2022
Big Data Analytics for Climate-Resilient Food Supply Chains: Opportunities and Way Forward
Navin Kumar C. Twarakavi, Kamal Das, Mohamed Akram Zaytar, Fred Otieno, Jitendra Singh, Bruno Silva, Komminist Weldemariam, Shantanu Godbole
Data Science in Agriculture and Natural Resource Management, pp. 181--192, Springer Singapore, 2022
Abstract
A reliable forecast of food production in a given region, under the effects of climate change and increased occurrence of extreme events, is a prerequisite to developing resilience in the future food supply. As the climate is changing, an increasing occurrence of extreme events combined with shift in seasonal weather pattern is rendering traditional agricultural practices a high level of risk. Currently, strategies to plan for an upcoming season are based on data from the recent seasons. Current methods to forecasting food production levels and derisk an upcoming season in any given region, are rudimentary and at best, not scalable. The advent of big data and new data sources such as weather forecasts, remote sensing, scalable machine-learning methods and cloud computing has created new opportunities for understanding the impact of an upcoming growing season. In order to demonstrate the usefulness of the current data sources and methods, this chapter presents a methodology that combines seasonal weather forecasts, geo-spatial information derived from remote-sensing, risks posed by extreme events and crop growth models to estimate production risk at a regional scale. The method was validated for multiple growing seasons in some counties in Iowa.
2021
Climate extremes on Crop Yield: A Case Study for USA Corn Belt
Kamal Das, Navin Twarakavi, Akram Zaytar, Fred Otieno, Jitendra Singh
AGU Fall Meeting 2021, AGU
Abstract
It is well known that climate is changing and extremes and variability have strong implications for crop productivity. Several prior research reported climate extreme induced crop yield variation w.r.t average/normal conditions. Here we aim to analyse how extreme events are related to the likelihood of lower yields at the regional scale. We investigate not only the impacts of drought and flood on crop yields but also several other climate extremes indices which describe changes in heatwaves, coldwaves, tropical cyclones etc, as these extremes can be detrimental to crops as well. This study intended to understand how climate extremes influence corn yield in USA using the Structural Equation Modelling (SEM) method. The SEM method is a typical systematic analysis method, which integrates the linear models of many variables to measure the relationship between yield and climate extremes (latent variables), and between all latent variables. Our preliminary results indicate that climate extremes (drought, flood, heat/cold waves) have a noticeable effect on corn yields in many areas across USA. This study will strengthen understanding of extreme events related implications on food production, which is relevant also in the context of climate change, as the frequency of extreme events is likely to increase in many regions worldwide.
Climate-aware Forecasting Of Agricultural Produce Across Large Regions
Navin Twarakavi, Kamal Das, Fred Otieno, Mohamed Akram Zaytar, Jitendra Singh
INFORMS, 2021
Abstract
As the climate is changing, an increasing occurrence of extreme events combined with random shift in seasonal weather patterns is leading to a high uncertainty in food supply. In this research, we present a methodology that combines seasonal forecasts of weather and extreme events, along with agronomic data using AI methods to predict risk to food production at scale. In our case study, we forecast supply of corn across a large state in India, with lead times of up to 4 months. Our method can predict other important considerations such as harvest window prediction and water footprint. Our analysis shows the potential of AI and geo-spatial data analytics to better quantify future food production.
AI-assisted tracking of worldwide non-pharmaceutical interventions for COVID-19
Parthasarathy Suryanarayanan, Ching-Huei Tsou, Ananya Poddar, Diwakar Mahajan, Bharath Dandala, Piyush Madan, Anshul Agrawal, Charles Wachira, Osebe Mogaka Samuel, Osnat Bar-Shira, Clifton Kipchirchir, Sharon Okwako, William Ogallo, Fred Otieno, Timothy Nyota, Fiona Matu, Vesna Resende Barros, Daniel Shats, Oren Kagan, Sekou Remy, Oliver Bent, Pooja Guhan, Shilpa Mahatma, Aisha Walcott-Bryant, Divya Pathak, Michal Rosen-Zvi
Scientific Data 8(1), 94, 2021
Abstract
The Coronavirus disease 2019 (COVID-19) global pandemic has transformed almost every facet of human society throughout the world. Against an emerging, highly transmissible disease, governments worldwide have implemented non-pharmaceutical interventions (NPIs) to slow the spread of the virus. Examples of such interventions include community actions, such as school closures or restrictions on mass gatherings, individual actions including mask wearing and self-quarantine, and environmental actions such as cleaning public facilities. We present the Worldwide Non-pharmaceutical Interventions Tracker for COVID-19 (WNTRAC), a comprehensive dataset consisting of over 6,000 NPIs implemented worldwide since the start of the pandemic. WNTRAC covers NPIs implemented across 261 countries and territories, and classifies NPIs into a taxonomy of 16 NPI types. NPIs are automatically extracted daily from Wikipedia articles using natural language processing techniques and then manually validated to ensure accuracy and veracity. We hope that the dataset will prove valuable for policymakers, public health leaders, and researchers in modeling and analysis efforts to control the spread of COVID-19.
2020
WNTRAC: Artificial Intelligence Assisted Tracking of Non-pharmaceutical Interventions Implemented Worldwide for COVID-19
Parthasarathy Suryanarayanan, Ching-Huei Tsou, Ananya Poddar, Diwakar Mahajan, Bharath Dandala, Piyush Madan, Anshul Agrawal, Charles Wachira, Samuel Mogaka Osebe, Osnat Bar-Shira, Clifton Kipchirchir, Sharon Okwako, William Ogallo, Fred Otieno, Timothy Nyota, Fiona Matu, Vesna Resende Barros, Daniel Shats, Oren Kagan, Sekou Remy, Oliver Bent, Shilpa Mahatma, Aisha Walcott-Bryant, Divya Pathak, Michal Rosen-Zvi
arXiv preprint arXiv:2009.07057, 2020
Abstract pandemic, public health, psychological intervention, knowledge management, business, coronavirus disease 2019, human society, public facility, transmissible disease
The Coronavirus disease 2019 (COVID-19) global pandemic has transformed almost every facet of human society throughout the world. Against an emerging, highly transmissible disease with no definitive treatment or vaccine, governments worldwide have implemented non-pharmaceutical intervention (NPI) to slow the spread of the virus. Examples of such interventions include community actions (e.g. school closures, restrictions on mass gatherings), individual actions (e.g. mask wearing, self-quarantine), and environmental actions (e.g. public facility cleaning). We present the Worldwide Non-pharmaceutical Interventions Tracker for COVID-19 (WNTRAC), a comprehensive dataset consisting of over 6,000 NPIs implemented worldwide since the start of the pandemic. WNTRAC covers NPIs implemented across 261 countries and territories, and classifies NPI measures into a taxonomy of sixteen NPI types. NPI measures are automatically extracted daily from Wikipedia articles using natural language processing techniques and manually validated to ensure accuracy and veracity. We hope that the dataset is valuable for policymakers, public health leaders, and researchers in modeling and analysis efforts for controlling the spread of COVID-19.
pandemic, public health, psychological intervention, knowledge management, business, coronavirus disease 2019, human society, public facility, transmissible disease
Multi-factor authentication for users of non-internet based applications of blockchain-based platforms
Andrew Kinai, Fred Otieno, Nelson Bore, Komminist Weldemariam
2020 IEEE International Conference on Blockchain (Blockchain), pp. 525-531
Abstract multi factor authentication, database transaction, authentication, unstructured supplementary service data, financial transaction, computer security, risk analysis, computer science, blockchain, internet based
Attacks targeting several millions of non-internet based application users are on the rise. These applications such as SMS and USSD typically do not benefit from existing multi-factor authentication methods due to the nature of their interaction interfaces and mode of operations. To address this problem, we propose an approach that augments blockchain with multi-factor authentication based on evidence from blockchain transactions combined with risk analysis. A profile of how a user performs transactions is built overtime and is used to analyse the risk level of each new transaction. If a transaction is flagged as high risk, we generate n-factor layers of authentication using past endorsed blockchain transactions. A demonstration of how we used the proposed approach to authenticate critical financial transactions in a blockchain-based asset financing platform is also discussed.
doi
multi factor authentication, database transaction, authentication, unstructured supplementary service data, financial transaction, computer security, risk analysis, computer science, blockchain, internet based
Machine Learning Approaches to Safeguarding Continuous Water Supply in the Arid and Semi-arid Lands of Northern Kenya
Fred Otieno, Timothy Nyota, Isaac Waweru, Celia Cintas, Samuel C Maina, William Ogallo, Aisha Walcott-Bryant
ICLR Workshop Tackling Climate Change with Machine Learning (Spotlight Talk), 2020
Abstract
Arid and semi-arid regions (ASALs) in developing countries are heavily affected by the effects of global warming and climate change, leading to adverse climatic conditions such as drought and flooding. This paper explores the problem of fresh-water access in northern Kenya and measures being taken to safeguard water access despite these harsh climatic changes. We present an integrated water management and decision-support platform, eMaji Manager, that we developed and deployed in five ASAL counties in northern Kenya to manage waterpoint access for people and livestock. We then propose innovative machine learning methods for understanding waterpoint usage and repair patterns for sensor-instrumented waterpoints (e.g., boreholes). We explore sub-sequence discriminatory models and recurrent neural networks to predict water-point failures, improve repair response times and ultimately support continuous access to water.
2019
On Using Blockchain Based Workflows
N. Bore, A. Kinai, J. Mutahi, D. Kaguma, F. Otieno, S. L. Remy, K. Weldemariam
2019 IEEE International Conference on Blockchain and Cryptocurrency (ICBC)
Abstract
We have been involved in the development of several blockchain-based solutions that largely utilize workflows. Workflows are used to guide users from independent organizations to process and manage transactions, data and documents in a trusted, immutable, and transparent manner for all relevant entities on a given blockchain network. This work discusses our approach to automate the process of creating, updating, and using workflows for blockchain-based solutions. In particular, we present a workflow definition schema using existing templates. We also show how the workflow definition is used to automate the generation of graphical user interfaces and the possibility of generating associated blockchain smart contracts in the future.
2018
Forecasting Energy Demand for Microgrids Over Multiple Horizons
Fred Otieno, Nathan Williams, Patrick McSharry
2018 IEEE PES/IAS PowerAfrica
Abstract
Access to electricity is one of the key enablers of socioeconomic development in Sub-Saharan Africa. Microgrid solutions are currently playing an increasing role in providing access to electricity, especially to rural populations whose electricity is not supplied by the national grid. Microgrid developers need to manage their existing sites and expand to new regions. In order for them to manage this expansion effectively and sustainably, they need to make data-driven decisions. Having access to accurate forecasts of electricity demand at the site level is a key input in designing, managing and up-scaling microgrid solutions. Several forecasting mechanisms are proposed for such microgrid developers. Using daily energy consumption data from seven sites operating in Kenya during 2014-2017, it was established that exponential smoothing offers the best out-of-sample forecasting performance with forecast skill exhibited for horizons up to four months ahead.
2014
Leveraging Linked Data to Enhance Programmers' Access to Government Open Data
Isaiah Mulang, Fred Otieno
IST-Africa 2014 Conference Proceedings
