Browsing by Author "Ogunrinde, Akinwale T."

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    Application of artificial neural network for forecasting standardized precipitation and evapotranspiration index: A case study of Nigeria
    (John Wiley & Sons, Inc., 2020-07) Ogunrinde, Akinwale T.; Oguntunde, Phillip G.; Fasinmirin, Johnson T.; Akinwumiju, Akinola S.
    The necessity to perform an accurate prediction of future characteristics of drought requires a robust and efficient technique that can deduce from historical data the stochastic relationship or dependency between history and future. In this study, the applicability of the artificial neural network (ANN) is used for forecasting the standardized precipitation and evapotranspiration index (SPEI) at 12-month timescale for five candidate stations in Nigeria using predictive variable data from 1985 to 2008 (training) and tested data between 2009 and 2015. The predictive variables are monthly average precipitation, average air temperature, maximum temperature, minimum temperature, mean speed, mean solar radiation, sunshine hours, and two large-scale climate indices (Southern Oscillation Index and North Atlantic Oscillation). From the several combinations of the input variables, training algorithms, hidden, and output transfer functions, a total of eight model runs stood out using a three-layer ANN network. The most efficient ANN model architecture had 9,8,1 as the input, hidden, and output neurons, respectively, trained using the Levenberg-Marquardt training algorithm and tansig as the activation and hidden transfer functions. Assessment on the efficiency of the model based on statistics indicate that the coefficient of determination, root mean square error, Nash-Sutcliffe coefficient of efficiency and the mean absolute error ranges between 0.51 and 0.82; 0.57 and 0.75; 0.28 and 0.79; 0.44 and 0.56, respectively, during the testing period. The output of these findings shows that ANN modeling technique can play a significant role as a data-driven model in forecasting monthly SPEI time series and drought characteristics in the study area, thereby leading to the development of an early warning system for the country
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    Evaluation of Evapotranspiration Prediction for Cassava Crop Using Artificial Neural Network Models and Empirical Models over Cross River Basin in Nigeria
    (MDPI, 2025-01-01) Eludire, Oluwadamilare Oluwasegun; Faloye, Oluwaseun Temitope; Alatise, Michael; Ajayi, Ayodele Ebenezer; Oguntunde, Phillip G.; Badmus, Tayo; Fashina, Abayomi; Adeyeri, Oluwafemi E.; Olorunfemi, Idowu Ezekiel; Ogunrinde, Akinwale T.
    first_pagesettingsOrder Article Reprints Open AccessArticle Evaluation of Evapotranspiration Prediction for Cassava Crop Using Artificial Neural Network Models and Empirical Models over Cross River Basin in Nigeria by Oluwadamilare Oluwasegun Eludire 1,2,Oluwaseun Temitope Faloye 3,4,*ORCID,Michael Alatise 2,Ayodele Ebenezer Ajayi 2,4,5,Philip Oguntunde 2,Tayo Badmus 1,Abayomi Fashina 6,Oluwafemi E. Adeyeri 7,*ORCID,Idowu Ezekiel Olorunfemi 8ORCID andAkinwale T. Ogunrinde 9 1 Department of Agricultural and Bioresources Engineering, Faculty of Engineering and Technology, University of Calabar, Calabar PMB 1115, Nigeria 2 Department of Agricultural and Environmental Engineering, Federal University of Technology, Akure PMB 704, Nigeria 3 Department of Water Resources Management and Agrometeorology, Federal University, Oye-Ekiti PMB 373, Nigeria 4 Institute for Plant Nutrition and Soil Science, Christian Albrecht’s University zu Kiel, Hermann Rodewaldstr. 2, 24118 Kiel, Germany 5 Institute for Fourth Industrial Revolution, SE Bogoro Centre, Afe Babalola University, Ado Ekiti 360001, Nigeria 6 Department of Soil Science and Land Resources Management, Federal University, PMB 373, Oye-Ekiti 371104, Nigeria 7 School of Energy and Environment, City University of Hong Kong, Kowloon, Hong Kong 8 Department of Civil Engineering, Lead City University Ibadan, Ibadan 200255, Nigeria 9 Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Beijing 100045, China * Authors to whom correspondence should be addressed. Water 2025, 17(1), 87; https://doi.org/10.3390/w17010087 Submission received: 28 August 2024 / Revised: 5 October 2024 / Accepted: 8 October 2024 / Published: 1 January 2025 (This article belongs to the Section Water, Agriculture and Aquaculture) Editorial Note: Due to an editorial processing error, this article was incorrectly included within the Special Issue Crop Evapotranspiration, Crop Irrigation and Water Savings upon publication. This article was removed from this Special Issue’s webpage on 14 February 2025 but remains within the regular issue in which it was originally published. The editorial office confirms that this article adhered to MDPI's standard editorial process (https://www.mdpi.com/editorial_process). Downloadkeyboard_arrow_down Browse Figures Versions Notes Abstract The accurate assessment of water availability throughout the cassava cropping season (the initial, developmental, mid-season, and late stages) is crucial for mitigating the impacts of climate change on crop production. Using the Mann–Kendall Test, we investigated the trends in rainfall and cassava crop evapotranspiration (ETc) within the Cross River basin in Nigeria. Reference evapotranspiration (ETo) was based on two approaches, namely Artificial Neural Network (ANN) modelling and three established empirical models—the Penman–Monteith (considered the standard method), Blaney–Morin–Nigeria (BMN), and Hargreaves–Samani (HAG) models. ANN predictions were performed by using inputs from BMN and HAG parameters, denoted as BMN-ANN and HAG-ANN, respectively. The results from the ANN models were compared to those obtained from the Penman–Monteith method. Remotely sensed meteorological data spanning 39 years (1979–2017) were acquired from the Climatic Research Unit (CRU) to estimate ETc, while cassava yield data were acquired from the International Institute of Tropical Agriculture (IITA), Ibadan. The study revealed a significant upward trend in cassava crop ETc over the study period. Additionally, the ANN models outperformed the empirical models in terms of prediction accuracy. The BMN-ANN model with a Tansig activation function and a 3-3-1 architecture (number of input neurons, hidden layers, and output neurons) achieved the highest performance, with a coefficient of determination (R2) of 0.9890, a root mean square error (RMSE) of 0.000056 mm/day, and a Willmott’s index of agreement (d) of 0.9960. There is a decreasing trend in cassava yield in the region and further analysis indicated potential average daily water deficits of approximately −1.1 mm/day during the developmental stage. These deficits could potentially hinder root biomass, yield, and overall cassava yield in the Cross River basin. Our findings highlight the effectiveness of ANN modelling for irrigation planning, especially in the face of a worsening climate change scenario.