Population Forecast of China’s Rural Community Based on CFANGBM and Improved Aquila Optimizer Algorithm

Rural community population forecasting has important guiding significance to rural construction and development. In this study, a novel grey Bernoulli model combined with an improved Aquila Optimizer (IAO) was used to forecast rural community population in China. Firstly, this study improved the Aquila Optimizer by combining quasi-opposition learning strategy and wavelet mutation strategy, and proposed the new IAO algorithm. By comparing with other algorithms on CEC2017 test functions, the proposed IAO algorithm has the advantages of faster convergence speed and higher convergence accuracy. Secondly, based on the data of China’s rural community population from 1990 to 2019, a consistent fractional accumulation nonhomogeneous grey Bernoulli model called CFANGBM(1, 1, b, c) was established for rural population forecasting. The proposed IAO algorithm was used to optimize the parameters of the model, and then the rural population of China was predicted. Four error measures were used to evaluate the model, and by comparing with other forecasting models, the experimental results show that the proposed model had the smallest error between the forecasted value and the real value, which illustrates the effectiveness of using the IAO algorithm to solve CFANGBM(1, 1, b, c). At the end of this paper, the forecast data of China’s rural population from 2020 to 2024 are given for reference.

Small–medium enterprise formation and Nigerian economic growth

PurposeThis study examines the nexus between entrepreneurship through small–medium enterprise (SME) business formation and the growth of the Nigerian economy. Furthermore, this paper seeks to explore the link between small enterprise development and econo.Design/methodology/approachThe paper focused on secondary data for the period 1990–2016 for macro parameters including, registered small and medium scale enterprise, nominal gross domestic product, employment, total labor force and population. Forecasting technique was applied to obtain data for missing trends. Quantitative analytical techniques used include the dynamic method of the error correction model (ECM) and Johansen co-integration test for a long-run correlation.FindingsThe result shows an increasing number of SME formation which has also led to the growth of the economy. However, an increase in the amount of micro-small and medium scale enterprises did not contribute to the development of the economy more than existing businesses. The employment elasticity is positive and significant and shows that the contribution of entrepreneurship regarding employment is the most essential factor that advances economic growth and reduction of unemployment.Originality/valueThe paper examines how the persistent increase in small and medium enterprise formation improves the growth and development of the Nigerian economy, employing the ECM approach.

Economic design of alternative system to reduce the water distribution losses for sustainability

Water companies and their consumers affected with leakages in water distribution system worldwide. This has attracted many practitioner’s attention as well as researchers over the past years. Selected study area suffers from water losses of about 10 to 15% which accounts to loss of about 9 to 9.75 million liters per month. The present study was under taken to understand, analyze and evaluate the losses and suggest preventive measures of wrapping and repair clamping for control of these losses. The assessment of water losses is done through comparative analysis of data using Microsoft Excel software. Population forecasting is done in context of assessing the amount of water lost that can be prevented in future decades, adjusting to increased water demand and losses. For better efficiency of the suggested methods, experimental analysis was carried out on a reduced scale model of a single stretched pipeline. Cost estimation of the preventive measures was done by obtaining information about the materials used by trading professionals.

Dengue Vector Population Forecasting Using Multisource Earth Observation Products and Recurrent Neural Networks

This article introduces a technique for using recurrent neural networks to forecast Ae. aegyptimosquito (Dengue transmission vector) counts at neighborhood-level, using Earth Observation data inputs as proxies to environmental variables. The model is validated using in situdata in two Brazilian cities, and compared with state-of-the-art multioutput random forest and k-nearest neighbor models. The approach exploits a clustering step performed before the model definition, which simplifies the task by aggregating mosquito count sequences with similar temporal patterns.

Dengue Vector Population Forecasting Using Multisource Earth Observation Products and Recurrent Neural Networks

This article introduces a technique for using recurrent neural networks to forecast Ae. aegyptimosquito (Dengue transmission vector) counts at neighborhood-level, using Earth Observation data inputs as proxies to environmental variables. The model is validated using in situdata in two Brazilian cities, and compared with state-of-the-art multioutput random forest and k-nearest neighbor models. The approach exploits a clustering step performed before the model definition, which simplifies the task by aggregating mosquito count sequences with similar temporal patterns.

Methods for small area population forecasts: state-of-the-art and research needs

Small area population forecasts are widely used by government and business for a variety of planning, research and policy purposes, and often influence major investment decisions. Yet the toolbox of small area population forecasting methods and techniques is modest relative to that for national and large subnational regional forecasting. In this paper we assess the current state of small area population forecasting, and suggest areas for further research. The paper provides a review of the literature on small area population forecasting methods published over the period 2001-2020. The key themes covered by the review are: extrapolative and comparative methods, simplified cohort-component methods, model averaging and combining, incorporating socio-economic variables and spatial relationships, ‘downscaling’ and disaggregation approaches, linking population with housing, estimating and projecting small area component input data, microsimulation, machine learning, and forecast uncertainty. Several avenues for further research are then suggested, including more work on model averaging and combining, developing new forecasting methods for situations which current models cannot handle, quantifying uncertainty, exploring methodologies such as machine learning and spatial statistics, creating user-friendly tools for practitioners, and understanding more about how forecasts are used.

Ambient Humidity Affects Development, Survival, and Reproduction of the Invasive Fall Armyworm, Spodoptera frugiperda (Lepidoptera: Noctuidae), in China

Ambient humidity can directly affect the water balance in insects. The migratory fall armyworm, Spodoptera frugiperda Smith, has spread to more than 60 countries and regions in Africa, Asia, and Oceania that have a great difference in average ambient humidity. Understanding the effects of ambient humidity changes on its development, survival, and reproduction can help to predict its population dynamics in different habitats. Therefore, we evaluated the effects of atmospheric relative humidity (RH) on the development, survival, and reproduction and soil moisture on the pupation and emergence of fall armyworm. As a result, survival and pupal mass increased significantly with increasing RH. Among the five RHs tested, 80% RH was the most suitable for fall armyworm with the highest intrinsic rate of increase (r), finite rate of increase (λ), and net reproduction rate (R0). The population growth at the different RHs in decreasing order was 80 > 100 > 60 > 40 > 20%. A relative moisture (RM) of soil from 6.80 to 47.59% was suitable for fall armyworm pupation, survival, and eclosion, but fall armyworm could not pupate normally in soil with 88.39 and 95.19% RM. The survival and emergence rate of fall armyworm pupae were reduced by irrigation that increased the RM after the mature larvae entered the soil. These findings may be helpful for refining laboratory rearing protocols, population forecasting, and management of fall armyworm.

Modeling water demand in a growing public university in Nigeria

Nigerian university campuses are facing the twin problems of providing portable water of adequate quantity and quality and the sustainability of such supply. This paper examines the water demand status of a public University in Nigeria. Domestic, Public and Industrial water uses were considered while population forecasting was done using regression analysis for a 30 years design period (2018 – 2048). Results reveals possible population increase of 53.8 % by 2048 when the institution will clock 60 years. Water demand is also expected to rise sharply from 5,206 m3 day -1 (2018) to 10,959 m3 day -1 (2048); existing storage capacity cannot satisfy the current needs not to talk of the projected demand hence, a reservoir of about 11,000 m3 will be needed to service the university for the next 30 years; attracting more investments into the water supply system becomes imperative as the existing supplies from groundwater is unsustainable. Keywords: Water Demand; Population; University; Sustainability; Growth