Water Level Prediction of Lake Cascade Mahakam Using Adaptive Neural Network Backpropagation (ANNBP)

Mislan,; Gaffar, Achmad Fanany Onnilita; Haviluddin, .; Puspitasari, Novianti

doi:10.1088/1755-1315/144/1/012009

Cited by 6 publications

(5 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Lake Taihu, located in the economically developed and densely populated middle and lower reaches of the Yangtze River in China, has a surface area of approximately 2338 km 2 . It is a typical large shallow lake, with an average depth of 1.9 m and a maximum depth of less than 3 m [24]. The Lake Taihu basin is in a subtropical monsoon climate zone, with southeasterly winds prevailing in summer and autumn and northwesterly winds prevailing in winter and spring [24].…”

Section: Study Areamentioning

confidence: 99%

“…It is a typical large shallow lake, with an average depth of 1.9 m and a maximum depth of less than 3 m [24]. The Lake Taihu basin is in a subtropical monsoon climate zone, with southeasterly winds prevailing in summer and autumn and northwesterly winds prevailing in winter and spring [24]. Studies have shown that the wind field has an important influence on the turbidity of Lake Taihu [25].…”

Section: Study Areamentioning

confidence: 99%

See 1 more Smart Citation

Comparison between Machine-Learning-Based Turbidity Models Developed for Different Lake Zones in a Large Shallow Lake

Xu²,

Yan

et al. 2023

Water

View full text Add to dashboard Cite

Machine learning has been used to mine the massive data collected by automatic environmental monitoring systems and predict the changes in the environmental factors in lakes. However, further study is needed to assess the feasibility of the development of a universal machine-learning-based turbidity model for a large shallow lake with considerable spatial heterogeneity in environmental factors. In this study, we collected and examined sediment and water quality data from Lake Taihu, China. Three monitoring stations were established in three lake zones to obtain continuous time series data of the water quality and meteorological variables. We used these data to develop three turbidity models based on long short-term memory (LSTM). The three zones differed in terms of environmental factors related to turbidity: in West Taihu, the Lake Center, and the mouth of Gonghu Bay, the critical shear stress of bed sediments was 0.029, 0.055, and 0.032 N m−2, and the chlorophyll-a concentration was 23.27, 14.62, 30.80 μg L−1, respectively. The LSTM-based turbidity model developed for any zone could predict the turbidity in the other two zones. For the model developed for West Taihu, its performance to predict the turbidity in the local zone (i.e., West Taihu) was inferior to that for the other zones; the reverse applied to the models developed for the Lake Center and Gonghu Bay. This can be attributed to the complex hydrodynamics in West Taihu, which weakens the learning of LSTM from the time series data. This study explores the feasibility of the development of a universal LSTM-based turbidity model for Lake Taihu and promotes the application of machine learning algorithms to large shallow lakes.

show abstract

Section: Study Areamentioning

confidence: 99%

Section: Study Areamentioning

confidence: 99%

Comparison between Machine-Learning-Based Turbidity Models Developed for Different Lake Zones in a Large Shallow Lake

Xu²,

Yan

et al. 2023

Water

View full text Add to dashboard Cite

show abstract

“…Di antaranya adalah metode Naïve Bayes untuk memprediksi pengunduran diri mahasiswa dengan menghitung sekumpulan probabilitas dengan tingkat akurasi sebesar 77,78% yang menunjukkan bahwa metode Naïve Bayes mampu memprediksi dengan baik namun perlu menambah atribut dan menggunakan dataset yang lebih banyak terutama data aktual (Mahanggara & Laksito, 2019). Prediksi peramalan kedatangan turis menggunakan metode Average Based Fuzzy Time Series yang memiliki nilai Mean Absolute Percentage Error (MAPE) sebesar 0,77375% (Widians et al, 2019), metode Adaptive Neural Network Backpropagation (ANNBP) untuk memprediksi tinggi muka air dengan hasil akurasi MSE dan MAPE sebesar 9,7% (Mislan et al, 2018), serta masih banyak lagi penelitian tentang peramalan menggunakan berbagai metode kecerdasan buatan (Alfajriani et al, 2020;Ardianto et al, 2018;Bisht & Kumar, 2016;Gadaleta et al, 2016;Minarni & Aldyanto, 2016;Puspitasari et al, 2019). Namun dari beberapa metode tersebut masih memiliki akurasi yang lebih kecil dibandingkan metode Jaringan Syaraf Tiruan (JST).…”

Section: Pendahuluanunclassified

Peramalan Pelayanan Service Mobil (After-Sale) Menggunakan Backpropagation Neural Network (BPNN)

Puspitasari

Haviluddin

Kustiawan

et al. 2021

JISKa

View full text Add to dashboard Cite

The automotive industry in Indonesia, primarily cars, is getting more and more varied. Along with increasing the number of vehicles, Brand Holder Sole Agents (ATPM) compete to provide after-sale services (mobile service). However, the company has difficulty knowing the rate of growth in the number of mobile services handled, thus causing losses that impact sources of income. Therefore, we need a standard method in determining the forecasting of the number of car services in the following year. This study implements the Backpropagation Neural Network (BPNN) method in forecasting car service services (after-sale) and Mean Square Error (MSE) for the process of testing the accuracy of the forecasting results formed. The data used in this study is car service data (after-sale) for the last five years. The results show that the best architecture for forecasting after-sales services using BPNN is the 5-10-5-1 architectural model with a learning rate of 0.2 and the learning function of trainlm and MSE of 0.00045581. This proves that the BPNN method can predict mobile service (after-sale) services with good forecasting accuracy values.

show abstract

“…Namun demikian, metode statistik masih memiliki kekurangan seperti jika data yang digunakan dalam jumlah yang besar maka prediksi yang dihasilkan kurang akurat. Sehingga beberapa peneliti menerapkan metode kecerdasan buatan untuk meningkatkan hasil akurasi prediksi mengingat kinerja algoritma yang bersifat cerdas karena ada tahapan pembelajaran (Haviluddin & Dengen, 2017;Mislan, Gaffar, Haviluddin, & Puspitasari, 2018). Peneliti (Gunawan et al, 2013) menerapkan metode neural network yaitu joint network dan separated network untuk memprediksi minyak kelapa sawit Indonesia.…”

Section: Pendahuluanunclassified

Prediksi Produksi Minyak Kelapa Sawit Menggunakan Metode Backpropagation Neural Network

Aini

Haviluddin

Budiman

et al. 2019

JSAKTI

View full text Add to dashboard Cite

Artikel ini bertujuan untuk memprediksi produksi minyak kelapa sawit mentah (CPO) di PT. Perkebunan Nusantara (PTPN) XIII, Desa Long Pinang. Kabupaten Paser, Kalimantan Timur dengan menggunakan algoritma cerdas, jaringan saraf tiruan (JST) yang disebut Backpropagation Neural Network (BPNN). Data penelitian berasal dari produksi CPO periode Januari 2015 hingga Januari 2018. Pengukuran akurasi prediksi algoritma BPNN menggunakan metode mean square error (MSE). Berdasarkan hasil percobaan, metode BPNN dengan parameter arsitektur 5-10-11-12-13-1; fungsi pembelajaran adalah trainlm; fungsi aktivasi adalah logsig dan purelin; laju pembelajaran adalah 0.7 mampu menghasilkan tingkat kesalahan prediksi yang baik dengan nilai MSE sebesar 0.0069. Hasil penelitian menunjukkan bahwa model ini dapat digunakan sebagai alternatif metode dalam memprediksi produksi CPO pada tahun 2019.

show abstract

Water Level Prediction of Lake Cascade Mahakam Using Adaptive Neural Network Backpropagation (ANNBP)

Cited by 6 publications

References 4 publications

Comparison between Machine-Learning-Based Turbidity Models Developed for Different Lake Zones in a Large Shallow Lake

Comparison between Machine-Learning-Based Turbidity Models Developed for Different Lake Zones in a Large Shallow Lake

Peramalan Pelayanan Service Mobil (After-Sale) Menggunakan Backpropagation Neural Network (BPNN)

Prediksi Produksi Minyak Kelapa Sawit Menggunakan Metode Backpropagation Neural Network

Contact Info

Product

Resources

About