DOI: https://doi.org/10.18517/ijods.5.1.19-32.2024
The Performance of Drought Indices on Maize Production in Northern Nigeria Using Artificial Neural Network Model
Abstract
Drought is widely known to put the ecosystem at risk. It ensues when there is a major rainfall shortage that causes hydrological discrepancies and alters the land productive structures. The degree of rainfall influences the growth and harvests of maize, particularly where irrigation is not practicable. In some parts of northern Nigeria, rainfall is unpredictable and often lower than the quantity needed for a viable crop. For the detection, classification, and control of drought conditions, drought indices are used. There has been notable progress in the last few years in terms of modelling droughts by utilizing statistical or physical models. Despite the successes documented by most of these approaches; a plain, effective, and well-built statistical model is the artificial neural network (ANN). The use of artificial neural networks (ANN) to evaluate the impact of drought indices on maize output in the 17 northern Nigerian states is presented in this research. For a 25-year period from 1993 to 2018, observed annual data of drought indices, RDI, and the Palmer drought indices, which comprise SCPDSI, SCPHDI, and SCWLPM, as well as maize yield (measured in tonnes) in Northern states of Nigeria. The ANN model was evaluated using several activation functions (sigmoid, hyperbolic tangent, and rectified linear unit), hidden layers (1, 2, and 3), and training sets (70%, 80%, and 90%). The Mean Square Error (MSE) was employed to evaluate each ANN model's performance. In summary, most of the states' lowest mean square errors (MSEs) were generated via RELU. Also, as the training percentage increases, the mean square error increases.
Article Details
References
Barua S, Ng AWM, Perera BJC. (2012). Artificial neural network-based drought forecasting using a non-linear aggregated drought index. J Hydrol Eng; 17(12): 1408- 1413.
Bates, B.C., Kundzewicz, Z.W., Wu, S.and Palutikof, J.P. (Eds). (2008). Climate Change and Water. Technical Paper of the Intergovernmental Panel on Climate Change, IPCC Secretariat, Geneva. Available from https://www.ipcc.ch/pdf/technical-papers/climate-change-water-en.pdf - 28/doc13.pdf (accessed: June, 2018).
Birikundavyi S, Labib R, Trung HT, Rousselle J. (2002). Performance of neural networks in daily streamflow forecasting. J Hydrolog Eng; 7(5): 392- 398.
Dai A. (2011). Drought under global warming: a review. Wiley Interdisciplinary Reviews: Climate Change 2 (1), 45–65.
Deo RC, Sahin M. (2015). Application of the artificial neural network model for prediction of monthly standardized precipitation and evapotranspiration index using hydrometeorological parameters and climate indices in eastern Australia. Atmos Res. 2015; 161(162): 65- 81. https://doi.org/10.1016/j.atmosres.2015.03.018.
Deo RC, Sahin M. (2015). Application of the artificial neural network model for prediction of monthly standardized precipitation and evapotranspiration index using hydrometeorological parameters and climate indices in eastern Australia. Atmos Res. 2015; 161(162): 65- 81. https://doi.org/10.1016/j.atmosres.2015.03.018.
Eze, J.N. (2017). Assessment of Household Vulnerability and Adaptation to Desertification in Yobe State, Nigeria, A thesis submitted to the School of Postgraduate studies and the Department of Geography. Nsukka in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy: University of Nigeria.
Eze, J.N., U. Aliyu, A. Alhaji-Baba, and M. Alfa. (2018). Analysis of farmers’ vulnerability to climate change in Niger state, Nigeria. International Letters of Social and Humanistic Sciences. 82: 1–9.
FAOSTAT. (2018). Production Statistics (Prodstat); Food and Agriculture Organization of the United Nations: Rome, Italy.
Fung KF, Huang YF, Koo CH, Mirzaei M. (2019). Improved SVR machine learning models for agricultural drought prediction at downstream of Langat River Basin. Malaysia J Water Climate Change; 10(2): 1– 16. https://doi.org/10.2166/wcc.2019.295.
Ghumman AR, Ghazaw YM, Sohail AR, Watanabe K. (2011). Runoff forecasting by artificial neural network and conventional model. Alex Eng J; 50: 345- 350.
Gidey, E., O. Dikinya, R. Sebego, E. Segosebe, and A. Zenebe. (2018). Analysis of the long-term agricultural drought onset, cessation, duration, frequency, severity and spatial extent using vegetation health index (VHI) in Raya and its environs, northern Ethiopia. Environmental Research System 7 (13). https://doi.org/10.1186/s40068-018-0115-z.
Hai T, Sharafati A, Mohammed A, et al. (2020). Global solar radiation estimation and climatic variability analysis using extreme learning machine based predictive model. IEEE Access. 2020; 14(8): 12026– 12042. https://doi.org/10.1109/ACCESS.2020.2965303.
Kamara A.Y., Kamai N., Omoigui L.O., Togola A., and Onyibe J.E. (2020). Guide to Maize Production in Northern Nigeria: lbadan, Nigeria. 18 pp.
Kamara, A.Y., S.U. Ewansiha, and A. Menkir. (2014). Assessment of nitrogen uptake and utilization in drought-tolerant and Striga resistant tropical maize varieties. Archives of Agronomy and Soil Science 60: 195–207. doi:10.1080 /03650340.2013.783204
Leste, RB. (2006). The earth is shrinking: Advancing deserts and rising seas squeezing civilization. Earth Policy Institute.
Liu, Xianfeng, Xiufang ZHU, Yaozhong PAN, Jianjun BAI, and Shuangshuang LI. (2018). Performance of different drought indices for agriculture drought in the North China plain. Journal of Arid Land. https://doi.org/10.1007/s40333-018-0005-2.
Morid S, Smakhtin V, Bagherzadeh K. (2007). Drought forecasting using artificial neural networks and time series of drought indices. Int J Climatol; 27: 2103- 2111.
Nyong AF, Adesina, Elasha BO (2007). The Value of Indegenous Knowledge in Climate change Mitigation and Adaptation Strategies in the African Sahel. Mitigation and Adaptation Strategies for Global Change 12: 787-797.
Shiru, M.S., S. Shahid, N. Alias, and E.S. Chung. (2018). Trend analysis of droughts during crop growing seasons of Nigeria. Sustainability 10 (871): 1–13. https://doi.org/10.3390/su10030871.
Tiamiyu, S.A., J.N. Eze, T.M. Yusuf, A.T. Maji, and S.O. Bakare. (2015). Rainfall variability and its effect on yield of Rice in Nigeria. International Letters of Natural Sciences 49: 63–68.
Tsakiris, G., and Vangelis, H. (2005). “Establishing a drought index incorporating evapotranspiration”. European Water, 9 (10): 3–11.
Um, M., Y. Kim, D. Park, and J. Kim. (2017). Effects of different reference periods on drought index estimations from 1901 to 2014. Hydrology and Earth System Sciences 21: 4989–5007.
Wang W, Van Gelder P, Vrijling JK, Ma J. (2006). Forecasting daily streamflow using hybrid ANN models. J Hydrol; 324: 383- 399.
Yue, Y., S. Shen, and Q. Wang. (2018). Trend and variability in droughts in Northeast China based on the reconnaissance drought index. Water 10 (318): 1–17.
Faiz, M. A., Zhang, Y., Ma, N., Baig, F., Naz, F., & Niaz, Y. (2021). Drought indices: aggregation is necessary or is it only the researcher’s choice? Water Supply, 00(0), 1–16. https://doi.org/10.2166/ws.2021.163
Sarki, Ahmed & Roni, Babangida. (2019). This disease is “not for hospital”: myths and misconceptions about cancers in Northern Nigeria. Journal of Global Health Reports. 3. 10.29392/joghr.3.e2019070.