Wavelet based analysis on rainfall and water table depth forecasting using Neural Networks in Kanyakumari district, Tamil Nadu, India

Raj, A. Stanley; Oliver, D. Hudson; Srinivas, Y.; Viswanath, J.

doi:10.1016/j.gsd.2017.06.009

Cited by 12 publications

(4 citation statements)

References 24 publications

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“…Where 𝑠 is the location of the sample point; 𝑍(𝑠) is the observation value at the location; and ℎ is the distance between the two sample points. To find the semivariogram value, there are several data pairs that are divided into classes using the sturge equation as follows (8):…”

Section: Semivariogrammentioning

confidence: 99%

“…According to previous research, the ANN methodology is suitable for rainfall prediction. We can observe from studies [7,8,9,10] that ANN can produce good predictive performance. In addition, the Ordinary Kriging approach can also be a suitable method for interpolating.…”

Section: Introductionmentioning

confidence: 99%

“…For the M741 model, the parameters obtained are 0.93, 0.99, and 6.02 mm, respectively. In similar studies [8,9,10], the ANN model was used to predict rainfall and got good prediction results. It was found that using the ANN model in one similar study resulted in an accuracy value of 98%.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Rainfall Classification of Java Island with ANN-Feature Expansion and Ordinary Kriging

Maulana

Prasetiyowati²,

Sibaroni³

2022

mib

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Precipitation is one of the most important climatic variables in many aspects of our daily lives. High rainfall intensity can cause floods, landslides, and other natural disasters. Therefore, rainfall prediction is important for predicting natural disasters, assisting farmers in production decisions, and crop harvesting. In this research, a system is built to create a rainfall prediction map using a machine learning approach and spatial interpolation algorithms in Java, Indonesia. In the field of weather prediction, the artificial neural network approach is a popular machine learning method. The artificial neural network (ANN) method is a method that has the advantage of studying connections in the previously unknown hidden layer between input data and output data through training procedures. By using the ANN method, historical weather and climate data can be applied to create a classification model and predict rainfall classes. The classification of data is determined based on the attributes of historical weather and climate data, namely temperature, humidity, air pressure, evaporation, sunlight, and the level of rainfall in the time range per day and month. From the results of the ANN modeling, it was found that the 5C month model with an accuracy value of 89% as the best monthly ANN model, and the 6C day model with an accuracy value of 81% as the best daily ANN model. After going through ANN modeling, there is a spatial interpolation algorithm that is often used to estimate rainfall, namely Ordinary Kriging. The Ordinary Kriging approach is used to reduce the estimated variance and estimate the rainfall value in the case study area. After going through Ordinary Kriging modeling, a rainfall prediction map for the next six months and seven days is made based on the coordinates as a result of the research. The results of this research are rainfall prediction maps for the next six months and the next seven days on Java Island.

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Section: Semivariogrammentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Prediction of Rainfall Classification of Java Island with ANN-Feature Expansion and Ordinary Kriging

Maulana

Prasetiyowati²,

Sibaroni³

2022

mib

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“…Furthermore, they also show that the affected area is more significant for a basin with dams than some freeflowing rivers. Recently, some methods have been developed to monitor the water level as a parameter of water resources, such as the Kriging method of spatio-temporal regression [8]; wavelets; the Artificial Neural Network [9]; the hydrogeological-hydrochemical model [10]; the Modular Finite Difference Model of groundwater flow [11]; a combination of remote sensing, water balance, and the physics-based hydrological model [12]; and empirical and water balance methods [13]. Meanwhile, one of unmet needs in managing water resources is the need for continuous, sustained, and periodic water level measurement data.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Evaluation of Water Resources in Citarum Watershed during Weak La Nina and Weak El Nino

Susandi,

Darmawan,

Sulaiman

et al. 2024

Hydrology

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This study investigates the dynamics of water resources in the Citarum watershed during periods of weak La Niña, normal, and weak El Niño conditions occurring sequentially. The Citarum watershed serves various purposes, being utilized not only by seven (7) districts and two (2) cities in West Java, Indonesia but also as a source of raw water for drinking in the City of Jakarta. Using a time-series analysis of surface water data, data-driven (machine learning) methods, and statistical analysis methods, spatiotemporal predictions of surface water have been made. The surface water time series data (2017–2021), obtained from in situ instruments, are used to assess water resources, predict groundwater recharge, and analyze seasonal patterns. The results indicate that surface water follows a seasonal pattern, particularly during the monsoon season, corresponding to the groundwater recharge pattern. In upstream areas, water resources exhibit an increasing trend during both weak La Nina and weak El Niño, except for Jatiluhur Dam, where a decline is observed in both seasons. Machine learning predictions suggest that water levels and groundwater recharge tend to decrease in both upstream and downstream areas.

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Potential impacts of climate change on groundwater level through hybrid soft-computing methods: a case study—Shabestar Plain, Iran

Jeihouni

Mohammadi

Eslamian

et al. 2019

Environ Monit Assess

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Wavelet based analysis on rainfall and water table depth forecasting using Neural Networks in Kanyakumari district, Tamil Nadu, India

Cited by 12 publications

References 24 publications

Prediction of Rainfall Classification of Java Island with ANN-Feature Expansion and Ordinary Kriging

Prediction of Rainfall Classification of Java Island with ANN-Feature Expansion and Ordinary Kriging

Spatiotemporal Evaluation of Water Resources in Citarum Watershed during Weak La Nina and Weak El Nino

Potential impacts of climate change on groundwater level through hybrid soft-computing methods: a case study—Shabestar Plain, Iran

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