Temporal Data Analysis and Mining Methods for Modelling the Climate Change Effects on Malaysia's Oil Palm Yield at Different Regional Scales

2016 ◽  
pp. 482-513
Author(s):  
Subana Shanmuganathan ◽  
Ajit Narayanan ◽  
Nishantha Priyanka Kumara Medagoda
Keyword(s):  
Climate Change ◽  
Oil Palm ◽  
Moving Average ◽  
Weather Data ◽  
Crop Models ◽  
Related Data ◽  
Climate Change Effects ◽  
Moving Average Models ◽  
Recent Trends ◽  
Temporal Data Analysis

Space and time related data generated is becoming ever more voluminous, noisy and heterogeneous outpacing the research efforts in the domain of climate. Nevertheless, this data portrays recent climate/ weather change patterns. Thus, insightful approaches are required to overcome the challenges when handling the so called “big data” to unravel the recent unprecedented climate change in particular, its variability, frequency and effects on key crops. Contemporary climate-crop models developed at least two decades ago are found to be unsuitable for analysing complex climate/weather data retrospectively. In this context, the chapter looks at the use of scalable time series analysis, namely ARIMA (Autoregressive integrated moving average) models and data mining techniques to extract new knowledge on the climate change effects on Malaysia's oil palm yield at the regional and administrative divisional scales. The results reveal recent trends and patterns in climate change and its effects on oil palm yield impossible otherwise e.g. Traditional statistical methods alone.

Temporal Data Analysis and Mining Methods for Modelling the Climate Change Effects on Malaysia's Oil Palm Yield at Different Regional Scales

2017 ◽  
pp. 499-531
Author(s):  
Subana Shanmuganathan ◽  
Ajit Narayanan ◽  
Nishantha Priyanka Kumara Medagoda
Keyword(s):  
Climate Change ◽  
Oil Palm ◽  
Moving Average ◽  
Weather Data ◽  
Crop Models ◽  
Related Data ◽  
Climate Change Effects ◽  
Moving Average Models ◽  
Recent Trends ◽  
Temporal Data Analysis

Space and time related data generated is becoming ever more voluminous, noisy and heterogeneous outpacing the research efforts in the domain of climate. Nevertheless, this data portrays recent climate/ weather change patterns. Thus, insightful approaches are required to overcome the challenges when handling the so called “big data” to unravel the recent unprecedented climate change in particular, its variability, frequency and effects on key crops. Contemporary climate-crop models developed at least two decades ago are found to be unsuitable for analysing complex climate/weather data retrospectively. In this context, the chapter looks at the use of scalable time series analysis, namely ARIMA (Autoregressive integrated moving average) models and data mining techniques to extract new knowledge on the climate change effects on Malaysia's oil palm yield at the regional and administrative divisional scales. The results reveal recent trends and patterns in climate change and its effects on oil palm yield impossible otherwise e.g. Traditional statistical methods alone.

scholarly journals Statistical Investigation of Climate Change Effects on the Utilization of the Sediment Heat Energy

Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 435
Author(s):  
Nebiyu Girgibo ◽  
Anne Mäkiranta ◽  
Xiaoshu Lü ◽  
Erkki Hiltunen
Keyword(s):  
Climate Change ◽  
Moving Average ◽  
Arima Model ◽  
Measurement Data ◽  
Statistical Investigation ◽  
Heat Energy ◽  
Land Uplift ◽  
Seabed Sediment ◽  
Climate Change Effects ◽  
Sediment Temperature

Suvilahti, a suburb of the city of Vaasa in western Finland, was the first area to use seabed sediment heat as the main source of heating for a high number of houses. Moreover, in the same area, a unique land uplift effect is ongoing. The aim of this paper is to solve the challenges and find opportunities caused by global warming by utilizing seabed sediment energy as a renewable heat source. Measurement data of water and air temperature were analyzed, and correlations were established for the sediment temperature data using Statistical Analysis System (SAS) Enterprise Guide 7.1. software. The analysis and provisional forecast based on the autoregression integrated moving average (ARIMA) model revealed that air and water temperatures show incremental increases through time, and that sediment temperature has positive correlations with water temperature with a 2-month lag. Therefore, sediment heat energy is also expected to increase in the future. Factor analysis validations show that the data have a normal cluster and no particular outliers. This study concludes that sediment heat energy can be considered in prominent renewable production, transforming climate change into a useful solution, at least in summertime.

Exploration of Climate Change Effects on Shifting Potato Seasons, Yields and Water Use Employing NASA and National Long-Term Weather Data

2020 ◽  
Vol 63 (4) ◽  
pp. 565-577 ◽  
Author(s):  
Ayten Kubra Yagiz ◽  
Mustafa Cakici ◽  
Nazlican Aydogan ◽  
Seher Omezli ◽  
Bayram Ali Yerlikaya ◽  
...  
Keyword(s):  
Climate Change ◽  
Water Use ◽  
Weather Data ◽  
Climate Change Effects

scholarly journals The Effect of Climate Change on Agricultural Crop Returns in Uganda

2019 ◽  
Vol 11 (4(J)) ◽  
pp. 71-87
Author(s):  
Geoffrey Norman Tumwine ◽  
Razack B Lokina ◽  
John Mary Matovu
Keyword(s):  
Climate Change ◽  
Farm Size ◽  
Weather Data ◽  
Agricultural Crop ◽  
Climate Factors ◽  
Climate Data ◽  
Climate Change Effects ◽  
Average Temperature ◽  
The World Bank ◽  
Farming Households

The study examined the effect of climate change on agricultural crop returns in Uganda using the Ricardian Panel Tobit technique and the World Bank Living Standards Measurement Survey (LSMS) data, climate data from Uganda National Meteorological Authority (UNMA) and global weather data. The findings showed that climate related risks account for over 67 percent of agricultural risks and less than 2 percent of the farming households practise irrigation. Farmers that practised irrigation earned higher agricultural returns nationally than their counterparts did. The findings show that the output elasticities with respect to temperature range from -2.02 percent to 0.543 percent. This implies that for the average temperature increase by 1 percent, maize farm returns decreased by 2.02 percent, banana by 1.7 percent, cassava by 1.50 percent and beans by 1.01 percent. While 1 percent increases in rainfall, lowered banana returns by 0.02 percent, beans by 0.08 percent, cassava by 0.035 percent, maize by 0.025 percent except for groundnuts’ returns increased by 0.115 percent. Apart from climate factors, non-climate factors such as capital, labour, farm size, fertilizers and soil quality are equally important inputs and significantly impact on agricultural farm returns. The study proposes that due to unrelenting adverse climate change effects in Uganda, adoption of multi-pronged approaches such as extensive irrigation, agro-insurance, diversification of agricultural activities, use of food cribs during bumper harvests would be the breath of life for Ugandan farmers.

Detection of regional climate change effects on alpine hydrology by daily resolution trend analysis in Tyrol, Austria

2014 ◽  
Vol 6 (1) ◽  
pp. 124-143 ◽  
Author(s):  
Christoph Kormann ◽  
Till Francke ◽  
Axel Bronstert
Keyword(s):  
Climate Change ◽  
Temporal Dynamics ◽  
Moving Average ◽  
Regional Climate ◽  
Regional Climate Change ◽  
Early Summer ◽  
Climate Change Effects ◽  
Investigation Area ◽  
Time Periods ◽  
Mean Climate

Owing to average temperature increases of at least twice the global mean, climate change is expected to have strong impacts on local hydrology and climatology in the Alps. Nevertheless, trend analyses of hydro-climatic station data rarely reveal clear patterns concerning climate change signals except in temperature observations. However, trend research has thus far mostly been based on analysing trends of averaged data such as yearly, seasonal or monthly averages and has therefore often not been able to detect the finer temporal dynamics. For this reason, we derived 30-day moving average trends, providing a daily resolution of the timing and magnitude of trends within the seasons. Results are validated by including different time periods. We studied daily observations of mean temperature, liquid and solid precipitation, snow height and runoff in the relatively dry central Alpine region in Tyrol, Austria. Our results indicate that the vast majority of changes are observed throughout spring to early summer, most likely triggered by the strong temperature increase during this season. Temperature, streamflow and snow trends have clearly amplified during recent decades. The overall results are consistent over the entire investigation area and different time periods.

Examining the Adoption of ICTs for Weather Monitoring and Climate Change Adaptation in Uganda

2020 ◽  
Vol 9 (1) ◽  
pp. 67-81
Author(s):  
Agnes Rwashana Semwanga ◽  
Alice Mary Atwine
Keyword(s):  
Climate Change ◽  
Government Support ◽  
Weather Data ◽  
Information Communication ◽  
Factors Affecting ◽  
Adaptation Capacity ◽  
Weather Forecasts ◽  
Climate Change Effects ◽  
Limited Government ◽  
Ict Adoption

Information communication technologies can only be beneficial to developing countries struggling to build adaptation capacity if technology adoption frameworks are tailored to suit their specific characteristics. The lack of timely, accurate, and reliable weather data and the increasing rate at which climate-related disasters are destroying lives and property in Uganda is evident of lack of good weather forecasts. The study set out to investigate the factors affecting ICT adoption and determine the technologies being used to respond to climate change effects. Specifically, the study set out the extent of use and the factors hindering or guiding ICT adoption. Factors hindering ICT adoption ranging from poor infrastructure to limited government support were established. The strategies that can be used to resolve challenges of ICT adoption, the major stakeholders, their responsibilities and how ICT adoption and utilisation can be enhanced to benefit other sectors of the economy is presented.

scholarly journals Predicting the impacts of climate change on groundwater recharge in an arid environment using modeling approach

2019 ◽  
Vol 11 (1) ◽  
pp. 88-99 ◽  
Author(s):  
Reza Ghazavi ◽  
Haidar Ebrahimi
Keyword(s):  
Climate Change ◽  
Groundwater Recharge ◽  
Flow Model ◽  
Moving Average ◽  
Arid Environment ◽  
Weather Data ◽  
Climate Scenarios ◽  
Content Type ◽  
Average Rainfall

Purpose Groundwater is an important source of water supply in arid and semi-arid areas. The purpose of this study is to predict the impact of climate change on groundwater recharge in an arid environment in Ilam Province, west of Iran. Design/methodology/approach A three-dimensional transient groundwater flow model (modular finite difference groundwater FLOW model: MODFLOW) was used to simulate the impacts of three climate scenarios (i.e. an average of a long-term rainfall, predicted rainfall in 2015-2030 and three years moving average rainfall) on groundwater recharge and groundwater levels. Various climate scenarios in Long Ashton Research Station Weather Generator were applied to predict weather data. Findings HadCM3 climatic model and A2 emission scenario were selected as the best methods for weather data generation. Based on the results of these models, annual precipitation will decrease by 3 per cent during 2015-2030. For three emission scenarios, i.e. an average of a long-term rainfall, predicted rainfall in 2015-2030 and three years moving average rainfall, precipitation in 2030 is estimated to be 265, 257 and 247 mm, respectively. For the studied aquifer, predicted recharge will decrease compared to recharge calculated based on the average of long-term rainfall. Originality/value The decline of groundwater level in the study area was 11.45 m during the past 24 years or 0.48 m/year. Annual groundwater depletion should increase to 0.75 m in the coming 16 years via climate change. Climate change adaptation policies in the basin should include changing the crop type, as well as water productivity and irrigation efficiency enhancement at the farm and regional scales.

Examining the Adoption of ICTs for Weather Monitoring and Climate Change Adaptation in Uganda

2022 ◽  
pp. 840-857
Author(s):  
Agnes Rwashana Semwanga ◽  
Alice Mary Atwine
Keyword(s):  
Climate Change ◽  
Communication Technologies ◽  
Government Support ◽  
Weather Data ◽  
Factors Affecting ◽  
Adaptation Capacity ◽  
Weather Forecasts ◽  
Climate Change Effects ◽  
Limited Government ◽  
Ict Adoption

Information communication technologies can only be beneficial to developing countries struggling to build adaptation capacity if technology adoption frameworks are tailored to suit their specific characteristics. The lack of timely, accurate, and reliable weather data and the increasing rate at which climate-related disasters are destroying lives and property in Uganda is evident of lack of good weather forecasts. The study set out to investigate the factors affecting ICT adoption and determine the technologies being used to respond to climate change effects. Specifically, the study set out the extent of use and the factors hindering or guiding ICT adoption. Factors hindering ICT adoption ranging from poor infrastructure to limited government support were established. The strategies that can be used to resolve challenges of ICT adoption, the major stakeholders, their responsibilities and how ICT adoption and utilisation can be enhanced to benefit other sectors of the economy is presented.

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