Comparative Analysis of Physiograpic Study for Hydrology of Benowo Region, Surabaya

This study aims to determine the physiographic characteristics according to the slope, land use, and runoff that affect water control in the Benowo area, Surabaya. From the comparison of the results of the runoff in the dry season and the rainy season there was an increase in the average runoff value from 24 % to 26 %. Although there is a difference in the value of the runoff of 2 %, this can affect the increase in water runoff above the surface of the study area. The value of the runoff in the study region will be significantly impacted by changes in land use from agriculture and water to residential or industrial areas. The runoff predicted data’s overall accuracy value for both dry and wet seasons is good, and the Cohen’s kappa value is almost a perfect consistency. The overall accuracy value is 89.7% in the wet season compared to 87.4% in the dry season. During the dry season, the Cohen’s Kappa value is 83.8%, and during the rainy season, it is 86.6%. The result of this research is a preliminary study to study the distribution of rainfall, discharge, and hydrological engineering for development in Benowo area, Surabaya.


Introduction
Surabaya serves as a strategic hub for the planning and growth of the region on the industrial, economic, and other levels.The Benowo area, which borders the Gresik Regency, is one area with a vital location.A study of the area According to the hydrology of the area is crucial for regional planning and development.The Kali Lamong River flows through the Benowo region.Flooding occurs in numerous Gresik areas that are close to Benowo Surabaya during the rainy season as a result of the Kali Lamong River's capability not being able to handle all of the incoming flow.Floods occur when the Kali Lamong River cannot handle the amount of rainfall.According to the research, this study was carried out in order to support with the summary of the hydrological preliminary study in the Benowo region of Surabaya (1,2).
The function of the absorption land may alter, becoming narrower as a result of the potential for growth in population density and development in different regions.Less water may seep into the earth as a result of the absorption land's altered role, increasing the likelihood of extreme weather and flooding (3).Hydrological studies are required to control water in the Benowo district of Surabaya due to the risk of flooding.This report offers a physiographic overview of the studied area using remote sensing technologies as a technical study of hydrology.According to Digital Elevation Model data and Landsat 8 satellite imaging data, the research area's physiography was determined.Review and classification of 1250 (2023) 012015 IOP Publishing doi:10.1088/1755-1315/1250/1/012015 2 Digital Elevation Model data (4).Landsat 8 satellite image processed land use classification (5)(6)(7).The runoff classification of the research region is produced by combining the slope and land use classifications (5,8).Reviewing hydrological research carried out utilizing remote sensing technology also takes into account the dry season and the rainy season.Different runoffs onto the land may be the result of these seasonal differences.Runoff is calculated using both the volume of water that evaporates from the surface and the volume of water that infiltrates the soil through infiltration (9)(10)(11).
This study was conducted to determine the effect of physiography According to the existing land conditions According to the dry season and rainy season.Future water control in the Benowo region of Surabaya and an estimation of the proportion of runoff water to the area of the built-up area in the area's hydrological planning would benefit from analysis of the available data.In order to analyze decisionmaking about the development, management, and monitoring of water in the Benowo region of Surabaya in the future better as a strategic location, this research's continuing development becomes crucial.

Methods
The study region is in East Java's Surabaya province's Benowo Region.According to the location of the Gresik Regency in Benowo, which is to the north.with Sambikerep Regency in the south.The Asemrowo and Tandes sub-districts are close with the Benowo area to the east.The Kali Lamong River runs through the study area.The following Figure 1 is a map of the location of the research area:

Figure 1. Research Area Map
The boundary of the study area is determined through the delineation of the Digital Elevation Model data.Digital Elevation Model data data is processed by process fill to reduce peak.Next, determine the flow direction to determine the direction of flow on the surface.The flow direction is processed for basin identification, so that the basin area is obtained as the boundary of the research area.The identification in this study examines the differences According to seasons, namely the rainy season and the dry season.

Slope Classification
Slope is a unit used to describe how steep a surface is.The slope increases with surface steepness.By computing the surface's tangent, slope is determined.The tangent is computed by dividing the horizontal distance by the vertical change in elevation (8,12).The classification of slope against runoff is shown in Table 1 below:

Runoff Classification
Using information on land use and slope, the coefficient of water flow on the surface of the land was calculated.Equation 1 is used to calculate the runoff in the simple model presented below: LT is the total study area (m 2 ), Lbl is the area (m 2 ) of a particular land class, and C is the runoff.This approach handles the intersection processing, including the math.Information for the categorization of runoffs is provided by the intersection processing results (5,8,15).The following is the runoff classification table shown in

Predictive Accuracy Test (Confusion Matrix and Cohen's Kappa)
Confusion matrix are a widely used measurement when attempting to solve classification issues.Both binary classification and multiclass classification issues can be solved with it (16).The following Table 4 shows the confusion matrix for binary classification: TP and TN display the quantity of correctly categorized positive and negative samples from this Confusion Matrix.In the meantime, FP and FN display the quantity of incorrectly identified positive and negative examples, respectively.The Confusion Matrix is a useful tool for model evaluation since it is simple to calculate, adaptable to situations with several classes and labels, straightforward to utilize in evaluations, and simple to comprehend by people.There are a number of equations in this system that can be used to calculate the value of the model's goodness, however in this study, user accuracy and overall accuracy were used to test the prediction's quality (18).
Cohen's Kappa is a metric that expresses the degree of agreement between two raters' measurements or the degree of agreement between two measuring techniques.Predictions and observations are what the rater here means when evaluating the model prediction.Using the Confusion Matrix as shown in the subsequent equation shown in Equation 2 below: The minimum value of Cohen's Kappa is -1 (perfectly wrong prediction) and the maximum value is 1 (perfectly right prediction).If the value of K = 0, then the prediction is declared very random.There is no general standard for assessing the significance of the Cohen's Kappa value (19), but many studies have used the Cohen's Kappa value significance classification as shown in Table 5 below:

Results and Discussion
According on the outcomes of intersect processing, data were used to categorize runoffs.The junction of numerous classes, including slope, land use, runoff, and physiographic impacts According to rainy season and dry season, was the main emphasis of this study.The following are the study's findings and analysis:

Slope Classification
Specifically for the slope classification, one Digital Elevation Model raster data is used for the rainy season and the dry season.The classification of slope According to the runoff is displayed in Table 6 as follows:  (20,21).

Figure 2. Slope Classification Map
The quantity of water that has gone off the surface is shown by the runoff in Figure 2.More water will run off a steeper slope, increasing runoff.This is seen on a slope in the orange area that is steep and has a runoff of 1.The runoff number shows that none of the water in the zone seeps into the surface and that all of it flows off the surface.In the studied region, 0.2 is the majority of the runoff.based on the slope.The runoff shows that only 20% of the water evaporates from the soil's surface, while the remaining 80% is intaken into the ground.(1).The slope classification in Figure 2 is used for all seasons.

Land Use Classification
The data employed in processing land use classification are Landsat 8 image data and river data (7,14).The following is the classification of the land use according to the dry season and the rainy season:

Dry Season
The results of processing data on land use classification according to dry season are shown below in Table 7: The research area is primarily identified on the land use categorization map as a dike.6.08 km 2 of the research area, or 32.9%, is taken up by the pond.A blue sign that represents the dike runoff has a value of 0.5.The settlement covers a total area of 5.52 km 2 , or 28.68% of the total area, which is made up of land.This has a runoff of 0.9 and can be detected in the red zone.2.39 km 2 or 19.48% of the land area is made up of industrial land.This has a runoff of 0.7 and can be detected in the light orange zone.6.76 km 2 of the area is dedicated to agriculture, or 18.35% of the total area.This may be seen in the area of dark green and has a runoff of 0.2 (22)(23)(24).

Figure 3. Land Use Classification Map of Dry Season
The amount of water that has flowed to the surface is shown in Figure 3 as runoff.Because more water flows off the ground surface as the density of land utilized for construction grows, the runoff rises.Ponds are the main land uses in the study region, and the runoff value is 0.5.This number Digital Elevation Modelonstrates that half of the water evaporates off the soil's surface while the other half is intaken into the soil (1,5).

Rainy Season
The results of processing data on land use classification according to rainy season are shown below in Table 8: The research area is primarily identified on the land use categorization map as a dike.9.32 km 2 of the research area, or 44.92%, is made up by the pond.A blue sign that represents the dike runoff has a value of 0.5.The settlement's total area is 5.52 km 2 , and 26.61% of that area is made up of land.This has a runoff of 0.9 and can be detected in the red zone.2.39 km 2 or 11.53% of all land area is made up of industrial land.This has a runoff of 0.7 and can be detected in the light orange zone.3.52 km 2 of the area is made up of agricultural land, or 16.95% of the total area.This may be seen in the area of dark green and has a runoff of 0.2 (22)(23)(24).

Figure 4. Land Use Classification Map of Rainy Season
The amount of water that has flowed to the surface is shown in Figure 4 as runoff.Because more water flows off the ground surface as the density of land utilized for construction grows, the runoff rises.Ponds are the main land uses in the study region, and the runoff value is 0.5.This number Digital Elevation Modelonstrates that half of the water evaporates off the soil's surface while the other half is intaken into the soil (1,5).
There are changes in the land use categorization outcomes between the dry and wet seasons, particularly when it comes to the classification of water bodies and agricultural land.During the dry season, there are 6.08 km 2 of water bodies and 6.76 km 2 of agricultural land.While the area of water bodies on land is 9.32 km 2 and that of agricultural land is 3.52 km 2 during the wet season.Agriculture land has reduced while the area of water bodies has expanded.During the rainy season, it is evident that agricultural land has been transformed into water-based bodies (1,5).

Runoff Classification
From the results of intersect processing, runoff classification data are gathered.The following runoff classification is obtained from intersect processing:

Dry Season
The results of processing data on runoff classification According to dry season are shown below in Table 9: As seen in Figure 5, The green zone, which is classified as having little runoff and has a range of values 0-0.25, a 12.12 km 2 area, and a zone area proportion of 58.4% of the entire area.The yellow zone falls under the category of the medium runoff and has a range of values 0.25 to 0.5, a total size of 7.43 km 2 , and a zone area percentage of 35.83%.The orange zone has a range of values 0.5 to 0.75, an area of 1.17   Figure 6 shows the green zone, which is classified as having minimal runoff and has a range of values 0-0.25, a size of 12.08 km 2 , and a percentage of 58.23% of the entire land in the zone.The yellow zone falls under the category of the medium runoff and has a range of values 0.25 to 0.5, a total area of 7.47 km 2 , and a zone area percentage of 36.01%.The orange zone has a range of values 0.5 to 0.75, an area of 1.17 km 2 , and a proportion of 5.66% of the entire area that falls within the high runoff category.The red zone falls under the category of the extremely high runoff and has a range of values 0.75-1, a total size of 0.02 km 2 , and a zone area percentage of 0.11%.The research area's annual runoff is 0.26 (13).Low is the classification for the runoff value.This figure correlates to 26% runoff on the surface of the land and 74% soil infiltration of water (12,25).

Predictive Accuracy Test (Confusion Matrix and Cohen's Kappa)
The data used to test the accuracy of predictions is according to data on the distribution of rock types in the study area.Data on the distribution of rock types is approached with infiltration values and runoffs.The following details Table 11 of the accuracy test data used: The probability that a value predicted to belong to a particular class actually belongs to that class is known as user accuracy.The probability is determined by dividing the total number of values anticipated to belong to a class by the percentage of values that were correctly predicted.According to the user accuracy value for each code runoff, the rainy season data has a higher user accuracy value than the dry season data (17,28).The following details the user accuracy results shown in The probability that a test will correctly classify a particular individual is known as overall accuracy; it is calculated by dividing the sum of true positives and true negatives by the total number of people examined.The overall accuracy value in the dry season is 87.4%, while the overall accuracy value in the rainy season is 89.7%.The overall accuracy value for the rainy season is higher than the overall accuracy for the dry season (29).
The Cohen's Kappa is used to assess the degree of agreement between two raters or judges who each assign items to categories that are mutually exclusive.According to the results obtained, the Cohen's Kappa value in the dry season is 83.8%, while in the rainy season it is 86.6 %.The two prediction data for each season show an almost perfect consistency value (30).

Discussion
Every year, there are advancements to how land is utilized, such as the enlargement of industrial land on previously agricultural ground.This may cause the runoff value to increase.A higher coefficient value denotes greater surface runoff from the same amount of precipitation.In order to reduce floods caused by rising runoff, it may be suggested in the future that a reservoir be needed for flood control during the rainy season and the stored water be used during the dry season (31).The runoff for the current situation is 24% during the dry season and 26% during the wet season.This proves that there is surface water flow that the Benowo region may use for future development.Future growth will considerably enhance its value, which will involve regulating the area's water supply (9)(10)(11)32).The overall accuracy value of the runoff prediction data for the dry and rainy seasons is good, and the Cohen's kappa value is almost perfect consistency.In the dry season, the total accuracy value is 87.4%, whereas in the wet season, it is 89.7%.The Cohen's Kappa value is 83.8% during the dry season and 86.6% during the wet season.The two predicted runoff data are a good representation of the original rock distribution data (including rock type, infiltration, and runoff methodologies) according to the results of overall accuracy and Cohen's Kappa (30).
The findings of this study may be applied to future hydrological technical research in order to develop, enhance, use, and regulate water flow in the Surabaya neighborhood of Benowo.One of the follow-ups will be a hydrological research, which will examine the impact of recent land use changes in this area and its environs, including an examination of rainfall distribution and flow.

Conclusion
A physiographic analysis of the Benowo region throughout the dry and wet seasons has been done.Data using Landsat 8 and Digital Elevation Model data were utilized.We have runoff classification maps, land use classification maps, and slope classification maps.According to the current circumstances, the runoff during the dry season is 0.24 and during the wet season is 0.26.The findings showed that 24% of the water maintained above the surface during the dry season and 76% of the water

User accuracy
Dry Season Rainy Season penetrated.In contrast, during the rainy season, 72% of penetrated water and 26% of the water held above the surface are retained.These findings lead to an upgrade in the runoff's categorization from low class to medium class.The overall accuracy value of the runoff prediction data for the dry and rainy seasons is good, and the Cohen's kappa value is almost perfect consistency.In the dry season, the total accuracy value is 87.4%, whereas in the wet season, it is 89.7%.The Cohen's Kappa value is 83.8% during the dry season and 86.6% during the wet season.The two predicted runoff data are a good representation of the original rock distribution data (including rock type, infiltration, and runoff methodologies) according to the results of overall accuracy and Cohen's Kappa.
So that this research can be used as a preliminary study for hydrological engineering and development, improvement, utilization, and control of water flow in the Benowo area of Surabaya.As for the recommendations obtained from this research, further hydrological studies are needed in planning the Benowo Surabaya area for hydrological management of the surrounding area.Further research is needed due to the increasing shift in land use from agriculture to settlements and industry.
km2 , and a proportion of 5.66% of the entire area that falls within the high runoff category.The red IOP Publishing doi:10.1088/1755-1315/1250/1/0120158 zone falls under the category of the extremely high runoff and has a range of values 0.75-1, a total size of 0.02 km2, and a zone area percentage of 0.11%.The research area's annual runoff is 0.24 (13).Low is a category for the runoff value.This figure equates to 25% runoff on the surface and 75% water intaken by the soil(1,5).

Figure 5 .
Figure 5. Runoff Map of Dry Season 3.3.2.Rainy Season The results of processing data on runoff classification According to rainy season are shown below inTable 10:

9 Figure 6 .
Figure 6.Runoff Map of Rainy SeasonThe average score runoff value has increased from 0.24 to 0.26 when The Results of Predicted Runoff during the dry and wet seasons are compared.Even if there is a discrepancy in the runoff value of 0.02, this may have an impact on the rise in runoff water over the research area's surface(26).3.4Predictive Accuracy Test (Confusion Matrix and Cohen's Kappa)The data used to test the accuracy of predictions is according to data on the distribution of rock types in the study area.Data on the distribution of rock types is approached with infiltration values and runoffs.The following details Table11of the accuracy test data used:

Figure 7 : 10 Figure 7 .
Figure 7.The Result of User Accuracy

Table 6 .
(5) Results of Slope Classification According to Runoff(5)The slope categorization map's findings are shown in the dark green region, which has a slope of 0-8%, covers 17.87 km 2 , and makes up 86.14% of the overall area.With a runoff of 0.2, flat land is classified.A light green zone with a 0.4 runoff indicates a class of sloping terrain with a slope of 8-15%, an area of 2.26 km 2 , and a land area proportion of 10.91% of the total area.With a slope of 15-25%, a 0.52 km 2 area, and a percentage of 2.49% for rather steep slopes, the slope class is rather steep.This has a runoff of 0.6 and is in the yellow area.Class of steep slopes with an area of 0.09 km 2 , a slope of 25-45%, and a steep slope percentage of 0.45%.In the orange zone, this is visible.The steep slopes category has a runoff of 0.80

Table 7 .
(5) Results of Classifying Land Use Using Runoff in Dry Season(5)

Table 8 .
(5) Results of Classifying Land Use Using Runoff in Rainy Season(5)