Variability and Changes in Rainfall Observed Across Jharkhand Region (India)

Rainfall variability has a substantial impact on water supplies, agricultural output, and, as a result, the economy. It examines the historical spatiotemporal variability and trend of rainfall on Jharkhand's annual and seasonal time series state over a 60-year period (1954–2013). The goal of this study was to find trends in long and short-term changes in rainfall amounts in the Jharkhand region at various spatial scales. With the help of the wavelet technique, we were able to determine the periodicity of rainfall over time and identify active and break days in the monsoon season. When the OLR positive anomaly increases, rainfall decreases (Break days), and when the OLR negative anomaly increases, rainfall increases (Active days). The Indian summer monsoon extreme is also strongly linked to the Equatorial Indian Ocean Oscillation (EQUINOO), which is based on surface zonal wind across the central equatorial Indian Ocean. Because the Bay of Bengal is next to Jharkhand, local disturbances or cyclonic events are also discovered and their impact on rainfall is investigated. Keywords: Rainfall, ENSO, Wavelet Transform, Active and Break days, Cyclone, Climate Change.

The Spatiotemporal Trend and Pattern of the Rainfall Amount, Intensity and Frequency in TRMM Multi-Satellite Precipitation Analysis (TMPA) Data

The spatiotemporal mean rain rate (MR) can be characterized by the rain frequency (RF) and the conditional rain rate (CR). We computed these parameters for each season using the TMPA 3-hourly, 0.25° gridded data for the 1998–2017 period at a quasi-global scale, 50°N~50°S. For the global long-term average, MR, RF, and CR are 2.83 mm/d, 10.55%, and 25.05 mm/d, respectively. The seasonal time series of global mean RF and CR show significant decreasing and increasing trends, respectively, while MR depicts only a small but significant trend. The seasonal anomaly of RF decreased by 5.29% and CR increased 13.07 mm/d over the study period, while MR only slightly decreased by −0.029 mm/day. The spatiotemporal patterns in MR, RF, and CR suggest that although there is no prominent trend in the total precipitation amount, the frequency of rainfall events becomes smaller and the average intensity of a single event becomes stronger. Based on the co-variability of RF and CR, the paper optimally classifies the precipitation over land and ocean into four categories using K-means clustering. The terrestrial clusters are consistent with the dry and wet climatology, while categories over the ocean indicate high RF and medium CR in the Inter Tropical Convergence Zone (ITCZ) region; low RF with low CR in oceanic dry zones; and low RF and high CR in storm track areas. Empirical Orthogonal Function (EOF) analysis was then performed, and these results indicated that the major pattern of MR is characterized by an El Niño-Southern Oscillation (ENSO) signal while RF and CR variations are dominated by their trends.

Short-Term Load Forecasting in a microgrid environment: Investigating the series-specific and cross-learning forecasting methods

A reliable and accurate load forecasting method is key to successful energy management of smart grids. Due to the non-linear relations in data generating process and data availability issues, load forecasting remains a challenging task. Here, we investigate the application of feed forward artificial neural networks, recurrent neural networks and crosslearning methods for day-ahead and three days-ahead load forecasting. The effectiveness of the proposed methods is evaluated against a statistical benchmark, using multiple accuracy metrics. The test data sets are high resolution multi-seasonal time series of electricity demand of buildings in Belgium, Canada and the UK from private measurements and open access sources. Both FFNN and RNN methods show competitive results on benchmarking datasets. Best method varies depending on the accuracy metric selected. The use of cross-learning in fitting a global RNN model has an improvement on the final accuracy.

Hybrid SARIMA-FFNN model in forecasting cash outflow and inflow

The monthly inflow and outflow of money from an area is one of the important concerns in the economic life of a region. This study aims to model and predict the monthly cash inflow and outflow of Kediri, East Java Province, Indonesia using the Hybrid Seasonal Autoregressive Integrated Moving Average – Feedforward Neural Network (SARIMA-FFNN) model. Seasonal time series data from monthly cash inflow and outflow of Kediri are used to test the forecasting accuracy of the proposed hybrid model. First, both variables are modeled using the SARIMA model. Then, non-linearity testing was carried out on the best SARIMA model for each variable and the results showed that only cash inflow was non-linear. Therefore, only cash inflow could be continued with the FFNN model. The best selected model was the FFNN model with the input SARIMA(0,0,0)(1,0,0)12 with five hidden layers. The input of FFNN modeling was based on the best SARIMA model with only the autoregressive order which for non-seasonal and seasonal. The sum of hidden layers was chosen by the smallest values of MAPE and RMSE. Forecasting results with the hybrid SARIMA-FFNN model on data testing followed the actual data pattern.

Forecasting Needs Of Mountain Types Of DDD Bike Using The Seasonal Autoregressive Integrated Moving Average Model Approach

One of the bicycle manufacturers in Indonesia, namely PT. DDD is a manufacture engaged in the production of various types of bicycles with a make to stock production system. Market demand that fluctuates every year results in a lack of readiness to meet market needs. So a re-planning is needed in order to meet all market demands. The Box Jenkins statistical method, the Seasonal Autoregressive Integrated Moving Average model, is one of the appropriate approaches to solve problems at PT. DDD. The advantages of the SARIMA model can be used to forecast seasonal or non-seasonal time series simultaneously. The best SARIMA model approach to forecasting demand for mountain bikes at PT. DDD is SARIMA (0,0,0)(0,1,1)12 with the equation Zt=Zt-12+ΘQat-12+at with the smallest MAPE value of 32.35%. So that the model is said to be feasible to predict mountain bikes and the model can predict up to 12 periods in 2021.

A parallel-series hybridization of seasonal intelligent based statistical model for demand forecasting

Purpose The purpose of this paper, because of the complexity of demand time series and the need to construct a more accurate hybrid model that can model all relationships in data, is to propose a parallel-series hybridization of seasonal neural networks and statistical models for demand time series forecasting. Design/methodology/approach The main idea of proposed model is centered around combining parallel and series hybrid methodologies to use the benefit of unique advantages of both hybrid strategies as well as intelligent and classic seasonal time series models simultaneously for achieving results that are more accurate for the first time. In the proposed model, in contrast of traditional parallel and series hybrid strategies, it can be generally shown that the performance of the proposed model will not be worse than components. Findings Empirical results of forecasting two well-known seasonal time series data sets, including the total production value of the Taiwan machinery industry and the sales volume of soft drinks, indicate that the proposed model can effectively improve the forecasting accuracy achieved by either of their components used in isolation. In addition, the proposed model can achieve more accurate results than parallel and series hybrid model with same components. Therefore, the proposed model can be used as an appropriate alternative model for seasonal time series forecasting, especially when higher forecasting accuracy is needed. Originality/value To the best of the authors’ knowledge, the proposed model, for first time and in contrast of traditional parallel and series hybrid strategies, is developed.