A novel approach for weather prediction for agriculture in Sri Lanka using Machine Learning techniques

J. Premachandra, Ppnv Kumara
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引用次数: 4

Abstract

Climate variability in recent years has critically affected the usual aspects of human lives, where the agriculture sector can be considered as one of the most vulnerable. Sri Lanka is also facing these climate changes over the past few decades. It has resulted in rainfall pattern changes where the expected rain may not occur during the expected time and amount. The mismatch between the rainfall pattern and traditional seasonal cultivation schedule has critically affected the agricultural sustainability. Even with the current technological advancements, weather prediction is one of the most technically and scientifically challenging tasks. This paper presents a novel machine learning-based approach for predicting rainfall for precision agriculture in Sri Lanka and it can be recognized as the first attempt to validate machine learning models to predict the weather in Sri Lankan context for precision agriculture. By analyzing the nature of the weather in Sri Lanka, the relationship of weather attributes with agriculture, availability, and accessibility, seven attributes are selected including rain gauge, relative humidity, average temperature, wind speed, wind direction where solar radiation and ozone concentration are uniquely selected for Sri Lankan context. For the prediction model, cross-validated data are trained and tested with four machine learning algorithms: Multiple Linear Regression, K-Nearest Neighbors, Support Vector Machine, and Random Forest. Currently, Support Vector Machine, K-Nearest Neighbors models have achieved accuracies of 88.57%, 88.66%. Random Forest has been recognized as the best-fitted model with 89.16% accuracy. The results depict a significant accuracy in this novel approach for Sri Lankan weather prediction.
一种利用机器学习技术为斯里兰卡农业进行天气预报的新方法
近年来,气候变化严重影响了人类生活的各个方面,其中农业部门可被视为最脆弱的部门之一。在过去的几十年里,斯里兰卡也面临着这些气候变化。它导致降雨模式改变,预期的降雨可能不会在预期的时间和数量内发生。降雨模式与传统季节耕作计划的不匹配严重影响了农业的可持续性。即使在目前的技术进步下,天气预报仍然是技术和科学上最具挑战性的任务之一。本文提出了一种新的基于机器学习的方法来预测斯里兰卡精准农业的降雨,它可以被认为是第一次尝试验证机器学习模型来预测斯里兰卡精准农业的天气。通过分析斯里兰卡的天气性质,天气属性与农业、可用性和可及性的关系,选择了七个属性,包括雨量计、相对湿度、平均温度、风速、风向,其中太阳辐射和臭氧浓度是斯里兰卡环境中唯一选择的。对于预测模型,交叉验证的数据使用四种机器学习算法进行训练和测试:多元线性回归,k近邻,支持向量机和随机森林。目前,支持向量机、k近邻模型的准确率分别达到了88.57%、88.66%。随机森林被认为是最适合的模型,准确率为89.16%。结果表明,这种新方法对斯里兰卡天气预报具有显著的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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