{"title":"基于奇异谱分析的机器学习降雨预报模型的开发","authors":"P. Reddy, Sucharitha Yadala, S. Goddumarri","doi":"10.31436/iiumej.v23i1.1822","DOIUrl":null,"url":null,"abstract":"Agriculture is the key point for survival for developing nations like India. For farming, rainfall is generally significant. Rainfall updates are help for evaluate water assets, farming, ecosystems and hydrology. Nowadays rainfall anticipation has become a foremost issue. Forecast of rainfall offers attention to individuals and knows in advance about rainfall to avoid potential risk to shield their crop yields from severe rainfall. This study intends to investigate the dependability of integrating a data pre-processing technique called singular-spectrum-analysis (SSA) with supervised learning models called least-squares support vector regression (LS-SVR), and Random-Forest (RF), for rainfall prediction. Integrating SSA with LS-SVR and RF, the combined framework is designed and contrasted with the customary approaches (LS-SVR and RF). The presented frameworks were trained and tested utilizing a monthly climate dataset which is separated into 80:20 ratios for training and testing respectively. Performance of the model was assessed using Root Mean Square Error (RMSE) and Nash–Sutcliffe Efficiency (NSE) and the proposed model produces the values as 71.6 %, 90.2 % respectively. Experimental outcomes illustrate that the proposed model can productively predict the rainfall.\nABSTRAK:Pertanian adalah titik utama kelangsungan hidup negara-negara membangun seperti India. Untuk pertanian, curah hujan pada amnya ketara. Kemas kini hujan adalah bantuan untuk menilai aset air, pertanian, ekosistem dan hidrologi. Kini, jangkaan hujan telah menjadi isu utama. Ramalan hujan memberikan perhatian kepada individu dan mengetahui terlebih dahulu mengenai hujan untuk menghindari potensi risiko untuk melindungi hasil tanaman mereka dari hujan lebat. Kajian ini bertujuan untuk menyelidiki kebolehpercayaan mengintegrasikan teknik pra-pemprosesan data yang disebut analisis-spektrum tunggal (SSA) dengan model pembelajaran yang diawasi yang disebut regresi vektor sokongan paling rendah (LS-SVR), dan Random-Forest (RF), ramalan hujan. Menggabungkan SSA dengan LS-SVR dan RF, kerangka gabungan dirancang dan dibeza-bezakan dengan pendekatan biasa (LS-SVR dan RF). Kerangka kerja yang disajikan dilatih dan diuji dengan menggunakan set data iklim bulanan yang masing-masing dipisahkan menjadi nisbah 80:20 untuk latihan dan ujian. Prestasi model dinilai menggunakan Root Mean Square Error (RMSE) dan Nash – Sutcliffe Efficiency (NSE) dan model yang dicadangkan menghasilkan nilai masing-masing sebanyak 71.6%, 90.2%. 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引用次数: 17
摘要
农业是印度等发展中国家生存的关键。对于农业来说,降雨通常意义重大。降雨更新有助于评估水资产、农业、生态系统和水文。如今,降雨预测已成为一个首要问题。降雨预报提供了对个人的关注,并提前了解降雨量,以避免潜在的风险,保护他们的作物产量免受强降雨的影响。本研究旨在探讨将奇异谱分析(SSA)数据预处理技术与最小二乘支持向量回归(LS-SVR)和随机森林(RF)监督学习模型集成用于降雨预测的可靠性。将SSA与LS-SVR和RF相结合,设计了组合框架,并与传统方法(LS-SVR和RF)进行了对比。所提出的框架使用月度气候数据集进行训练和测试,该数据集分别分为80:20的比例进行训练和测试。使用均方根误差(RMSE)和纳什-萨克利夫效率(NSE)对模型的性能进行评估,所提出的模型分别产生71.6%和90.2%的值。实验结果表明,该模型能够有效地预测降雨。摘要:在印度,一种名叫“negara-negara -negara”的植物。Untuk pertanian, curah hujan pada amnya ketara。Kemas kini hujan adalah bantuan untuk,空气,大气,生态系统和水文。基尼,我想说的是,我想说的是我想说的。拉玛兰人,胡吉安人,胡吉安人,胡吉安人,胡吉安人,胡吉安人,胡吉安人,胡吉安人,胡吉安人,胡吉安人,胡吉安人。[3] [j] .数据杨分布分析-谱图分析(SSA) - dengan模型杨分布回归向量(LS-SVR), dan Random-Forest (RF), ramalan hujan.]孟加邦干SSA dengan LS-SVR dan RF, kerangka gabungan dirancang dan dibeza-bezakan dengan pendekatan biasa (LS-SVR dan RF)。Kerangka kerja yang disajikan dilatih dan diuji dengan menggunakan set数据klim bulanan yang masing-masing dipisahkan menjadi nisbah 80:20 untuk latihan dan ujian。Prestasi模型均方根误差(RMSE) dan Nash - Sutcliffe效率(NSE) dan模型yang dicadangkan menghasilkan nilai masing-masing sebanyak分别为71.6%、90.2%。哈西尔实验,孟甘巴坎巴哈瓦模型,杨迪卡丹坎帕特,梅拉马尔坎,胡甘斯卡拉产品。
DEVELOPMENT OF RAINFALL FORECASTING MODEL USING MACHINE LEARNING WITH SINGULAR SPECTRUM ANALYSIS
Agriculture is the key point for survival for developing nations like India. For farming, rainfall is generally significant. Rainfall updates are help for evaluate water assets, farming, ecosystems and hydrology. Nowadays rainfall anticipation has become a foremost issue. Forecast of rainfall offers attention to individuals and knows in advance about rainfall to avoid potential risk to shield their crop yields from severe rainfall. This study intends to investigate the dependability of integrating a data pre-processing technique called singular-spectrum-analysis (SSA) with supervised learning models called least-squares support vector regression (LS-SVR), and Random-Forest (RF), for rainfall prediction. Integrating SSA with LS-SVR and RF, the combined framework is designed and contrasted with the customary approaches (LS-SVR and RF). The presented frameworks were trained and tested utilizing a monthly climate dataset which is separated into 80:20 ratios for training and testing respectively. Performance of the model was assessed using Root Mean Square Error (RMSE) and Nash–Sutcliffe Efficiency (NSE) and the proposed model produces the values as 71.6 %, 90.2 % respectively. Experimental outcomes illustrate that the proposed model can productively predict the rainfall.
ABSTRAK:Pertanian adalah titik utama kelangsungan hidup negara-negara membangun seperti India. Untuk pertanian, curah hujan pada amnya ketara. Kemas kini hujan adalah bantuan untuk menilai aset air, pertanian, ekosistem dan hidrologi. Kini, jangkaan hujan telah menjadi isu utama. Ramalan hujan memberikan perhatian kepada individu dan mengetahui terlebih dahulu mengenai hujan untuk menghindari potensi risiko untuk melindungi hasil tanaman mereka dari hujan lebat. Kajian ini bertujuan untuk menyelidiki kebolehpercayaan mengintegrasikan teknik pra-pemprosesan data yang disebut analisis-spektrum tunggal (SSA) dengan model pembelajaran yang diawasi yang disebut regresi vektor sokongan paling rendah (LS-SVR), dan Random-Forest (RF), ramalan hujan. Menggabungkan SSA dengan LS-SVR dan RF, kerangka gabungan dirancang dan dibeza-bezakan dengan pendekatan biasa (LS-SVR dan RF). Kerangka kerja yang disajikan dilatih dan diuji dengan menggunakan set data iklim bulanan yang masing-masing dipisahkan menjadi nisbah 80:20 untuk latihan dan ujian. Prestasi model dinilai menggunakan Root Mean Square Error (RMSE) dan Nash – Sutcliffe Efficiency (NSE) dan model yang dicadangkan menghasilkan nilai masing-masing sebanyak 71.6%, 90.2%. Hasil eksperimen menggambarkan bahawa model yang dicadangkan dapat meramalkan hujan secara produktif.
期刊介绍:
The IIUM Engineering Journal, published biannually (June and December), is a peer-reviewed open-access journal of the Faculty of Engineering, International Islamic University Malaysia (IIUM). The IIUM Engineering Journal publishes original research findings as regular papers, review papers (by invitation). The Journal provides a platform for Engineers, Researchers, Academicians, and Practitioners who are highly motivated in contributing to the Engineering disciplines, and Applied Sciences. It also welcomes contributions that address solutions to the specific challenges of the developing world, and address science and technology issues from an Islamic and multidisciplinary perspective. Subject areas suitable for publication are as follows: -Chemical and Biotechnology Engineering -Civil and Environmental Engineering -Computer Science and Information Technology -Electrical, Computer, and Communications Engineering -Engineering Mathematics and Applied Science -Materials and Manufacturing Engineering -Mechanical and Aerospace Engineering -Mechatronics and Automation Engineering