Weather prediction is paramount for many applications and scenarios, among them is agriculture. In order to efficiently irrigate the crops with the exact needed water amount, weather forecasting can be used to optimize the quantity of required irrigation water such that the crops are neither dried up nor over-irrigated. This paper proposes a Machine Learning (ML)-based weather forecasting model, which utilizes the Social Spider Algorithm-Least Square-Support Vector Machine (SSA-LS-SVM) algorithm. The simulation results are used to predict the prime weather and soil parameters such as the atmospheric temperature, pressure, and soil humidity for 24, 48, and 72 hours based on previous 39 days’ hourly data for Amman city. The predicted values showed low relative mean square errors compared with the actual values and the LS-SVM predictor.
FenteD.N. and SinghD.K., Weather forecasting using artificial neural network, In 2018 second international conference on inventive communication and computational technologies (ICICCT), pp. 1757–1761, IEEE, 2018.
2.
SinghS., MalikP. and AroraB., An effective weather forecasting using neural networks, International Journal of Emerging Engineering Research and Technology2(2) (2014)209–212.
3.
FAO, World Agriculture: Towards 2015/2030—An FAO Perspective; Earthscan Publications Ltd.: London, Uk, 2014.
4.
HaverkortA.J., Ancha Srinivasan (ed), Handbook of precision agriculture, Principles and applications, Euphytica156(1) (2007)269–270.
5.
FontenelleT., da CunhaW. and FucknerM., Atlas irrigação: uso da água na agricultura irrigada, Agência Nacional de Águas, Brasília, DF, Brasil, (2017).
6.
GS CamposN., RochaA.R., GondimR., Coelho da SilvaT.L. and GomesD.G., Smart & green: An internet-of-things framework for smart irrigation, Sensors20(1) (2020)190.
7.
VoutosY., MylonasP., KatheniotisJ. and SofouA., A survey on intelligent agricultural information handling methodologies, Sustainability11(12) (2019)3278.
BorgiaE., The Internet of Things vision: Key features, applications and open issues, Computer Communications54 (2014)1–31.
10.
DaskalakisS.N., GoussetisG., AssimonisS.D., TentzerisM.M. and GeorgiadisA., A uW backscatter-morse-leaf sensor for low-power agricultural wireless sensor networks, IEEE Sensors Journal18(19) (2018)7889–7898.
11.
GuruprasadhJ.P., HarshanandaA., KeerthanaI.K., KrishnanK.Y., RangarajanM. and SathyadevanS., Intelligent soil quality monitoring system for judicious irrigation, In 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 443–448, IEEE, 2017.
12.
ParraL.O.R.E.N.A., OrtuñoV., SendraS. and LloretJ.A.I.M.E., Low-cost conductivity sensor based on two coils, In Proceedings of the First International Conference on Computational Science and Engineering (CSE’13), Valencia, Spain, vol. 68, p. 107112, 2013.
13.
SendraS., ParraL., OrtuñoV., LloretJ. and De ValenciaU.P., A low cost turbidity sensor development, In Proceedings of the Seventh International Conference on Sensor Technologies and Applications (SENSORCOMM 2013), Barcelona, Spain, pp. 25–31. 2013.
14.
GarcíaL., ParraL., JimenezJ.M., LloretJ. and LorenzP., IoT-based smart irrigation systems: An overview on the recent trends on sensors and IoT systems for irrigation in precision agriculture, Sensors20(4), 1042.
15.
XieF. and TangD., Forecasting Model of Irrigation Water Requirement Based on Least Squares Support Vector Machine, 2010 International Conference on Intelligent Computation Technology and Automation, Changsha, 2010, pp. 335–338, doi: 10.1109/ICICTA.2010.91.
16.
XuH. and ZhuX., LS-SVM Based Two Step Commercial Short-Term Load Prediction, In Proceedings of the 3rd International Conference on Computer Science and Application Engineering (pp. 1–6).
17.
KhalifehA., Al-QammazA., DarabkhK.A., AbualigahL., KhasawnehA.M. and ZinonosZ., An AI Based Irrigation and Weather Forecasting System utilizing LoRaWAN and Cloud Computing Technologies, In 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus), pp. 443–448, IEEE, 2021.
18.
LavdasS., ZacharioudakisL., KhalifehA. and ZinonosZ., The Effect of Temperature and Humidity on Indoor LoRa Propagation Model, in 2021 17th International Conference on Distributed Computing in Sensor Systems (DCOSS), 2021, pp. 374–379.
19.
ShraidehS. and DarabkhK.A., Joint Channel and Spreading Factor Selection Algorithm for LoRaWAN Based Networks, in 2020 International Conference on UK-China Emerging Technologies (UCET), 2020, pp. 1–4.
20.
KhalifehA., AldahdouhK. and AlounehS., LoRaWAN Energy Optimization with Security Consideration, Int Arab J Inf Technol18 (2021)476–483.
21.
elD., Abou-TairD., BuechsensteinS. and KhalifehA., A fog computing-based framework for privacy preserving IoT environments, Int Arab J Inf Technol17(3) (2020)306–315.
22.
TanashR., KhalifehA. and DarabkhK.A., Communication over Cloud Computing: A Security Survey, In 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 496–501, IEEE, 2019.
23.
JamesJ.Q. and LiV.O., A social spider algorithm for global optimization, Applied Soft Computing30 (2015)614–627.
24.
SuykensJ.A. and VandewalleJ., Multiclass least squares support vector machines, In IJCNN’99, International Joint Conference on Neural Networks, Proceedings (Cat. No. 99CH36339), vol. 2, pp. 900–903, IEEE, 1999.
