scholarly journals Sustainable Agriculture Development in the Western Desert of Egypt: A Case Study on Crop Production, Profit, and Uncertainty in the Siwa Region

2020 ◽  
Vol 12 (16) ◽  
pp. 6568
Author(s):  
Noha H. Moghazy ◽  
Jagath J. Kaluarachchi
Keyword(s):  
Crop Production ◽  
Moving Average ◽  
Arima Model ◽  
Development Project ◽  
Production Costs ◽  
Western Desert ◽  
Aquifer System ◽  
Nubian Aquifer ◽  
Egyptian Government ◽  
Agriculture Development

The Egyptian government initiated a development project in 2015 to reclaim 1.5 million acres with the primary goal of increasing agricultural production. Siwa is one of these areas in the Western Desert of Egypt, with 30,000 acres using groundwater from the Nubian Sandstone Aquifer System (NSAS). This study investigates if government goals are achievable in the next 20 years to secure the food and water needs of the Siwa region. Results show that total required crop areas are 7154 and 6629 acres in winter and summer, respectively. These areas are less than 17,010 acres of available area for cultivation (Av). The estimated total water use is 40.6 million cubic meters (MCM), which is less than the 88 MCM that is considered available groundwater in the Nubian Aquifer System (NAS). Due to available capacity in Siwa, an optimization model is used to maximize crop production considering government policies. The Autoregressive Integrated Moving Average (ARIMA) model was applied to predict production costs and sell prices of cultivated crops. Analysis included different scenarios beyond government-recommended approaches to identify ways to further expand agriculture production under sustainable conditions. Results provide valuable insights to the ability to achieve government goals from the project and changes that may be required to enhance production.

scholarly journals Assessing and Forecasting of Groundwater Level Fluctuation in Joypurhat District, Northwest Bangladesh Using Wavelet Analysis and ARIMA Modeling

2021 ◽  
Author(s):  
Almamunur Rashid ◽  
Mahiuddin Alamgir ◽  
Mohamad Tofayal Ahmed ◽  
Roquia Salam ◽  
Abu Reza Md. Towfiqul I ◽  
...  
Keyword(s):  
Groundwater Level ◽  
Crop Production ◽  
Time Series Data ◽  
Average Rate ◽  
Moving Average ◽  
Arima Model ◽  
Kendall Test ◽  
Series Data ◽  
Agricultural Crop

Abstract Groundwater resource plays a crucial role for agricultural crop production and socio-economic development in some parts of the world including Bangladesh. Joypurhat district, the northwest part of Bangladesh, a crop production hub, is entirely dependent on groundwater irrigation. A precise assessment and prediction of groundwater level (GWL) can assist long-term GWR management, especially in drought-prone agricultural regions. Therefore, this study was carried out to identify trends and magnitude of GWL fluctuation (1980-2019) using the Modified Mann- Kendall test, Pettitt’s Test, and Sen Slope estimators in the drought-prone Joypurhat district, northwest Bangladesh. Time-series data analysis was performed to forecast GWL from 2020 to 2050 using the Auto-Regressive Integrated Moving Average (ARIMA) model. The findings of the MMK test revealed a significant declining trend of GWL, and the trend turning points were identified in the years 1991, 1993, 1997, and 2004, respectively. Results also indicate that the declining rate of GWL varied from 0.104 m/yr to 0.159 m/yr and the average rate of GWL declination was 0.136 m/yr during 1980-2019. The outcomes of wavelet spectrum analysis depicted two significant periods of the declining trend in Khetlal and Akkelpur Upazilas. The results obtained from the optimal identified model ARIMA (2,1,0), indicating that GWL will decline at a depth of 13.76 m in 2050, and the average declination rate of GWL will be 0.143 m/yr in the study area. The predicted results showed a similar declining tendency of GWL from 2020 to 2050, suggesting a disquieting condition, particularly for Khetlal Upazila. This research would provide a practical approach for GWL assessment and prediction that could help decision-makers implement long-term GWR management in the study area.

scholarly journals Peramalan Produksi Menggunakan Metode Autoregressive Integrated Moving Average (ARIMA) di PT. XYZ

2019 ◽  
Vol 2 (1) ◽  
pp. 27
Author(s):  
Mohammad Buchori ◽  
Tedjo Sukmono
Keyword(s):  
Raw Materials ◽  
Moving Average ◽  
Arima Model ◽  
Production Costs ◽  
Planning And Control ◽  
Forecasting Method ◽  
Time Series Method ◽  
Long Term Forecasting ◽  
Short Term Forecasting ◽  
And Control

In production planning and control the first step is to forecast to determine how much production, the company forecasting is still not optimal, because forecasting has an important role in a company. PT. XYZ is a food company that produces chicken meatballs and chicken dumplings. So from that this study uses the forecasting method Autoregressive Integreted Moving Average (ARIMA). ARIMA is often also called the Box-Jenkins time series method. ARIMA is very good for short-term forecasting, while for long-term forecasting the forecasting accuracy is not good. The purpose of this research is to get a good ARIMA model, used to forecast production in the company. So that the production becomes optimal and not excessive which can cause waste of raw materials, which will make production costs a lot. Data processing is done with the help of an Eviews computer program to determine a good ARIMA model, from processing data obtained by ARIMA (1.0,0). With the results obtained forecasting in the period 37 to period 48. 

scholarly journals Elevated radium levels in Nubian Aquifer groundwater of Northeastern Africa

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mahmoud I. Sherif ◽  
Neil C. Sturchio
Keyword(s):  
Water Quality Management ◽  
Radioactive Decay ◽  
Western Desert ◽  
Mitigation Measures ◽  
Radiation Doses ◽  
Radium Isotopes ◽  
Aquifer System ◽  
Nubian Sandstone ◽  
Nubian Aquifer ◽  
Radiological Health

AbstractThe Nubian Sandstone Aquifer System in Northeast Africa and the Middle East is a huge water resource of inestimable value to the population. However, natural radioactivity impairs groundwater quality throughout the aquifer posing a radiological health risk to millions of people. Here we present measurements of radium isotopes in Nubian Aquifer groundwater from population centers in the Western Desert of Egypt. Groundwater has 226Ra and 228Ra activities ranging from 0.01 to 2.11 and 0.03 to 2.31 Bq/L, respectively. Most activities (combined 226Ra + 228Ra) exceed U.S. EPA drinking water standards. The estimated annual radiation doses associated with ingestion of water having the highest measured Ra activities are up to 138 and 14 times the WHO-recommended maxima for infants and adults, respectively. Dissolved Ra activities are positively correlated with barium and negatively correlated with sulfate, while barite is approximately saturated. In contrast, Ra is uncorrelated with salinity. These observations indicate the dominant geochemical mechanisms controlling dissolved Ra activity may be barite precipitation and sulfate reduction, along with input from alpha-recoil and dissolution of aquifer minerals and loss by radioactive decay. Radium mitigation measures should be adopted for water quality management where Nubian Aquifer groundwater is produced for agricultural and domestic consumption.

Forecasting maize yield using ARIMA-Genetic Algorithm approach

2017 ◽  
Vol 46 (4) ◽  
pp. 265-271 ◽  
Author(s):  
Santosha Rathod ◽  
KN Singh ◽  
Prawin Arya ◽  
Mrinmoy Ray ◽  
Anirban Mukherjee ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Crop Production ◽  
Optimization Problems ◽  
Moving Average ◽  
Arima Model ◽  
Maize Yield ◽  
Total Food ◽  
The World ◽  
Food Grain ◽  
Living Organisms

Maize is widely cultivated throughout the world and has highest production among all the cereals. India is the sixth largest producer of maize in the world, contributing 2% of global production and accounting for 9% of the total food grain production in the country. Based on increasing growth rates of poultry, livestock, fish, and milling industries, the demand for maize is expected to increase from the current level of 17 to 45 million tons by 2030. To understand the growing pattern and economics of crop production, it is necessary to predict crop yield using statistical models and geographic information system soil mapping and the impacts of insect and pest damage. In this study, the focus was to forecast maize yield in India using an autoregressive integrated moving average (ARIMA) model and genetic algorithm (GA) approach. GA simulates the evolution of living organisms, where the fittest individual dominates the weaker ones by mimicking the biological mechanism of evolution, such as selection, crossover, and mutation. GA has successfully been applied to solve optimization problems. The study reveals that implementation of GA in ARIMA enhances the prediction accuracy of the model.

Model Statistika Prediksi Energi Surya Dengan Menggunakan Autoregresif Integrated Moving Average (ARIMA)

Jurnal MIPA ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 181
Author(s):  
Imriani Moroki ◽  
Alfrets Septy Wauran
Keyword(s):  
Renewable Energy ◽  
Solar Energy ◽  
Moving Average ◽  
Arima Model ◽  
Solar Irradiation ◽  
Energy Sources ◽  
Energy Potential ◽  
Good Design ◽  
Potential Sources ◽  
The Common

Energi terbarukan adalah salah satu masalah energi paling terkenal saat ini. Ada beberapa sumber potensial energi terbarukan. Salah satu energi terbarukan yang umum dan sederhana adalah energi matahari. Masalah besar ketersediaan energi saat ini adalah terbatasnya sumber energi konvensional seperti bahan bakar. Ini semua sumber energi memiliki banyak masalah karena memiliki jumlah energi yang terbatas. Penting untuk membuat model dan analisis berdasarkan ketersediaan sumber energi. Energi matahari adalah energi terbarukan yang paling disukai di negara-negara khatulistiwa saat ini. Tergantung pada produksi energi surya di daerah tertentu untuk memiliki desain dan analisis energi matahari yang baik. Untuk memiliki analisis yang baik tentang itu, dalam makalah ini kami membuat model prediksi energi surya berdasarkan data iradiasi matahari. Kami membuat model energi surya dan angin dengan menggunakan Metode Autoregresif Integrated Moving Average (ARIMA). Model ini diimplementasikan oleh R Studio yang kuat dari statistik. Sebagai hasil akhir, kami mendapatkan model statistik solar yang dibandingkan dengan data aktualRenewable energy is one of the most fomous issues of energy today. There are some renewable energy potential sources. One of the common n simple renewable energy is solar energy. The big problem of the availability of energy today is the limeted sources of conventional enery like fuel. This all energy sources have a lot of problem because it has a limited number of energy. It is important to make a model and analysis based on the availability of the energy sources. Solar energy is the most prefered renewable energy in equator countries today. It depends on the production of solar energy in certain area to have a good design and analysis of  the solar energy. To have a good analysis of it, in this paper we make a prediction model of solar energy based on the data of solar irradiation. We make the solar and wind enery model by using Autoregresif Integrated Moving Average (ARIMA) Method. This model is implemented by R Studio that is a powerfull of statistical. As the final result, we got the statistical model of solar comparing with the actual data

Prognosticating the spread of covid-19 pandemic based on optimal arima estimators

2020 ◽  
Vol 20 ◽  
Author(s):  
Venuka Sandhir ◽  
Vinod Kumar ◽  
Vikash Kumar
Keyword(s):  
South Africa ◽  
Statistical Analysis ◽  
Moving Average ◽  
Arima Model ◽  
Information Criterion ◽  
Stable Case ◽  
Johns Hopkins University ◽  
Modelling Techniques ◽  
Auto Regressive ◽  
Global Threat

Background: COVID-19 cases have been reported as a global threat and several studies are being conducted using various modelling techniques to evaluate patterns of disease dispersion in the upcoming weeks. Here we propose a simple statistical model that could be used to predict the epidemiological extent of community spread of COVID-19from the explicit data based on optimal ARIMA model estimators. Methods: Raw data was retrieved on confirmed cases of COVID-19 from Johns Hopkins University (https://github.com/CSSEGISandData/COVID-19) and Auto-Regressive Integrated Moving Average (ARIMA) model was fitted based on cumulative daily figures of confirmed cases aggregated globally for ten major countries to predict their incidence trend. Statistical analysis was completed by using R 3.5.3 software. Results: The optimal ARIMA model having the lowest Akaike information criterion (AIC) value for US (0,2,0); Spain (1,2,0); France (0,2,1); Germany (3,2,2); Iran (1,2,1); China (0,2,1); Russia (3,2,1); India (2,2,2); Australia (1,2,0) and South Africa (0,2,2) imparted the nowcasting of trends for the upcoming weeks. These parameters are (p, d, q) where p refers to number of autoregressive terms, d refers to number of times the series has to be differenced before it becomes stationary, and q refers to number of moving average terms. Results obtained from ARIMA model showed significant decrease cases in Australia; stable case for China and rising cases has been observed in other countries. Conclusion: This study tried their best at predicting the possible proliferate of COVID-19, although spreading significantly depends upon the various control and measurement policy taken by each country.

Forecast of COVID-19 Cases in Nigeria: An implementation of an ARIMA model (Preprint)

2020 ◽  
Author(s):  
Sanyaolu Ameye ◽  
Michael Awoleye ◽  
Emmanuel Agogo ◽  
Ette Etuk
Keyword(s):  
Predictive Model ◽  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Epidemiological Data ◽  
Series Data ◽  
Global Pandemic ◽  
First Case ◽  
Epidemiological Trend ◽  
Data Points

BACKGROUND The Coronavirus disease 2019 (COVID-2019) is a global pandemic and Nigeria is not left out in being affected. Though, the disease is just over three months since first case was identified in the country, we present a predictive model to forecast the number of cases expected to be seen in the country in the next 100 days. OBJECTIVE To implement a predictive model in forecasting the near future number of positive cases expected in the country following the present trend METHODS We performed an Auto Regressive Integrated Moving Average (ARIMA) model prediction on the epidemiological data obtained from Nigerian Centre for Disease Control to predict the epidemiological trend of the prevalence and incidence of COVID-2019. RESULTS There were 93 time series data points which lacked stationarity. From our ARIMA model, it is expected that the number of new cases declared per day will keep rising and towards the early September, 2020, Nigeria is expected to have well above sixty thousand confirmed cases. CONCLUSIONS We however believe that as we have more data points our model will be better fine-tuned.

scholarly journals Economics of autonomous equipment for arable farms

2021 ◽  
Author(s):  
James Lowenberg-DeBoer ◽  
Kit Franklin ◽  
Karl Behrendt ◽  
Richard Godwin
Keyword(s):  
Precision Agriculture ◽  
Environmental Performance ◽  
Crop Production ◽  
Practical Experience ◽  
Production Costs ◽  
Farm Size ◽  
Medium Size ◽  
Economic Implications ◽  
The United Kingdom ◽  
Crop Agriculture

AbstractBy collecting more data at a higher resolution and by creating the capacity to implement detailed crop management, autonomous crop equipment has the potential to revolutionise precision agriculture (PA), but unless farmers find autonomous equipment profitable it is unlikely to be widely adopted. The objective of this study was to identify the potential economic implications of autonomous crop equipment for arable agriculture using a grain-oilseed farm in the United Kingdom as an example. The study is possible because the Hands Free Hectare (HFH) demonstration project at Harper Adams University has produced grain with autonomous equipment since 2017. That practical experience showed the technical feasibility of autonomous grain production and provides parameters for farm-level linear programming (LP) to estimate farm management opportunities when autonomous equipment is available. The study shows that arable crop production with autonomous equipment is technically and economically feasible, allowing medium size farms to approach minimum per unit production cost levels. The ability to achieve minimum production costs at relatively modest farm size means that the pressure to “get big or get out” will diminish. Costs of production that are internationally competitive will mean reduced need for government subsidies and greater independence for farmers. The ability of autonomous equipment to achieve minimum production costs even on small, irregularly shaped fields will improve environmental performance of crop agriculture by reducing pressure to remove hedges, fell infield trees and enlarge fields.

scholarly journals Identification of monthly municipal water demand system based on autoregressive integrated moving average model tuned by particle swarm optimization

2017 ◽  
Vol 19 (2) ◽  
pp. 261-281 ◽  
Author(s):  
Sahbi Boubaker
Keyword(s):  
Time Series ◽  
Particle Swarm Optimization ◽  
Water Demand ◽  
Moving Average ◽  
Arima Model ◽  
Particle Swarm ◽  
Demand System ◽  
Swarm Optimization ◽  
Municipal Water ◽  
Average Absolute Relative Error

In this paper, a modeling-identification approach for the monthly municipal water demand system in Hail region, Saudi Arabia, is developed. This approach is based on an auto-regressive integrated moving average (ARIMA) model tuned by the particle swarm optimization (PSO). The ARIMA (p, d, q) modeling requires estimation of the integer orders p and q of the AR and MA parts; and the real coefficients of the model. More than being simple, easy to implement and effective, the PSO-ARIMA model does not require data pre-processing (original time-series normalization for artificial neural network (ANN) or data stationarization for traditional stochastic time-series (STS)). Moreover, its performance indicators such as the mean absolute percentage error (MAPE), coefficient of determination (R2), root mean squared error (RMSE) and average absolute relative error (AARE) are compared with those of ANN and STS. The obtained results show that the PSO-ARIMA outperforms the ANN and STS approaches since it can optimize simultaneously integer and real parameters and provides better accuracy in terms of MAPE (5.2832%), R2 (0.9375), RMSE (2.2111 × 105m3) and AARE (5.2911%). The PSO-ARIMA model has been implemented using 69 records (for both training and testing). The results can help local water decision makers to better manage the current water resources and to plan extensions in response to the increasing need.

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