Ramya Nemani¹, G. Jose Moses², Fayadh Alenezi³, K. Vijaya Kumar⁴, Seifedine Kadry^5,6,7,*, Jungeun Kim⁸, Keejun Han⁹

Computer Systems Science and Engineering, Vol.47, No.1, pp. 919-935, 2023, DOI:10.32604/csse.2023.034213

Abstract Statistics are most crucial than ever due to the accessibility of huge counts of data from several domains such as finance, medicine, science, engineering, and so on. Statistical data mining (SDM) is an interdisciplinary domain that examines huge existing databases to discover patterns and connections from the data. It varies in classical statistics on the size of datasets and on the detail that the data could not primarily be gathered based on some experimental strategy but conversely for other resolves. Thus, this paper introduces an effective statistical Data Mining for Intelligent Rainfall Prediction using Slime Mould Optimization with Deep Learning… More >

El-Sayed M. El-kenawy¹, Abdelaziz A. Abdelhamid^2,3, Fadwa Alrowais^4,*, Mostafa Abotaleb⁵, Abdelhameed Ibrahim⁶, Doaa Sami Khafaga⁴

Computer Systems Science and Engineering, Vol.45, No.3, pp. 2885-2899, 2023, DOI:10.32604/csse.2023.034206

Abstract Rainfall plays a significant role in managing the water level in the reservoir. The unpredictable amount of rainfall due to the climate change can cause either overflow or dry in the reservoir. Many individuals, especially those in the agricultural sector, rely on rain forecasts. Forecasting rainfall is challenging because of the changing nature of the weather. The area of Jimma in southwest Oromia, Ethiopia is the subject of this research, which aims to develop a rainfall forecasting model. To estimate Jimma’s daily rainfall, we propose a novel approach based on optimizing the parameters of long short-term memory (LSTM) using Al-Biruni… More >

Shaojie Feng^*, Leipeng Liu, Chen Gao, Hang Hu

FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.5, pp. 1189-1201, 2023, DOI:10.32604/fdmp.2023.023637

Abstract The present study considers the damage mechanisms and the rainfall infiltration process responsible for landslide phenomena which originate from accumulation slopes. Accordingly, a physical test model is developed for different slopes and different rainfall conditions. Moreover, a three-dimensional laser scanner and a camera are used to monitor the slope erosion and the landslide dynamic evolution. Using this approach, the time variation curves of volumetric water content, pore water pressure, soil pressure, slope deformation, and damage are determined. The results show that under similar conditions, similar trends of the pore water pressure are achieved for different slopes and rainfall intensities. More > Graphic Abstract

Mohammad Alamgeer¹, Amal Al-Rasheed², Ahmad Alhindi³, Manar Ahmed Hamza^4,*, Abdelwahed Motwakel⁴, Mohamed I. Eldesouki⁵

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2725-2738, 2023, DOI:10.32604/cmc.2023.029163

Abstract Data mining process involves a number of steps from data collection to visualization to identify useful data from massive data set. the same time, the recent advances of machine learning (ML) and deep learning (DL) models can be utilized for effectual rainfall prediction. With this motivation, this article develops a novel comprehensive oppositional moth flame optimization with deep learning for rainfall prediction (COMFO-DLRP) Technique. The proposed CMFO-DLRP model mainly intends to predict the rainfall and thereby determine the environmental changes. Primarily, data pre-processing and correlation matrix (CM) based feature selection processes are carried out. In addition, deep belief network (DBN)… More >

Wen Zhang^1,2, Wei Fang^1,3,*, Xuelei Jia^1,2, Victor S. Sheng⁴

Journal of Quantum Computing, Vol.4, No.1, pp. 23-37, 2022, DOI:10.32604/jqc.2022.026975

Abstract In order to solve the rainfall estimation error caused by various noise factors such as clutter, super refraction, and raindrops during the detection process of Doppler weather radar. This paper proposes to improve the rainfall estimation model of radar combined with rain gauge which calibrated by common Kalman filter. After data preprocessing, the radar data should be classified according to the precipitation intensity. And then, they are respectively substituted into the improved filter for calibration. The state noise variance and the measurement noise variance can be adaptively calculated and updated according to the input observation data during this process. Then… More >

Mahmoud Ragab^1,2,3,*

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 4143-4155, 2022, DOI:10.32604/cmc.2022.027075

Abstract Rainfall prediction becomes popular in real time environment due to the developments of recent technologies. Accurate and fast rainfall predictive models can be designed by the use of machine learning (ML), statistical models, etc. Besides, feature selection approaches can be derived for eliminating the curse of dimensionality problems. In this aspect, this paper presents a novel chaotic spider money optimization with optimal kernel ridge regression (CSMO-OKRR) model for accurate rainfall prediction. The goal of the CSMO-OKRR technique is to properly predict the rainfall using the weather data. The proposed CSMO-OKRR technique encompasses three major processes namely feature selection, prediction, and… More >

Ganapathy Pattukandan Ganapathy¹, Kathiravan Srinivasan², Debajit Datta², Chuan-Yu Chang^3,4,*, Om Purohit⁵, Vladislav Zaalishvili⁶, Olga Burdzieva⁶

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 6333-6350, 2022, DOI:10.32604/cmc.2022.023254

Abstract A substantial amount of the Indian economy depends solely on agriculture. Rainfall, on the other hand, plays a significant role in agriculture–while an adequate amount of rainfall can be considered as a blessing, if the amount is inordinate or scant, it can ruin the entire hard work of the farmers. In this work, the rainfall dataset of the Vellore region, of Tamil Nadu, India, in the years 2021 and 2022 is forecasted using several machine learning algorithms. Feature engineering has been performed in this work in order to generate new features that remove all sorts of autocorrelation present in the… More >

Mohd Anul Haq^*

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2363-2381, 2022, DOI:10.32604/cmc.2022.023059

Abstract Water received in rainfall is a crucial natural resource for agriculture, the hydrological cycle, and municipal purposes. The changing rainfall pattern is an essential aspect of assessing the impact of climate change on water resources planning and management. Climate change affected the entire world, specifically India’s fragile Himalayan mountain region, which has high significance due to being a climatic indicator. The water coming from Himalayan rivers is essential for 1.4 billion people living downstream. Earlier studies either modeled temperature or rainfall for the Himalayan area; however, the combined influence of both in a long-term analysis was not performed utilizing Deep… More >

Haifeng Huang¹, Zhao Li^2,*, Junhui Luo¹, Zhenchao Chang¹

FDMP-Fluid Dynamics & Materials Processing, Vol.17, No.6, pp. 1165-1178, 2021, DOI:10.32604/fdmp.2021.017256

Abstract Carbonaceous shale is a sedimentary rock containing a large amount of dispersed carbonaceous organic material. It is easy to crack and soften when exposed to water. In the present work, the stability of such a rock and its sensitivity to the formation of infiltrations due to rainfall are analyzed numerically using the GeoStudio software. The slope stability coefficient is calculated and verified using the landslide thrust calculation method. The results show that under the action of heavy rainfall, water infiltrates into the slope layer by layer, and, accordingly, the soil volume water content is different with respect to that typical… More >

Xiaoli Li¹, Shuailing Zhou¹, Zixu An^2,*, Zhenlong Du¹

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3765-3780, 2021, DOI:10.32604/cmc.2021.012422

Abstract Rough set theory has been widely researched for time series prediction problems such as rainfall runoff. Accurate forecasting of rainfall runoff is a long standing but still mostly significant problem for water resource planning and management, reservoir and river regulation. Most research is focused on constructing the better model for improving prediction accuracy. In this paper, a rainfall runoff forecast model based on the variable-precision fuzzy neighborhood rough set (VPFNRS) is constructed to predict Watershed runoff value. Fuzzy neighborhood rough set define the fuzzy decision of a sample by using the concept of fuzzy neighborhood. The fuzzy neighborhood rough set… More >