Big Data Analytics for Improved Weather Forecasting and Disaster Management

Weather forecasting and agriculture is an interdisciplinary research programme based on advancements in several domains. Earth temperature is anticipated to rise by 2 °C by 2050, putting up to 50 million people at danger of starvation owing to agricultural consequences. Lot of models have been made to detect changes and warn of potentially dangerous circumstances in future. Data mining methods and methodologies are more effective than traditional ones. Convolutional Neural Networks and Long Short-Term Memory Networks envisage weather radar echo images with various radar echo shapes depicting various terrible weather situations moulding forecast to estimate future changes to avert agricultural and natural disaster circumstances and minimize economic loss and impact too. WSN-based systems also assist in avoiding such things. Internet of Thing-based Agribot (IOT-Agribot) is playing a crucial role in precision agriculture, natural disaster as well as in monitoring. Big data not only critical in agriculture but also important in tourism, airport operations, mining sector, and power generation in forecasting changes. Because of advancements in weather monitoring systems and the rapid increase in meteorological data, weather forecasting entered into new era. Using contemporary big data analytic (BDA) techniques and new tools and technology, now it is feasible to forecast in the beginning or change in severity of diseases and pests of crops. Deep learning is excellent application in numerous domains has spurred its usage in forecasting makes a significant advance. In future, transfer learning and standard data augmentation are insufficient, and generative adversarial networks (GANs) are employed to overcome data scarcity problem in agricultural computer vision. GAN may generate images with synthetic “real” cropping rather than just rotating or adding noise. Big data is the future to secure the future in various aspects. Thus, this chapter includes the application and its future employment with its limitations to overcome.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime

Buy Now

eBook EUR 117.69 Price includes VAT (France)

Hardcover Book EUR 147.69 Price includes VAT (France)

Tax calculation will be finalised at checkout

Purchases are for personal use only

References

• Abkenar SB, Kashani MH, Mahdipour E, Jameii SM (2021) Big data analytics meets social media: a systematic review of techniques, open issues, and future directions. Telemat Inform 57:101517 ArticleGoogle Scholar
• Balti H, Abbes AB, Mellouli N, Farah IR, Sang Y, Lamolle M (2020) A review of drought monitoring with big data: issues, methods, challenges and research directions. Ecol Inform 60:101136 ArticleGoogle Scholar
• Bendre MR, Thool RC, Thool VR (2015) Big data in precision agriculture: Weather forecasting for future farming. In: 2015 1st international conference on next generation computing technologies (NGCT). IEEE, pp 744–750 Google Scholar
• Beresford RM, Mackay, AH (2012) Climate change impacts on plant diseases affecting New Zealand horticulture. Ministry for Primary Industries, Wellington Google Scholar
• Bhuvaneswari V (2022) Role of big data analytics in industrial revolution 4.0. In: Big data applications in industry 4.0. Auerbach Publications, pp 85–105 Google Scholar
• Blackburn R, Lurz K, Priese B, Göb R, Darkow IL (2015) A predictive analytics approach for demand forecasting in the process industry. Int Trans Oper Res 22(3):407–428 ArticleGoogle Scholar
• Braman LM, van Aalst MK, Mason SJ, Suarez P, Ait-Chellouche Y, Tall A (2013) Climate forecasts in disaster management: red cross food operations in West Africa, 2008. Disasters 37(1):144–164 ArticleGoogle Scholar
• Casas DM, González JÁT, Rodríguez JEA, Pet JV (2009) Using data-mining for short-term rainfall forecasting. In: International work-conference on artificial neural networks. Springer, Berlin, pp 487–490 Google Scholar
• Cheng Y, Zheng Z, Wang J, Yang L, Wan S (2019) Attribute reduction based on genetic algorithm for the coevolution of meteorological data in the industrial internet of things. Wirel Commun Mob Comput Google Scholar
• Collier N (2012) Uncovering text mining: a survey of current work on web-based epidemic intelligence. Glob Public Health 7(7):731–749 ArticleGoogle Scholar
• Cramer S, Kampouridis M, Freitas A (2016) A genetic decomposition algorithm for predicting rainfall within financial weather derivatives. In: Proceedings of the genetic and evolutionary computation conference, pp 885–892 Google Scholar
• Davenport TH, Harris JG, De Long DW, Jacobson AL (2001) Data to knowledge to results: building an analytic capability. Calif Manage Rev 43(2):117–138 ArticleGoogle Scholar
• Dutta D, Bose I (2015) Managing a big data project: the case of ramco cements limited. Int J Prod Econ 165:293–306 ArticleGoogle Scholar
• Fathi M, Haghi Kashani M, Jameii S M, Mahdipour E (2021) Big data analytics in weather forecasting: a systematic review. Arch Comput Methods Eng 1–29 Google Scholar
• Fenu G, Malloci FM (2019) An application of machine learning technique in forecasting crop disease. In: Proceedings of the 2019 3rd international conference on big data research, pp 76–82 Google Scholar
• Frechtling D (2012) Forecasting tourism demand. Routledge BookGoogle Scholar
• Hazyuk I, Ghiaus C, Penhouet D (2012) Optimal temperature control of intermittently heated buildings using model predictive control: part I-building modeling. Build Environ 51:379–387 ArticleGoogle Scholar
• Jaber MM, Ali MH, Abd SK, Jassim MM, Alkhayyat A, Aziz HW, Alkhuwaylidee AR (2022) Predicting climate factors based on big data analytics based agricultural disaster management. Phys Chem Earth 128:103243 ArticleGoogle Scholar
• Jain H, Jain R (2017) Big data in weather forecasting: applications and challenges. In: 2017 International conference on big data analytics and computational intelligence (ICBDAC). IEEE, pp 138–142 Google Scholar
• Kang Y, Park I, Rhee J, Lee Y (2015) MongoDB-based repository design for IoT-generated RFID/sensor big data. IEEE Sens J 16(2):485–497 ArticleGoogle Scholar
• Laurenson MR, Manktelow DW (1992) Monitor: a computer-based tool for monitoring apple black spot infection periods. In: III International symposium on computer modelling in fruit research and orchard management, vol 313, pp 197–204 Google Scholar
• Leu JS, Su KW, Chen CT (2014) Ambient mesoscale weather forecasting system featuring mobile augmented reality. Multimed Tools Appl 72(2):1585–1609 ArticleGoogle Scholar
• Li J, Xu L, Tang L, Wang S, Li L (2018) Big data in tourism research: a literature review. Tour Manag 68:301–323 ArticleGoogle Scholar
• Li K, Liu YS (2005) A rough set based fuzzy neural network algorithm for weather prediction. In: 2005 international conference on machine learning and cybernetics, vol 3. IEEE, pp 1888–1892 Google Scholar
• Lu Y, Chen D, Olaniyi E, Huang Y (2022) Generative adversarial networks (GANs) for image augmentation in agriculture: a systematic review. Comput Electron Agric 200:107208 ArticleGoogle Scholar
• Mawson VJ, Hughes BR (2020) Deep learning techniques for energy forecasting and condition monitoring in the manufacturing sector. Energy Build 217:109966 ArticleGoogle Scholar
• Meixell MJ, Wu SD (2001) Scenario analysis of demand in a technology market using leading indicators. IEEE Trans Semicond Manuf 14(1):65–75 ArticleGoogle Scholar
• Mittal S, Sangwan OP (2018) Big data analytics using data mining techniques: a survey. In: International conference on advanced informatics for computing research. Springer, Singapore, pp 264–273 Google Scholar
• Pandey, K., Singh, K.G., and Singh, A (2023) Multi-Sensors based smart nutrient reuse management system for closed soilless culture under protected cultivation. Comput Electron Agric 107495 Google Scholar
• Pathak AR, Pandey M, Rautaray S (2018) Construing the big data based on taxonomy, analytics and approaches. Iran J Comput Sci 1(4):237–259 ArticleGoogle Scholar
• Poornima S, Pushpalatha M (2018) A survey of predictive analytics using big data with data mining. IJBRA 14(3):269–282 ArticleGoogle Scholar
• Radke AM, Tseng MM (2015) Design considerations for building distributed supply chain management systems based on cloud computing. J Manuf Sci Eng 137(4) Google Scholar
• Rathore MMU, Paul A, Ahmad A, Chen BW, Huang B, Ji W (2015) Real-time big data analytical architecture for remote sensing application. IEEE J STARS 8(10):4610–4621 Google Scholar
• Reddy PC, Babu AS (2017) Survey on weather prediction using big data analytics. In: 2017 Second international conference on electrical, computer and communication technologies (ICECCT), pp 1–6 Google Scholar
• Richey RG, Morgan TR, Lindsey-Hall K, Adams FG (2016) A global exploration of big data in the supply chain. Int J Phys Distrib Logist Manag Google Scholar
• Ristaino JB, Anderson PK, Bebber DP, Brauman KA, Cunniffe NJ, Fedoroff NV, Wei Q (2021) The persistent threat of emerging plant disease pandemics to global food security. Proc Natl Acad Sci 118(23):e2022239118 ArticleCASGoogle Scholar
• Saneja B, Rani R (2018) A hybrid approach for outlier detection in weather sensor data. In: 2018 IEEE 8th international advance computing conference (IACC), IEEE, pp 321–326 Google Scholar
• Sarker MNI, Peng Y, Yiran C, Shouse RC (2020) Disaster resilience through big data: way to environmental sustainability. IJDRR 51:101769 Google Scholar
• Seah M, Hsieh MH, Weng PD (2010) A case analysis of Savecom: the role of indigenous leadership in implementing a business intelligence system. IJIM 30(4):368–373 Google Scholar
• Tsai CW, Lai CF, Chao HC, Vasilakos AV (2015) Big data analytics: a survey. J Big Data 2(1):1–32 ArticleGoogle Scholar
• Rehman MH, Chang V, Batool A, Wah TY (2016) Big data reduction framework for value creation in sustainable enterprises. IJIM 36(6):917–928 Google Scholar
• Waller MA, Fawcett SE (2013) Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. J Bus Logist 34(2):77–84 ArticleGoogle Scholar
• Xiao Z, Liu B, Liu H, Zhang D (2012) Progress in climate prediction and weather forecast operations in China. Adv Atmos Sci 29(5):943–957 ArticleGoogle Scholar
• Xu XM, Butt DJ (1993) PC-based disease warning systems for use by apple growers 1. EPPO Bulletin 23(4):595–600 ArticleGoogle Scholar
• Yu M, Yang C, Li Y (2018) Big data in natural disaster management: a review. Geosci 8(5):165 ArticleGoogle Scholar
• Zhang B, Tang L, Roemer M (2017a) Probabilistic planning and risk evaluation based on ensemble weather forecasting. IEEE Trans Autom 15(2):556–566 ArticleGoogle Scholar
• Zhang P, Gao J, Thomas AG, Alagupackiam KP, Mannava K, Bosco PI, Chiao S (2017b) On building a big data analysis system for California drought. In: 2017 IEEE third international conference on big data computing service and applications (BigDataService), IEEE, pp 49–156 Google Scholar
• Zhou K, Fu C, Yang S (2016) Big data driven smart energy management: from big data to big insights. Renew Sustain Energy Rev 56:215–225 ArticleGoogle Scholar

Author information

Authors and Affiliations

1. Department of Plant Pathology, University of Agricultural Sciences, GKVK, Bengaluru, Karnataka, 560065, India Gaurav Y. Rakhonde, Namburi Karunakar Reddy & Pooja Purushotham
2. Department of Plant Pathology, Punjab Agricultural University, Ludhiana, Punjab, 141004, India Shalaka Ahale
3. Department of Horticulture, University of Agricultural Sciences, GKVK, Bengaluru, Karnataka, 560065, India Ananya Deshkar

1. Gaurav Y. Rakhonde