permata Sari

Keyword(s):

Neural Network ◽

Time Series Data ◽

Sea Water ◽

Salinity Level ◽

Series Data ◽

Precipitation Data ◽

Backpropagation Neural Network ◽

Seawater Salinity ◽

Salinity Data ◽

Hidden Layer

Salinity is the level of salt dissolved in water. The salinity level of seawater can affect the hydrological balance and climate change. The salinity level of seawater in each area varies depending on the influencing factors, that is evaporation and precipitation (rainfall). One way to find out the salinity level is by taking seawater samples, which requires a long time and costs a lot. In this study, the salinity level of seawater can be predicted by utilizing time series data patterns from evaporation and precipitation using artificial neural network learning, namely the backpropagation neural network. The evaporation and precipitation data used were derived from the ECMWF dataset, while the salinity data were derived from NOAA where each data was taken at the coordinate point of 9,625 113,625 in the south of Java island. Seawater salinity, evaporation, and precipitation data were formed into a 7-day time series data. This study conducted several backpropagation architectural experiments, that is the learning rate, hidden layer, and the number of nodes in the hidden layer to obtain the best results. The results of the seawater salinity prediction were obtained at a MAPE value of 2.063% with a model architecture using 14 input layers, 2 hidden layers with 10 nodes and 2 nodes, 1 output layer, and a learning rate of 0.7. Predicted sea water salinity data ranging from 33 to 35 ppt. Therefore, the prediction system for seawater salinity using the backpropagation method can be said to be good in providing information about the salinity level of sea water on the island of Java.

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Innovative trend analysis of rainfall in relation to soybean productivity over western Maharashtra

Journal of Agrometeorology ◽

10.54386/jam.v23i2.73 ◽

2021 ◽

Vol 23 (2) ◽

pp. 228-235

Author(s):

R.N. SINGH ◽

SONAM SAH ◽

GAURAV CHATURVEDI ◽

BAPPA DAS ◽

H. PATHAK

Keyword(s):

Time Series ◽

Trend Analysis ◽

Time Series Data ◽

Rainfall Variability ◽

Rank Correlation ◽

Annual Rainfall ◽

Series Data ◽

Traditional Methods ◽

Innovative Trend Analysis ◽

Level Of Significance

This study examined and compared the new innovative trend analysis (ITA) of monthly, seasonal and annual rainfall with traditional trend analysis methods in relation to soybean productivity in western Maharashtra. Spearman’s rank correlation, Mann-Kendall and its 6 different modifications were used to analyze the trends of rainfall, whereas Spearman’s rho, simple linear regression and Sen’s slope with two different modifications were employed to quantify the magnitude of trends at 1%, 5% and 10% level of significance. Autocorrelation coefficient was calculated at lag-1 and tested at 5% level of significance. Rainfall variability of the region is very high (CV>30) in all the months and seasons with positively skewed rainfall distribution. Our results revealed that out of 34-time series data analyzed, ITA was able to ide ntify all the significant trends (11 -time series) that can be detected by traditional methods. Meanwhile, ITA also identified trends in 17-time series which cannot be detected by any of the traditional methods. The study revealed significant increase in monsoon and annual rainfall values, which is helpful in sustaining soybean productivity in the western parts of the Maharashtra.

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Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

PsycEXTRA Dataset ◽

10.1037/e634372013-001 ◽

2013 ◽

Author(s):

Stephen J. Tueller ◽

Richard A. Van Dorn ◽

Georgiy Bobashev ◽

Barry Eggleston

Keyword(s):

Time Series ◽

Data Analysis ◽

Exploratory Data Analysis ◽

Time Series Data ◽

Series Data ◽

Exploratory Data

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Some statistical and CI models to predict chaotic high-frequency financial data

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189107 ◽

2020 ◽

Vol 39 (5) ◽

pp. 6419-6430

Author(s):

Dusan Marcek

Keyword(s):

Time Series Data ◽

Moving Average ◽

Methodological Approach ◽

Back Propagation ◽

Large Data ◽

Series Data ◽

Data Set ◽

Training Time ◽

Optimal Population ◽

Forecast Time

To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.

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Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

Jurnal Riset Manajemen Sekolah Tinggi Ilmu Ekonomi Widya Wiwaha Program Magister Manajemen ◽

10.32477/jrm.v1i2.72 ◽

2017 ◽

Vol 1 (2) ◽

pp. 177-191

Author(s):

Rizki Rahma Kusumadewi ◽

Wahyu Widayat

Keyword(s):

Time Series ◽

Exchange Rate ◽

Money Supply ◽

Time Series Data ◽

The United States ◽

Economic Conditions ◽

Series Data ◽

Arch Model ◽

United States Dollar ◽

The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

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Economic Turmoil in Islamic Banking Investment

ETIKONOMI ◽

10.15408/etk.v19i2.17206 ◽

2020 ◽

Vol 19 (2) ◽

Author(s):

Budiandru Budiandru ◽

Sari Yuniarti

Keyword(s):

Interest Rates ◽

Stock Price ◽

Time Series Data ◽

Series Data ◽

Islamic Banking ◽

Vector Error Correction ◽

Investment Financing ◽

Vector Error ◽

Operational Activities

Investment financing is one of the operational activities of Islamic banking to encourage the real sector. This study aims to analyze the effect of economic turmoil on investment financing, analyze the response to investment financing, and analyze each variable's contribution in explaining the diversity of investment financing. This study uses monthly time series data from 2009 to 2020 using the Vector Error Correction Model (VECM) analysis. The results show that the exchange rate, inflation, and interest rates significantly affect Islamic banking investment financing in the long term. The response to investment financing is the fastest to achieve stability when it responds to shocks to the composite stock price index. Inflation is the most significant contribution in explaining diversity in investment financing. Islamic banking should increase the proportion of funding for investment. Customers can have a larger business scale to encourage economic growth, with investment financing increasing.JEL Classification: E22, G11, G24How to Cite:Budiandru., & Yuniarti, S. (2020). Economic Turmoil in Islamic Banking Investment. Etikonomi: Jurnal Ekonomi, 19(2), xx – xx. https://doi.org/10.15408/etk.v19i2.17206.

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